Creating a C++ extension for Python

Modules written in C++ (or C) are commonly used to extend the capabilities of a Python interpreter as well as to enable access to low-level operating system capabilities. There are three primary types of modules:

  • Accelerator modules: because Python is an interpreted language, certain pieces of code can be written in C++ for higher performance.
  • Wrapper modules: expose existing C/C++ interfaces to Python code or expose a more "Pythonic" API that makes use of Python language features to make the API easier to use.
  • Low-level system access modules: created to access lower-level features of the CPython runtime, the operating system, or the underlying hardware.

This topic walks through building a C++ extension module for CPython that computes a hyperbolic tangent and calls it from Python code. To demonstrate the performance difference, you'll create and test the routine first in Python.

The approach taken here is that for standard CPython extensions as described in the Python documentation. A comparison between this and other means is described under alternative approaches at the end of this topic.


This walkthrough is written for Visual Studio 2017 with both the Desktop Development with C++ and Python Development workloads with their default options (such as Python 3.6 as the default interpreter). In the Python Development workload, also check the box on the right for Python native development tools, which will set up most of the options described in this topic. (This option will also include the C++ workload automatically.)

Selecting the Python native development tools option

For more details, see Installing Python Support for Visual Studio, including using other versions of Visual Studio. If you install Python separately, be sure to select Download debugging symbols and Download debug binaries under Advanced Options in the installer. This makes sure that you have the necessary debug libraries available if you choose to do a debug build.


Python is also available through the Data science and analytical applications workload, which includes Anaconda 3 64-bit (with the latest version of CPython) and the Python native development tools option by default.

Create the Python application

  1. Create a new Python project in Visual Studio by selecting the File > New > Project menu command, then searching for "Python", selecting the Python Application template, giving it a suitable name and location, and selecting OK.

  2. In the project's .py file, paste the following code that benchmarks the computation of a hyperbolic tangent (implemented without using the math library for easier comparison). Feel free to enter the code manually to experience some of the Python editing features.

    from itertools import islice
    from random import random
    from time import perf_counter
    COUNT = 100000
    DATA = list(islice(iter(lambda: (random() - 0.5) * 3.0, None), COUNT))
    e = 2.7182818284590452353602874713527
    def sinh(x):
        return (1 - (e ** (-2 * x))) / (2 * (e ** -x))
    def cosh(x):
        return (1 + (e ** (-2 * x))) / (2 * (e ** -x))
    def tanh(x):
        tanh_x = sinh(x) / cosh(x)
        return tanh_x
    def sequence_tanh(data):
        '''Applies the hyperbolic tanger function to map all values in
        the sequence to a value between -1.0 and 1.0.
        result = []
        for x in data:
        return result
    def test(fn, name):
        start = perf_counter()
        result = fn(DATA)
        duration = perf_counter() - start
        print('{} took {:.3f} seconds\n\n'.format(name, duration))
        for d in result:
            assert -1 <= d <=1, " incorrect values"
    if __name__ == "__main__":
        test(sequence_tanh, 'sequence_tanh')
        test(lambda d: [tanh(x) for x in d], '[tanh(x) for x in d]')
  3. Run the program using Debug > Start without Debugging (Ctrl+F5) to see the results. You'll see that each benchmark takes several seconds to complete.

Create the core C++ project

  1. Right-click the solution in Solution Explorer and select Add > New Project.... A Visual Studio solution can contain both Python and C++ projects together.

  2. Search on "C++", select Empty project, specify a name (such as TanhBenchmark), and select OK. Note: if you've installed the Python native development tools with Visual Studio 2017, you can start with the Python Extension Module template, which has much of what's described here already in place. For this walkthrough, though, starting with an empty project will demonstrate building the extension module step by step.

  3. Create a C++ file in the new project by right-clicking the Source Files node, then select Add > New Item...", select C++ File, give it a name (like module.cpp), and select OK. This step is necessary to turn on the C++ property pages in the next steps.

  4. Right-click the new project and select Properties, then at the top of the Property Pages dialog that appears, set Configuration to All Configurations.

  5. Set the specific properties as described below, then select Apply (you may need to click outside of an editable field for the Apply button to become enabled).

    Tab Property Value
    General General > Target Name Set this to exactly match the name of the module as Python sees it.
    General > Target Extension .pyd
    Project Defaults > Configuration Type Dynamic Library (.dll)
    C/C++ > General Additional Include Directories Add the Python include folder as appropriate for your installation, for example, c:\Python36\include
    C/C++ > Code Generation Runtime Library Multi-threaded DLL (/MD) (see Warning below)
    C/C++ > Preprocessor Preprocessor Definitions Add Py_LIMITED_API; to the beginning of the string. This restricts some of the functions you can call from Python and makes the code more portable between different versions of Python.
    Linker > General Additional Library Directories Add the Python lib folder containing .lib files as appropriate for your installation, for example, c:\Python36\libs. (Be sure to point to the libs folder that contains .lib files, and not the Lib folder that contains .py files.)

    If you don't see the C/C++ tab, it's because the project doesn't contain any files that it identifies as C/C++ source files. This can happen if you create a source file without a .c or .cpp extension. For example, if you accidentally entered module.coo instead of module.cpp in the new item dialog earlier, then Visual Studio creates the file but doesn't set the file type to "C/C+ Code," which is what activates the C/C++ properties tab. This remains the case even if you rename the file with .cpp. To correct this, right-click the file in Solution Explorer, select Properties, then set File Type to C/C++ Code.


    Don't set the C/C++ > Code Generation > Runtime Library option to "Multi-threaded Debug DLL (/MDd)" even for a Debug configuration. You need to select the "Multi-threaded DLL (/MD)" runtime because that's what the non-debug Python binaries are built with. If you happen to set the /MDd option, you'll see error C1189: Py_LIMITED_API is incompatible with Py_DEBUG, Py_TRACE_REFS, and Py_REF_DEBUG when building a Debug configuration of your DLL. Furthermore, if you remove Py_LIMITED_API to avoid the build error, then Python will crash when attempting to import the module. (The crash will happen within the DLL's call to PyModule_Create as described later, with the output message of Fatal Python error: PyThreadState_Get: no current thread.)

    Note that the /MDd option is what's used to build the Python debug binaries (such as python_d.exe), but selecting it for an extension DLL will still cause the build error with Py_LIMITED_API.

  6. Right click the C++ project and select Build to test your configurations (both Debug and Release). Note that you'll find the .pyd files in the solution folder under Debug and Release, not the C++ project folder itself.

  7. Add the following code to the C++ project's main .cpp file:

    #include <Windows.h>
    #include <cmath>    
    const double e = 2.7182818284590452353602874713527;
    double sinh_impl(double x) {
        return (1 - pow(e, (-2 * x))) / (2 * pow(e, -x));
    double cosh_impl(double x) {
        return (1 + pow(e, (-2 * x))) / (2 * pow(e, -x));
    double tanh(x) {
        return sinh(x) / cosh(x);
  8. Build the C++ project again to confirm that your code is correct.

Convert the C++ project to an extension for Python

To make the C++ DLL into an extension for Python, you need to modify the exported method to interact with Python types. Then you need to add a function that exports the module, along with definitions of the module's methods. For background on what's shown here, refer to the Python/C API Reference Manual and especially Module Objects on (Remember to select your version of Python from the drop-down control on the upper right.)

  1. In the C++ file, include Python.h at the top:

    include <Python.h>
  2. Modify the tanh method to accept and return Python types:

    PyObject* tanh(PyObject *, PyObject* o) {
        double x = PyFloat_AsDouble(o);
        double tanh_x = sinh_impl(x) / cosh_impl(x);
        return PyFloat_FromDouble(tanh_x);
  3. Add a structure that defines how the C++ tanh function is presented to Python:

    static PyMethodDef superfastcode_methods[] = {
        // The first property is the name exposed to python, the second is the C++ function name        
        { "fast_tanh", (PyCFunction)tanh, METH_O, nullptr },
        // Terminate the array with an object containing nulls.
        { nullptr, nullptr, 0, nullptr }
  4. Add a structure that defines the module as Python will see it:

    static PyModuleDef superfastcode_module = {
        "superfastcode",                        // Module name
        "Provides some functions, but faster",  // Module description
        superfastcode_methods                   // Structure that defines the methods
  5. Add a method that Python calls when it loads the module, which must be must be named PyInit_<module-name> where <module_name> exactly matches the C++ Project's General > Target Name property (that is, it matches the filename of the .pyd built by the project).

    PyMODINIT_FUNC PyInit_superfastcode() {    
        return PyModule_Create(&superfastcode_module);
  6. Build the DLL again to verify your code.

Test the code and compare the results

Now that you have the DLL structured as a Python extension, you can refer to it from the Python project, import the module, and use its methods.

There are two ways to make the DLL available to Python. First, you can add a reference from the Python project to the C++ project, provided that they're in the same Visual Studio solution:

  1. In Solution Explorer, right-click the Python project and select References. In the dialog, select the Projects tab, select the superfastcode project, and then OK.

Second, you can install the module in the global Python environment, making it available to other Python projects as well. Note that doing so will typically require that you refresh the IntelliSense completion database for that environment, as will removing the module from the environment.

  1. If you're using Visual Studio 2017, run the Visual Studio installer, select Modify, select Individual Components > Compilers, build tools, and runtimes > Visual C++ 2015.3 v140 toolset. This is because Python (for Windows) is itself build with Visual Studio 2015 (version 14.0) and expects those tools be available when building an extension through the method described here.

  2. Create a file named in your C++ project by right clicking the project, selecting Add > New Items...*, searching for "Python" and selecting **Python file, naming it, and selecting OK. When the file appears in the editor, paste the following code into it:

    from distutils.core import setup, Extension, DEBUG
    sfc_module = Extension('superfastcode', sources = ['module.cpp'])
    setup(name = 'superfastcode', version = '1.0',
        description = 'Python Package with superfastcode C++ Extension',
        ext_modules = [sfc_module]

    See Building C and C++ Extentions ( for documentation on this script.

  3. The code instructs Python to build the extension (using the Visual Studio 2015 C++ toolset), which happens from the command line. Open an elevated command prompt, navigate to the folder containing the C++ project (and, and enter the following command:

    pip install .

Now you can call the tanh code the module and compare its performance to the Python implementation:

  1. Add the following lines in to call the fast_tanh method exported from the DLL, and add it to the benchmark output. If you type the from s statement manually, you'll see superfastcode come up in the completion list, and after typing import you'll see the fast_tanh method.

    from superfastcode import fast_tanh    
    test(lambda d: [fast_tanh(x) for x in d], '[fast_tanh(x) for x in d]')
  2. Run the Python program and you'll see that the C++ routine runs around 15 to 20 times faster than the Python implementation.

Debug the C++ code

Python support in Visual Studio includes the ability to debug Python and C++ code together. To experience this, do the following:

  1. Right-click the Python project in Solution Explorer, select Properties, select the Debug tab, and then select the Debug > Enable native code debugging option.


    When you enable native code debugging, the Python output window may disappear immediately when the program has completed without giving you the usual "Press any key to continue..." pause. To force a pause, add the -i option to the Run > Interpreter Arguments field on the Debug tab when you enable native code debugging. This will put the Python interpreter into interactive mode after the code finishes, at which point it waits for you to press Ctrl+Z, Enter to exit. (Alternately, if you don't mind modifying your Python code, you can add import os and os.system("pause") statements at the end of your program. This duplicates the original pause prompt.)

  2. In your C++ code, set a breakpoint on the first line within the tanh method, then start the debugger. You'll see the debugger stop when that code is called:

    Stopping at a breakpoint in C++ code

  3. At this point you can step through the C++ code, examine variables, and so on, as detailed in Debugging C++ and Python Together.

Alternative approaches

There are other means to create Python extensions as described in the table below. The first entry for CPython is what's been discussed this topic already.

Approach Vintage Representative User(s) Pro(s) Con(s)
C/C++ extension modules for CPython 1991 Standard Library Extensive documentation and tutorials. Total control. Compilation, portability, reference management. High C knowledge.
SWIG 1996 crfsuite Generate bindings for many languages at once. Excessive overhead if Python is the only target.
ctypes 2003 oscrypto No compilation, wide availability. Accessing and mutating C structures cumbersome and error prone.
Cython 2007 gevent, kivy Python-like. Highly mature. High performance. Compilation, new syntax and toolchain.
cffi 2013 cryptography, pypy Ease of integration, PyPy compatibility. New, less mature.