Wrapping a C library in Python: C, Cython or ctypes?
Question:
I want to call a C library from a Python application. I don’t want to wrap the whole API, only the functions and datatypes that are relevant to my case. As I see it, I have three choices:
- Create an actual extension module in C. Probably overkill, and I’d also like to avoid the overhead of learning extension writing.
- Use Cython to expose the relevant parts from the C library to Python.
- Do the whole thing in Python, using
ctypes to communicate with the external library.
I’m not sure whether 2) or 3) is the better choice. The advantage of 3) is that ctypes is part of the standard library, and the resulting code would be pure Python – although I’m not sure how big that advantage actually is.
Are there more advantages / disadvantages with either choice? Which approach do you recommend?
Edit: Thanks for all your answers, they provide a good resource for anyone looking to do something similar. The decision, of course, is still to be made for the single case—there’s no one “This is the right thing” sort of answer. For my own case, I’ll probably go with ctypes, but I’m also looking forward to trying out Cython in some other project.
With there being no single true answer, accepting one is somewhat arbitrary; I chose FogleBird’s answer as it provides some good insight into ctypes and it currently also is the highest-voted answer. However, I suggest to read all the answers to get a good overview.
Thanks again.
Answers:
I’ll throw another one out there: SWIG
It’s easy to learn, does a lot of things right, and supports many more languages so the time spent learning it can be pretty useful.
If you use SWIG, you are creating a new python extension module, but with SWIG doing most of the heavy lifting for you.
Cython is a pretty cool tool in itself, well worth learning, and is surprisingly close to the Python syntax. If you do any scientific computing with Numpy, then Cython is the way to go because it integrates with Numpy for fast matrix operations.
Cython is a superset of Python language. You can throw any valid Python file at it, and it will spit out a valid C program. In this case, Cython will just map the Python calls to the underlying CPython API. This results in perhaps a 50% speedup because your code is no longer interpreted.
To get some optimizations, you have to start telling Cython additional facts about your code, such as type declarations. If you tell it enough, it can boil the code down to pure C. That is, a for loop in Python becomes a for loop in C. Here you will see massive speed gains. You can also link to external C programs here.
Using Cython code is also incredibly easy. I thought the manual makes it sound difficult. You literally just do:
$ cython mymodule.pyx
$ gcc [some arguments here] mymodule.c -o mymodule.so
and then you can import mymodule in your Python code and forget entirely that it compiles down to C.
In any case, because Cython is so easy to setup and start using, I suggest trying it to see if it suits your needs. It won’t be a waste if it turns out not to be the tool you’re looking for.
Personally, I’d write an extension module in C. Don’t be intimidated by Python C extensions — they’re not hard at all to write. The documentation is very clear and helpful. When I first wrote a C extension in Python, I think it took me about an hour to figure out how to write one — not much time at all.
ctypes is your best bet for getting it done quickly, and it’s a pleasure to work with as you’re still writing Python!
I recently wrapped an FTDI driver for communicating with a USB chip using ctypes and it was great. I had it all done and working in less than one work day. (I only implemented the functions we needed, about 15 functions).
We were previously using a third-party module, PyUSB, for the same purpose. PyUSB is an actual C/Python extension module. But PyUSB wasn’t releasing the GIL when doing blocking reads/writes, which was causing problems for us. So I wrote our own module using ctypes, which does release the GIL when calling the native functions.
One thing to note is that ctypes won’t know about #define constants and stuff in the library you’re using, only the functions, so you’ll have to redefine those constants in your own code.
Here’s an example of how the code ended up looking (lots snipped out, just trying to show you the gist of it):
from ctypes import *
d2xx = WinDLL('ftd2xx')
OK = 0
INVALID_HANDLE = 1
DEVICE_NOT_FOUND = 2
DEVICE_NOT_OPENED = 3
...
def openEx(serial):
serial = create_string_buffer(serial)
handle = c_int()
if d2xx.FT_OpenEx(serial, OPEN_BY_SERIAL_NUMBER, byref(handle)) == OK:
return Handle(handle.value)
raise D2XXException
class Handle(object):
def __init__(self, handle):
self.handle = handle
...
def read(self, bytes):
buffer = create_string_buffer(bytes)
count = c_int()
if d2xx.FT_Read(self.handle, buffer, bytes, byref(count)) == OK:
return buffer.raw[:count.value]
raise D2XXException
def write(self, data):
buffer = create_string_buffer(data)
count = c_int()
bytes = len(data)
if d2xx.FT_Write(self.handle, buffer, bytes, byref(count)) == OK:
return count.value
raise D2XXException
Someone did some benchmarks on the various options.
I might be more hesitant if I had to wrap a C++ library with lots of classes/templates/etc. But ctypes works well with structs and can even callback into Python.
If you have already a library with a defined API, I think ctypes is the best option, as you only have to do a little initialization and then more or less call the library the way you’re used to.
I think Cython or creating an extension module in C (which is not very difficult) are more useful when you need new code, e.g. calling that library and do some complex, time-consuming tasks, and then passing the result to Python.
Another approach, for simple programs, is directly do a different process (compiled externally), outputting the result to standard output and call it with subprocess module. Sometimes it’s the easiest approach.
For example, if you make a console C program that works more or less that way
$miCcode 10
Result: 12345678
You could call it from Python
>>> import subprocess
>>> p = subprocess.Popen(['miCcode', '10'], shell=True, stdout=subprocess.PIPE)
>>> std_out, std_err = p.communicate()
>>> print std_out
Result: 12345678
With a little string formating, you can take the result in any way you want. You can also capture the standard error output, so it’s quite flexible.
ctypes is great when you’ve already got a compiled library blob to deal with (such as OS libraries). The calling overhead is severe, however, so if you’ll be making a lot of calls into the library, and you’re going to be writing the C code anyway (or at least compiling it), I’d say to go for cython. It’s not much more work, and it’ll be much faster and more pythonic to use the resulting pyd file.
I personally tend to use cython for quick speedups of python code (loops and integer comparisons are two areas where cython particularly shines), and when there is some more involved code/wrapping of other libraries involved, I’ll turn to Boost.Python. Boost.Python can be finicky to set up, but once you’ve got it working, it makes wrapping C/C++ code straightforward.
cython is also great at wrapping numpy (which I learned from the SciPy 2009 proceedings), but I haven’t used numpy, so I can’t comment on that.
Warning: a Cython core developer’s opinion ahead.
I almost always recommend Cython over ctypes. The reason is that it has a much smoother upgrade path. If you use ctypes, many things will be simple at first, and it’s certainly cool to write your FFI code in plain Python, without compilation, build dependencies and all that. However, at some point, you will almost certainly find that you have to call into your C library a lot, either in a loop or in a longer series of interdependent calls, and you would like to speed that up. That’s the point where you’ll notice that you can’t do that with ctypes. Or, when you need callback functions and you find that your Python callback code becomes a bottleneck, you’d like to speed it up and/or move it down into C as well. Again, you cannot do that with ctypes. So you have to switch languages at that point and start rewriting parts of your code, potentially reverse engineering your Python/ctypes code into plain C, thus spoiling the whole benefit of writing your code in plain Python in the first place.
With Cython, OTOH, you’re completely free to make the wrapping and calling code as thin or thick as you want. You can start with simple calls into your C code from regular Python code, and Cython will translate them into native C calls, without any additional calling overhead, and with an extremely low conversion overhead for Python parameters. When you notice that you need even more performance at some point where you are making too many expensive calls into your C library, you can start annotating your surrounding Python code with static types and let Cython optimise it straight down into C for you. Or, you can start rewriting parts of your C code in Cython in order to avoid calls and to specialise and tighten your loops algorithmically. And if you need a fast callback, just write a function with the appropriate signature and pass it into the C callback registry directly. Again, no overhead, and it gives you plain C calling performance. And in the much less likely case that you really cannot get your code fast enough in Cython, you can still consider rewriting the truly critical parts of it in C (or C++ or Fortran) and call it from your Cython code naturally and natively. But then, this really becomes the last resort instead of the only option.
So, ctypes is nice to do simple things and to quickly get something running. However, as soon as things start to grow, you’ll most likely come to the point where you notice that you’d better used Cython right from the start.
There’s also one possibility to use GObject Introspection for libraries that are using GLib.
For calling a C library from a Python application there is also cffi which is a new alternative for ctypes. It brings a fresh look for FFI:
- it handles the problem in a fascinating, clean way (as opposed to ctypes)
- it doesn’t require to write non Python code (as in SWIG, Cython, …)
There is one issue which made me use ctypes and not cython and which is not mentioned in other answers.
Using ctypes the result does not depend on compiler you are using at all. You may write a library using more or less any language which may be compiled to native shared library. It does not matter much, which system, which language and which compiler. Cython, however, is limited by the infrastructure. E.g, if you want to use intel compiler on windows, it is much more tricky to make cython work: you should “explain” compiler to cython, recompile something with this exact compiler, etc. Which significantly limits portability.
If you are targeting Windows and choose to wrap some proprietary C++ libraries, then you may soon discover that different versions of msvcrt***.dll (Visual C++ Runtime) are slightly incompatible.
This means that you may not be able to use Cython since resulting wrapper.pyd is linked against msvcr90.dll (Python 2.7) or msvcr100.dll (Python 3.x). If the library that you are wrapping is linked against different version of runtime, then you’re out of luck.
Then to make things work you’ll need to create C wrappers for C++ libraries, link that wrapper dll against the same version of msvcrt***.dll as your C++ library. And then use ctypes to load your hand-rolled wrapper dll dynamically at the runtime.
So there are lots of small details, which are described in great detail in following article:
“Beautiful Native Libraries (in Python)“: http://lucumr.pocoo.org/2013/8/18/beautiful-native-libraries/
I know this is an old question but this thing comes up on google when you search stuff like ctypes vs cython, and most of the answers here are written by those who are proficient already in cython or c which might not reflect the actual time you needed to invest to learn those to implement your solution. I am a complete beginner in both. I have never touched cython before, and have very little experience on c/c++.
For the last two days, I was looking for a way to delegate a performance heavy part of my code to something more low level than python. I implemented my code both in ctypes and Cython, which consisted basically of two simple functions.
I had a huge string list that needed to processed. Notice list and string.
Both types do not correspond perfectly to types in c, because python strings are by default unicode and c strings are not. Lists in python are simply NOT arrays of c.
Here is my verdict. Use cython. It integrates more fluently to python, and easier to work with in general. When something goes wrong ctypes just throws you segfault, at least cython will give you compile warnings with a stack trace whenever it is possible, and you can return a valid python object easily with cython.
Here is a detailed account on how much time I needed to invest in both them to implement the same function. I did very little C/C++ programming by the way:
-
Ctypes:
- About 2h on researching how to transform my list of unicode strings to a c compatible type.
- About an hour on how to return a string properly from a c function. Here I actually provided my own solution to SO once I have written the functions.
- About half an hour to write the code in c, compile it to a dynamic library.
- 10 minutes to write a test code in python to check if
c code works.
- About an hour of doing some tests and rearranging the
c code.
- Then I plugged the
c code into actual code base, and saw that ctypes does not play well with multiprocessing module as its handler is not pickable by default.
- About 20 minutes I rearranged my code to not use
multiprocessing module, and retried.
- Then second function in my
c code generated segfaults in my code base although it passed my testing code. Well, this is probably my fault for not checking well with edge cases, I was looking for a quick solution.
- For about 40 minutes I tried to determine possible causes of these segfaults.
- I split my functions into two libraries and tried again. Still had segfaults for my second function.
- I decided to let go of the second function and use only the first function of
c code and at the second or third iteration of the python loop that uses it, I had a UnicodeError about not decoding a byte at the some position though I encoded and decoded everthing explicitely.
At this point, I decided to search for an alternative and decided to look into cython:
- Cython
- 10 min of reading cython hello world.
- 15 min of checking SO on how to use cython with
setuptools instead of distutils.
- 10 min of reading on cython types and python types. I learnt I can use most of the builtin python types for static typing.
- 15 min of reannotating my python code with cython types.
- 10 min of modifying my
setup.py to use compiled module in my codebase.
- Plugged in the module directly to the
multiprocessing version of codebase. It works.
For the record, I of course, did not measure the exact timings of my investment. It may very well be the case that my perception of time was a little to attentive due too mental effort required while I was dealing with ctypes. But it should convey the feel of dealing with cython and ctypes
I want to call a C library from a Python application. I don’t want to wrap the whole API, only the functions and datatypes that are relevant to my case. As I see it, I have three choices:
- Create an actual extension module in C. Probably overkill, and I’d also like to avoid the overhead of learning extension writing.
- Use Cython to expose the relevant parts from the C library to Python.
- Do the whole thing in Python, using
ctypesto communicate with the external library.
I’m not sure whether 2) or 3) is the better choice. The advantage of 3) is that ctypes is part of the standard library, and the resulting code would be pure Python – although I’m not sure how big that advantage actually is.
Are there more advantages / disadvantages with either choice? Which approach do you recommend?
Edit: Thanks for all your answers, they provide a good resource for anyone looking to do something similar. The decision, of course, is still to be made for the single case—there’s no one “This is the right thing” sort of answer. For my own case, I’ll probably go with ctypes, but I’m also looking forward to trying out Cython in some other project.
With there being no single true answer, accepting one is somewhat arbitrary; I chose FogleBird’s answer as it provides some good insight into ctypes and it currently also is the highest-voted answer. However, I suggest to read all the answers to get a good overview.
Thanks again.
I’ll throw another one out there: SWIG
It’s easy to learn, does a lot of things right, and supports many more languages so the time spent learning it can be pretty useful.
If you use SWIG, you are creating a new python extension module, but with SWIG doing most of the heavy lifting for you.
Cython is a pretty cool tool in itself, well worth learning, and is surprisingly close to the Python syntax. If you do any scientific computing with Numpy, then Cython is the way to go because it integrates with Numpy for fast matrix operations.
Cython is a superset of Python language. You can throw any valid Python file at it, and it will spit out a valid C program. In this case, Cython will just map the Python calls to the underlying CPython API. This results in perhaps a 50% speedup because your code is no longer interpreted.
To get some optimizations, you have to start telling Cython additional facts about your code, such as type declarations. If you tell it enough, it can boil the code down to pure C. That is, a for loop in Python becomes a for loop in C. Here you will see massive speed gains. You can also link to external C programs here.
Using Cython code is also incredibly easy. I thought the manual makes it sound difficult. You literally just do:
$ cython mymodule.pyx
$ gcc [some arguments here] mymodule.c -o mymodule.so
and then you can import mymodule in your Python code and forget entirely that it compiles down to C.
In any case, because Cython is so easy to setup and start using, I suggest trying it to see if it suits your needs. It won’t be a waste if it turns out not to be the tool you’re looking for.
Personally, I’d write an extension module in C. Don’t be intimidated by Python C extensions — they’re not hard at all to write. The documentation is very clear and helpful. When I first wrote a C extension in Python, I think it took me about an hour to figure out how to write one — not much time at all.
ctypes is your best bet for getting it done quickly, and it’s a pleasure to work with as you’re still writing Python!
I recently wrapped an FTDI driver for communicating with a USB chip using ctypes and it was great. I had it all done and working in less than one work day. (I only implemented the functions we needed, about 15 functions).
We were previously using a third-party module, PyUSB, for the same purpose. PyUSB is an actual C/Python extension module. But PyUSB wasn’t releasing the GIL when doing blocking reads/writes, which was causing problems for us. So I wrote our own module using ctypes, which does release the GIL when calling the native functions.
One thing to note is that ctypes won’t know about #define constants and stuff in the library you’re using, only the functions, so you’ll have to redefine those constants in your own code.
Here’s an example of how the code ended up looking (lots snipped out, just trying to show you the gist of it):
from ctypes import *
d2xx = WinDLL('ftd2xx')
OK = 0
INVALID_HANDLE = 1
DEVICE_NOT_FOUND = 2
DEVICE_NOT_OPENED = 3
...
def openEx(serial):
serial = create_string_buffer(serial)
handle = c_int()
if d2xx.FT_OpenEx(serial, OPEN_BY_SERIAL_NUMBER, byref(handle)) == OK:
return Handle(handle.value)
raise D2XXException
class Handle(object):
def __init__(self, handle):
self.handle = handle
...
def read(self, bytes):
buffer = create_string_buffer(bytes)
count = c_int()
if d2xx.FT_Read(self.handle, buffer, bytes, byref(count)) == OK:
return buffer.raw[:count.value]
raise D2XXException
def write(self, data):
buffer = create_string_buffer(data)
count = c_int()
bytes = len(data)
if d2xx.FT_Write(self.handle, buffer, bytes, byref(count)) == OK:
return count.value
raise D2XXException
Someone did some benchmarks on the various options.
I might be more hesitant if I had to wrap a C++ library with lots of classes/templates/etc. But ctypes works well with structs and can even callback into Python.
If you have already a library with a defined API, I think ctypes is the best option, as you only have to do a little initialization and then more or less call the library the way you’re used to.
I think Cython or creating an extension module in C (which is not very difficult) are more useful when you need new code, e.g. calling that library and do some complex, time-consuming tasks, and then passing the result to Python.
Another approach, for simple programs, is directly do a different process (compiled externally), outputting the result to standard output and call it with subprocess module. Sometimes it’s the easiest approach.
For example, if you make a console C program that works more or less that way
$miCcode 10
Result: 12345678
You could call it from Python
>>> import subprocess
>>> p = subprocess.Popen(['miCcode', '10'], shell=True, stdout=subprocess.PIPE)
>>> std_out, std_err = p.communicate()
>>> print std_out
Result: 12345678
With a little string formating, you can take the result in any way you want. You can also capture the standard error output, so it’s quite flexible.
ctypes is great when you’ve already got a compiled library blob to deal with (such as OS libraries). The calling overhead is severe, however, so if you’ll be making a lot of calls into the library, and you’re going to be writing the C code anyway (or at least compiling it), I’d say to go for cython. It’s not much more work, and it’ll be much faster and more pythonic to use the resulting pyd file.
I personally tend to use cython for quick speedups of python code (loops and integer comparisons are two areas where cython particularly shines), and when there is some more involved code/wrapping of other libraries involved, I’ll turn to Boost.Python. Boost.Python can be finicky to set up, but once you’ve got it working, it makes wrapping C/C++ code straightforward.
cython is also great at wrapping numpy (which I learned from the SciPy 2009 proceedings), but I haven’t used numpy, so I can’t comment on that.
Warning: a Cython core developer’s opinion ahead.
I almost always recommend Cython over ctypes. The reason is that it has a much smoother upgrade path. If you use ctypes, many things will be simple at first, and it’s certainly cool to write your FFI code in plain Python, without compilation, build dependencies and all that. However, at some point, you will almost certainly find that you have to call into your C library a lot, either in a loop or in a longer series of interdependent calls, and you would like to speed that up. That’s the point where you’ll notice that you can’t do that with ctypes. Or, when you need callback functions and you find that your Python callback code becomes a bottleneck, you’d like to speed it up and/or move it down into C as well. Again, you cannot do that with ctypes. So you have to switch languages at that point and start rewriting parts of your code, potentially reverse engineering your Python/ctypes code into plain C, thus spoiling the whole benefit of writing your code in plain Python in the first place.
With Cython, OTOH, you’re completely free to make the wrapping and calling code as thin or thick as you want. You can start with simple calls into your C code from regular Python code, and Cython will translate them into native C calls, without any additional calling overhead, and with an extremely low conversion overhead for Python parameters. When you notice that you need even more performance at some point where you are making too many expensive calls into your C library, you can start annotating your surrounding Python code with static types and let Cython optimise it straight down into C for you. Or, you can start rewriting parts of your C code in Cython in order to avoid calls and to specialise and tighten your loops algorithmically. And if you need a fast callback, just write a function with the appropriate signature and pass it into the C callback registry directly. Again, no overhead, and it gives you plain C calling performance. And in the much less likely case that you really cannot get your code fast enough in Cython, you can still consider rewriting the truly critical parts of it in C (or C++ or Fortran) and call it from your Cython code naturally and natively. But then, this really becomes the last resort instead of the only option.
So, ctypes is nice to do simple things and to quickly get something running. However, as soon as things start to grow, you’ll most likely come to the point where you notice that you’d better used Cython right from the start.
There’s also one possibility to use GObject Introspection for libraries that are using GLib.
For calling a C library from a Python application there is also cffi which is a new alternative for ctypes. It brings a fresh look for FFI:
- it handles the problem in a fascinating, clean way (as opposed to ctypes)
- it doesn’t require to write non Python code (as in SWIG, Cython, …)
There is one issue which made me use ctypes and not cython and which is not mentioned in other answers.
Using ctypes the result does not depend on compiler you are using at all. You may write a library using more or less any language which may be compiled to native shared library. It does not matter much, which system, which language and which compiler. Cython, however, is limited by the infrastructure. E.g, if you want to use intel compiler on windows, it is much more tricky to make cython work: you should “explain” compiler to cython, recompile something with this exact compiler, etc. Which significantly limits portability.
If you are targeting Windows and choose to wrap some proprietary C++ libraries, then you may soon discover that different versions of msvcrt***.dll (Visual C++ Runtime) are slightly incompatible.
This means that you may not be able to use Cython since resulting wrapper.pyd is linked against msvcr90.dll (Python 2.7) or msvcr100.dll (Python 3.x). If the library that you are wrapping is linked against different version of runtime, then you’re out of luck.
Then to make things work you’ll need to create C wrappers for C++ libraries, link that wrapper dll against the same version of msvcrt***.dll as your C++ library. And then use ctypes to load your hand-rolled wrapper dll dynamically at the runtime.
So there are lots of small details, which are described in great detail in following article:
“Beautiful Native Libraries (in Python)“: http://lucumr.pocoo.org/2013/8/18/beautiful-native-libraries/
I know this is an old question but this thing comes up on google when you search stuff like ctypes vs cython, and most of the answers here are written by those who are proficient already in cython or c which might not reflect the actual time you needed to invest to learn those to implement your solution. I am a complete beginner in both. I have never touched cython before, and have very little experience on c/c++.
For the last two days, I was looking for a way to delegate a performance heavy part of my code to something more low level than python. I implemented my code both in ctypes and Cython, which consisted basically of two simple functions.
I had a huge string list that needed to processed. Notice list and string.
Both types do not correspond perfectly to types in c, because python strings are by default unicode and c strings are not. Lists in python are simply NOT arrays of c.
Here is my verdict. Use cython. It integrates more fluently to python, and easier to work with in general. When something goes wrong ctypes just throws you segfault, at least cython will give you compile warnings with a stack trace whenever it is possible, and you can return a valid python object easily with cython.
Here is a detailed account on how much time I needed to invest in both them to implement the same function. I did very little C/C++ programming by the way:
-
Ctypes:
- About 2h on researching how to transform my list of unicode strings to a c compatible type.
- About an hour on how to return a string properly from a c function. Here I actually provided my own solution to SO once I have written the functions.
- About half an hour to write the code in c, compile it to a dynamic library.
- 10 minutes to write a test code in python to check if
ccode works. - About an hour of doing some tests and rearranging the
ccode. - Then I plugged the
ccode into actual code base, and saw thatctypesdoes not play well withmultiprocessingmodule as its handler is not pickable by default. - About 20 minutes I rearranged my code to not use
multiprocessingmodule, and retried. - Then second function in my
ccode generated segfaults in my code base although it passed my testing code. Well, this is probably my fault for not checking well with edge cases, I was looking for a quick solution. - For about 40 minutes I tried to determine possible causes of these segfaults.
- I split my functions into two libraries and tried again. Still had segfaults for my second function.
- I decided to let go of the second function and use only the first function of
ccode and at the second or third iteration of the python loop that uses it, I had aUnicodeErrorabout not decoding a byte at the some position though I encoded and decoded everthing explicitely.
At this point, I decided to search for an alternative and decided to look into cython:
- Cython
- 10 min of reading cython hello world.
- 15 min of checking SO on how to use cython with
setuptoolsinstead ofdistutils. - 10 min of reading on cython types and python types. I learnt I can use most of the builtin python types for static typing.
- 15 min of reannotating my python code with cython types.
- 10 min of modifying my
setup.pyto use compiled module in my codebase. - Plugged in the module directly to the
multiprocessingversion of codebase. It works.
For the record, I of course, did not measure the exact timings of my investment. It may very well be the case that my perception of time was a little to attentive due too mental effort required while I was dealing with ctypes. But it should convey the feel of dealing with cython and ctypes