how to define a list in Cython

Question

I want to transform the below python code in Cython:

x_array = [] x_array.append(x_new)

I tried the following Cython codes but it gives error:

cdef np.ndarray[double, dim=1] x_array x_array.append(x_new)

The error shows:

Cannot coerce list to type [double, dim=1]

Welcome to stack overflow. When asking a question about a problem caused by your code, you will get much better answers if you provide code people can use to reproduce the problem. Please have a read on how to provide a Minimal, Complete, and Verifiable example. — danny
– danny, Commented May 4, 2018 at 9:52
You've typed is as a numpy array rather than a list. There isn't really an advantage to trying to fixing the type of builtin Python objects such as list — DavidW
– DavidW, Commented May 4, 2018 at 10:30
is there a way to fix the problem? i have a lot of list objects in my python code. Is there a way of using Cython to improve the performance? — Michael Fan Zhang
– Michael Fan Zhang, Commented May 7, 2018 at 3:30

DavidW · Accepted Answer · 2018-05-07 14:55:38Z

Your options are:

cdef list x_array. This lets Cython know that the type of x_array is actually a list. You may get a small speed-up from this.
Make x_array a numpy array instead. If all the elements in the list are the same simple, numeric type then this is probably a better option. Be aware that appending to numpy arrays is likely to be pretty slow, so you should calculate the size in advance.
```
cdef np.array[double, dim=1] x_array = np.zeros((some_precomputed_size,)) # or cdef double[:] x_array = np.zeros((some_precomputed_size,)) 
```
Note that this will only give you a speed-up for some types of operations (mostly accessing individual elements in Cython)
If you're set on using Python lists you can sometimes get a speed-up by accessing them through the Python C API in Cython. This answer provides an example of where that worked well. This works best when you know a size in advance and so you can pre-allocate the array (i.e. don't append!) and also avoid some Cython reference counting. It's very easy to go wrong and make reference counting errors with this method, so proceed carefully.

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