If you are a newcomer to Cython just like me, it is probably that you will be confused by the usage time of np.float64_t and np.float64.

Below, I’ll briefly introduce how these two types are fundamentally different, and generalize this concept to other datatypes as well.

Before that, we need to know a bit of Cython.

## cimport in Cython

In Python, we use the import statement to access functions, objects, and classes inside other modules and packages.

Cython also fully supports the import statement, so it allows us to access Python objects defined in external Python modules.

However, note that if above were the end of the story, Cython modules would not allow to access other Cython modules’ cdef functions, ctypedefs, or structs, and it would not allow C-level access to other extension types.

To remedy this, Cython has a cimport statement that provides compile time access to C-level constructs, and it looks for these constructs’ declarations from separate Cython files called definition files, which have a .pxd extension and need to be created by us.

In a .pxd file, we can place the declarations of C-level constructs that we wish to share, and only the declarations here are cimportable. Also, since some_file_name.pxd created by us have the same base name as the original file some_file_name.pyx, they are treated as one namespace by Cython. Therefore, we need to modify some_file_name.pyx in order to remove the repeat declarations in it.

And after we have created the .pxd file and clear the .pyx file, now an external implementation file can access all C-level constructs inside .pyx via the cimport statement.

Let’s take an real-world example to see how cimport works!

## cimport numpy as np

In some files of the well-known machine library scikit-learn (such as this one), you can find the following code snippet:

cimport numpy as np
import numpy as np


I remember I was stunned when I saw these lines for the first time, WHAT IS IT?

Well, the good news is that we now know the basics of the cimport statement, so we can figure it out step by step.

First, since only the declarations in .pxd file are cimportable, we have to identify which .pxd file is Cython looking when executing cimport numpy as np.

After a bit of research, we should find that a file called __init__.pxd lies in the numpy folder under our Cython installation. An __init__.pxd file can make Cython treat the directory as a package just like how __init.py__ works for Python (see here). Therefore, in this case Cython will treat the numpy folder as a package and give us access to Numpy’s C API defined in the __init__.pxd file during compile time.

On the contrary, import numpy as np will only give us access to Numpy’s pure-Python API and it occurs at runtime.

Note that here we use the same alias (i.e., np) for both of the imported external packages, but thanks to the almighty Cython which will internally handles this ambiguity, we don’t not need to use different names.

## np.float64 v.s np.float64_t

So here comes our main topic, what is the difference between np.float64 and np.float64_t, and which should I use?

#### np.float64

np.float64 is a Python type object that is defined at Python level to represent 64 bits float data, and it has common attributes such as __name__ that most of other Python objects have too. You can simply use the following code to verify it:

import numpy as np
type(np.float64)
print np.float64
print np.float64.__name__


#### np.float64

In __init__.pxd, you can find the following lines:

ctypedef double       npy_float64
ctypedef npy_float64    float64_t


So it is clear that np.float64_t represents the type double in C, and it is nowhere near as a Python object. Therefore, if you call print np.float64_t in a .pyx file, it will warn you the following message during compile time: 'float64_t' is not a constant, variable or function identifier

#### Which to use?

Let’s take another simple example to illustrate the usage time between these two types:

import numpy as np
cimport numpy as np

def test():

// 1
cdef np.ndarray[np.float64_t, ndim=1] array

// 2
array = np.empty(10, dtype=np.float64)

print array

1. We use np.ndarray to declare the type of the object exposing the buffer interface, and place C data type inside the bracket for the array elements. So, we should make sure we use np.float64_t here to specify the element’s data type .

2. To initialize the Numpy buffer we just declared, we can create an array object at Python level and assign it to the Numpy buffer. In this case, we should use np.float64 since we are not declaring C type variable.

Of course, The same concept can be generalized to other data types (e.g., np.int32 v.s np.int32_t, np.int64 v.s np.int_64_t, etc.)

## Summary

After working on Cython for a month, I found debugging in Cython is both hard and frustrated because the documents is not really thorough.

Consequently, I hope this blog post can safe your effort by helping you clarify the difference between data types defined in Cython and Python.

In the future, I will also document more of my findings about Cython during my GSoC.