Python's sum vs. NumPy's numpy.sum

Question

What are the differences in performance and behavior between using Python's native sum function and NumPy's numpy.sum? sum works on NumPy's arrays and numpy.sum works on Python lists and they both return the same effective result (haven't tested edge cases such as overflow) but different types.

>>> import numpy as np >>> np_a = np.array(range(5)) >>> np_a array([0, 1, 2, 3, 4]) >>> type(np_a) <class 'numpy.ndarray')  >>> py_a = list(range(5)) >>> py_a [0, 1, 2, 3, 4] >>> type(py_a) <class 'list'>  # The numerical answer (10) is the same for the following sums: >>> type(np.sum(np_a)) <class 'numpy.int32'> >>> type(sum(np_a)) <class 'numpy.int32'> >>> type(np.sum(py_a)) <class 'numpy.int32'> >>> type(sum(py_a)) <class 'int'> 

Edit: I think my practical question here is would using numpy.sum on a list of Python integers be any faster than using Python's own sum?

Additionally, what are the implications (including performance) of using a Python integer versus a scalar numpy.int32? For example, for a += 1, is there a behavior or performance difference if the type of a is a Python integer or a numpy.int32? I am curious if it is faster to use a NumPy scalar datatype such as numpy.int32 for a value that is added or subtracted a lot in Python code.

For clarification, I am working on a bioinformatics simulation which partly consists of collapsing multidimensional numpy.ndarrays into single scalar sums which are then additionally processed. I am using Python 3.2 and NumPy 1.6.

Thanks in advance!

Answer 1

I got curious and timed it. numpy.sum seems much faster for numpy arrays, but much slower on lists.

import numpy as np import timeit  x = range(1000) # or  #x = np.random.standard_normal(1000)  def pure_sum():     return sum(x)  def numpy_sum():     return np.sum(x)  n = 10000  t1 = timeit.timeit(pure_sum, number = n) print 'Pure Python Sum:', t1 t2 = timeit.timeit(numpy_sum, number = n) print 'Numpy Sum:', t2 

Result when x = range(1000):

Pure Python Sum: 0.445913167735 Numpy Sum: 8.54926219673 

Result when x = np.random.standard_normal(1000):

Pure Python Sum: 12.1442425643 Numpy Sum: 0.303303771848 

I am using Python 2.7.2 and Numpy 1.6.1