# Help benchmarking small python code

**URL:** <https://discourse.julialang.org/t/help-benchmarking-small-python-code/67283>\
**Category:** Offtopic\
**Tags:** python\
**Created:** [August 29, 2021, 1:56pm UTC](https://discourse.julialang.org/t/help-benchmarking-small-python-code/67283 "2021-08-29T13:56:35Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![lmiq](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/lmiq/32/18314_2.png) [@lmiq](https://discourse.julialang.org/u/lmiq)\
**Post date:** [August 29, 2021, 1:56pm UTC](https://discourse.julialang.org/t/help-benchmarking-small-python-code/67283/1 "2021-08-29T13:56:35Z")

</div>

**Disclaimer:** I asked now the same thing in the Python discourse forum, and had deleted it here. But anyway, if anyone want’s to help, it will be most welcome.

I am trying to compare a neighbour list search to other implementations, and `scipy` has one. I am benchmarking it but it seem to be so slow that I don’t really trust what I’m doing. I am just doing this:

Input file:

```python
import numpy as np
from scipy.spatial import KDTree

def pp(points) : 
    kd_tree = KDTree(points) 
    pairs = kd_tree.query_pairs(r=0.05) 
    return pairs 

points = np.random.random((10000,3))

#%timeit pp(points)

```

then I run:

```julia
% ipython3 -i nn.py

In [1]: %timeit pp(points)                                                                            
2.78 s ± 134 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

```

It that correct? It sounds strange, because the Julia solutions that do the same thing take of the order of 6ms for that size of points, and these are just a couple of library calls, I wouldn’t expect the different to be that large.