# Passing numpy arrays between python and Julia

**URL:** <https://discourse.julialang.org/t/passing-numpy-arrays-between-python-and-julia/25826>\
**Category:** General Usage\
**Created:** [June 29, 2019, 12:16am UTC](https://discourse.julialang.org/t/passing-numpy-arrays-between-python-and-julia/25826 "2019-06-29T00:16:45Z")\
**Posts on this page:** 4\
**Page:** 1

<div class="post-metadata">

**Author:** ![00krishna](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/00krishna/32/8843_2.png) [@00krishna](https://discourse.julialang.org/u/00krishna)\
**Post date:** [June 29, 2019, 12:16am UTC](https://discourse.julialang.org/t/passing-numpy-arrays-between-python-and-julia/25826/1 "2019-06-29T00:16:45Z")

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I am still new to Julia, so just wanted to incrementally test out some features. One thing I wanted to try was accelerating some python code I have by offloading it to Julia.

The first scenario is simple. So I have a dictionary of model settings that I want to pass to Julia. Then after running the model in julia–a time series simulation–I wanted to return a multidimensional array from julia back to python. To be precise by multi-dimensional I mean a time series that has a set of variables in the columns, the rows indexed by the timestep, and finally the depth dimension indexed by complete run of the simulation (where I might run 100 complete simulations).

So like I said, I pass the model parameters as a dictionary and then get back a numpy array.

I was looking at `PyCall.jl` to do this. The documentation says:

> Multidimensional arrays exploit the NumPy array interface for conversions between Python and Julia. By default, they are passed from Julia to Python without making a copy, but from Python to Julia a copy is made; no-copy conversion of Python to Julia arrays can be achieved with the `PyArray` type below.

However, I did not see an example of this feature implemented in actual code. So do I need to create an empty array and pass it as a reference to the Julia PyCall function, or do I need to do any special handling of these types of requests. Does anyone have an example code to do this?

Oops, forgot to mention that I looked around for some other posts on this topic, because I figured it was not unique. The closest I found was this post below, but I did not see any code in it as an example.

> [@How hard would it be to implement Numpy.jl, i.e. Numpy in Julia?](https://discourse.julialang.org/t/how-hard-would-it-be-to-implement-numpy-jl-i-e-numpy-in-julia/22080):
>
> Hi, I’m working with Pythran ([http://github.com/serge-sans-paille/pythran](http://github.com/serge-sans-paille/pythran)), a Python/Numpy to C++ transpiler, which of course supports only a subset of Python (in particular not all the crazy cool Python stuffs stupid for performance). With Pythran, you can prototype in Python/Numpy and get very efficient C++ which does not use the Python interpreter. We were thinking about the possibility to implement a Julia backend for Pythran, i.e. to be able to transpile the subset of Python/Numpy support…

Thanks.

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<div class="post-metadata">

**Author:** ![tkf](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/tkf/32/17635_2.png) [@tkf](https://discourse.julialang.org/u/tkf)\
**Post date:** [June 29, 2019, 12:52am UTC](https://discourse.julialang.org/t/passing-numpy-arrays-between-python-and-julia/25826/2 "2019-06-29T00:52:01Z")

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If you just want to “get back a numpy array”, you don’t need to do anything:

```julia
julia> using PyCall

julia> py"""
       def printflags(xs):
           print(xs.flags)
       """

julia> pyprintflags = py"printflags"
PyObject <function printflags at 0x7fe387b5cea0>

julia> pyprintflags(ones(2, 3))
  C_CONTIGUOUS : False
  F_CONTIGUOUS : True
  OWNDATA : False
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False

```

Observe that `OWNDATA` flag is `False`; Python/Numpy does not own the data but Julia does in this case.

Or, an example a bit more closer to what you describe is

```julia
julia> py"""
       jlones = $ones
       """

julia> py"""
       printflags(jlones(2, 3))
       """
  C_CONTIGUOUS : False
  F_CONTIGUOUS : True
  OWNDATA : False
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False

```

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<div class="post-metadata">

**Author:** ![00krishna](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/00krishna/32/8843_2.png) [@00krishna](https://discourse.julialang.org/u/00krishna)\
**Post date:** [June 29, 2019, 1:38am UTC](https://discourse.julialang.org/t/passing-numpy-arrays-between-python-and-julia/25826/3 "2019-06-29T01:38:04Z")

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Hey @tkf this is helpful. I had not seen this approach to interrogating the calls, so that is nice. Do you know the corresponding python code to call into Julia? Like if I am in python and I wanted to pass the array to a Julia function.

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<div class="post-metadata">

**Author:** ![tkf](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/tkf/32/17635_2.png) [@tkf](https://discourse.julialang.org/u/tkf)\
**Post date:** [June 29, 2019, 1:48am UTC](https://discourse.julialang.org/t/passing-numpy-arrays-between-python-and-julia/25826/4 "2019-06-29T01:48:16Z")

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A minimal example is something like this

```julia
julia> using PyCall

julia> py"""
       import numpy
       xs = numpy.ones((2, 3))
       """

julia> xs = PyArray(py"xs"o)
2×3 PyArray{Float64,2}:
 1.0 1.0 1.0
 1.0 1.0 1.0

julia> xs[1, 1] = 2
2

julia> py"""
       print(xs)
       """
[[2. 1. 1.]
 [1. 1. 1.]]

```

Notice the `o` in `py"xs"o`; this tells PyCall to _not_ auto-convert/copy the Numpy array `xs`. This is then converted to `PyArray` manually.

To pass a Julia function to Python, you need to use `pyfunction`:

```julia
julia> printtype(x) = println(typeof(x))
printtype (generic function with 1 method)

julia> pyjlprinttype = pyfunction(printtype, PyArray)
PyObject <PyCall.jlwrap PyCall.FuncWrapper{Tuple{PyArray},typeof(printtype)}(printtype, Dict{Symbol,Any}())>

julia> py"""
       jlprinttype = $pyjlprinttype
       jlprinttype(xs)
       """
PyArray{Float64,2}

```