# JuliaCall: Pass numpy array to julia function as Vector{Float64}

**URL:** <https://discourse.julialang.org/t/juliacall-pass-numpy-array-to-julia-function-as-vector-float64/87728>\
**Category:** General Usage\
**Tags:** question, package, type, python, juliacall\
**Created:** [September 23, 2022, 10:01pm UTC](https://discourse.julialang.org/t/juliacall-pass-numpy-array-to-julia-function-as-vector-float64/87728 "2022-09-23T22:01:54Z")\
**Posts on this page:** 1\
**Showing post:** 5

<div class="post-metadata">

**Author:** ![cjdoris](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/cjdoris/32/213133_2.png) [@cjdoris](https://discourse.julialang.org/u/cjdoris)\
**Post date:** [September 24, 2022, 9:25am UTC](https://discourse.julialang.org/t/juliacall-pass-numpy-array-to-julia-function-as-vector-float64/87728/5 "2022-09-24T09:25:43Z")

</div>

This is a nice example, so I’ll run through the options in detail.

To summarise the problem, you have defined a Julia function `myfunc(::Vector{Float64})` and then find that you cannot call it from Python (using JuliaCall) with a `list` or `numpy.ndarray` as the argument. This fails because of the default conversion rules when calling a Julia function from Python:

- `list` is converted to `PyList{Py}`
- `numpy.ndarray` is converted to `PyArray{Float64,1}` (in this case)

**Option 1 (highly recommended):** As suggested in previous replies, make the signature of `myfunc` more general, e.g. `myfunc(::AbstractArray{<:Real})`. This is generally a good thing to do in Julia any, but in this case it means that it can be called with a `PyArray{Floay64,1}` argument, so will work with `numpy.ndarray` or any other Python array type. However it will still not work with `list` because `PyList{Py} <: AbstractVector{<:Real}` is false (because `Py <: Real` is false).

**Option 2:** If you can’t do that, then write a Julia wrapper function

```julia
myfunc2(a::AbstractVector) = myfunc(convert(Vector{Float64}, a))

```

and call that instead.

**Option 3:** If you have done 2 or 3, then you can make it also work with lists by explicitly converting to a numpy array from Python:

```python
jl.myfunc(np.asarray([1.0, 2.0, 3.0]))

```

**Option 4:** Or without any changes to `myfunc` you could instead explicitly convert to a `Vector{Float64}` from Python:

```python
jl.myfunc(juliacall.convert(jl.Vector[jl.Float64], [1.0, 2.0, 3.0]))

```

**Option 5:** Or you can create a wrapper function which does the conversion on the Julia side:

```python
myfunc = jl.seval("pyfunc((a::Py)->myfunc(pyconvert(Vector{Float64}, a)))")

```

To explain this a bit, `pyfunc` wraps a Julia function into a Python function, but the arguments are not automatically converted (hence the `a::Py` argument) and so you can call `pyconvert` to convert them to the desired type. This option is mostly useful from the Julia side to create a Python callback function.

If I were you I’d do Option 1 and maybe Option 3. Or do Option 4.

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_[View the full topic](https://discourse.julialang.org/t/juliacall-pass-numpy-array-to-julia-function-as-vector-float64/87728)._