text: Hi,
  I have a Python job that calls Julia for some computation on my datasets. Right now, passing data back and forth between Julia and Python is a bottleneck – my current process is to save the Pandas DataFrame as a feather file to disk, and load it from Julia.
  I know PyJulia can pass numpy arrays with 0 copying, and I’m trying to figure out if it’s possible to do the same thing with an Arrow Table. However, a table gets passed as a
  PyObject, and the
  Arrow library in Julia doesn’t seem to be able to convert it, even on the latest version.
  import pandas as pd df = pd.DataFrame({"id": [1, 2], "name": ["bob", "sam"]}) table = pyarrow.Table.from_pandas(df) import julia jl = julia.Julia(compiled_modules=False) from julia import Arrow from julia import Base Base.length([1, 2, 3]) >> 3 Base.length(table) >> 2 Base.typeof(table) >> <PyCall.jlwrap PyObject> Arrow.Table(table) RuntimeError: <PyCall.jlwrap (in a Julia function called from Python) JULIA: MethodError: no method matching Arrow.Table(::PyObject)
  Is there a better way to pass datasets between the two?