julia PyPlot support missing data

[deszoeke]

Passing a value of missing, a first class type in Julia Base, to matplotlib should simply not the plot missing data. It should handle missing just like it presently does NaN.

However, passing a missing to matplotlib results in long and unhelpful errors from PyPlot and matplotlib:

plot([0,missing,2])

ERROR: PyError ($(Expr(:escape, :(ccall(#= ~/.julia/packages/PyCall/1gn3u/src/pyfncall.jl:43 =# @pysym(:PyObject_Call), PyPtr, (PyPtr, PyPtr, PyPtr), o, pyargsptr, kw))))) <class 'TypeError'>
TypeError("float() argument must be a string or a real number, not 'PyCall.jlwrap'")
  File "mypath/python3.12/site-packages/matplotlib/pyplot.py", line 3794, in plot
    return gca().plot(
           ^^^^^^^^^^^
  File "/mypath/python3.12/site-packages/matplotlib/axes/_axes.py", line 1781, in plot
    self.add_line(line)
  File "/mypath/python3.12/site-packages/matplotlib/axes/_base.py", line 2339, in add_line
    self._update_line_limits(line)
  File "/mypath/python3.12/site-packages/matplotlib/axes/_base.py", line 2362, in _update_line_limits
    path = line.get_path()
           ^^^^^^^^^^^^^^^
  File "/mypath/python3.12/site-packages/matplotlib/lines.py", line 1037, in get_path
    self.recache()
  File "/mypath/python3.12/site-packages/matplotlib/lines.py", line 679, in recache
    y = _to_unmasked_float_array(yconv).ravel()
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/mypath/python3.12/site-packages/matplotlib/cbook.py", line 1398, in _to_unmasked_float_array
    return np.asarray(x, float)
           ^^^^^^^^^^^^^^^^^^^^

Stacktrace:
  [1] pyerr_check
    @ ~/.julia/packages/PyCall/1gn3u/src/exception.jl:75 [inlined]
  [2] pyerr_check
    @ ~/.julia/packages/PyCall/1gn3u/src/exception.jl:79 [inlined]
  [3] _handle_error(msg::String)
    @ PyCall ~/.julia/packages/PyCall/1gn3u/src/exception.jl:96
  [4] macro expansion
    @ ~/.julia/packages/PyCall/1gn3u/src/exception.jl:110 [inlined]
  [5] #107
    @ ~/.julia/packages/PyCall/1gn3u/src/pyfncall.jl:43 [inlined]
  [6] disable_sigint
    @ ./c.jl:473 [inlined]
  [7] __pycall!
    @ ~/.julia/packages/PyCall/1gn3u/src/pyfncall.jl:42 [inlined]
  [8] _pycall!(ret::PyObject, o::PyObject, args::Tuple{Vector{Union{Missing, Int64}}}, nargs::Int64, kw::Ptr{Nothing})
    @ PyCall ~/.julia/packages/PyCall/1gn3u/src/pyfncall.jl:29
  [9] _pycall!
    @ ~/.julia/packages/PyCall/1gn3u/src/pyfncall.jl:11 [inlined]
 [10] pycall
    @ ~/.julia/packages/PyCall/1gn3u/src/pyfncall.jl:83 [inlined]
 [11] plot(args::Vector{Union{Missing, Int64}}; kws::@Kwargs{})
    @ PyPlot ~/.julia/packages/PyPlot/rWSdf/src/PyPlot.jl:194
 [12] plot(args::Vector{Union{Missing, Int64}})
    @ PyPlot ~/.julia/packages/PyPlot/rWSdf/src/PyPlot.jl:190
 [13] top-level scope
    @ REPL[56]:1

For years I have wrapped my inputs to matplotlib in the helper function

m2n(x) = ismissing(x) ? NaN : x

It is awkward.

I propose then, that

1. missing values are simply (not) plotted, just like NaNs.
2. a brief warning catch and warn the user of missings in the data.

The proposed change will make PyPlot more compatible with Julia and Plots.jl, cf.
JuliaPlots/Plots.jl#1706

I expect this will not break most cases, because missing is just a newer use pattern than NaN for missing data.

[Alexander-Barth]

This would be nice to have. See also this issue here:
JuliaPy/PyCall.jl#616

One can also used masked arrays as shown in this example if one wants to avoid a promotion of integers to floats.