Map_partition function to apply a plotting function on partitions

I’m wondering if it’s possible to use a customized plotting function in the map_partition function?

Currently, my implementation (parsed) looks something like:

def plotting_function(pandas_df, column, kwargs):
     # Some stuff
     f, t, Sxx = scipy.signal.spectogram(pandas_df[column], kwargs)
     fig = matplotlib.pyplot.figure()
     fig.gca().pcolormesh(t, f, tmp)
     return []

I would like to apply this function on the partitions (n=10) of a dask dataframe created with a set of parquet files:

dask_df = dd.read_parquet(base_path+'/*.parquet')
dask_df.map_partitions(plotting_function, kwargs, meta=[])

There is no error when I run this. However, there is no plots that appear in the jupyter cell output either. It’s just an empty cell output. Any suggestions? Am I completely misinterpreting the intended use of this function? If so, my apologies, I am quite new to Dask.