I have this code to create a swarmplot from data from a DataFrame:

df = pd.DataFrame({"Refined__Some_ID":some_id_list,"Refined_Age":age_list,"Name":name_list                   })
#Creating dataframe with strings from the lists
select  = df.apply(lambda row : any([isinstance(e, str) for e in row  ]),axis=1) 
#Exlcluding data from select in a new dataframe
dfAnalysis = df[~select]
dfAnalysis['Refined_Age'].replace('', np.nan, inplace=True)
dfAnalysis = dfAnalysis.dropna()
dfAnalysis['Refined_Age'] = dfAnalysis['Refined_Age'].apply(int)
# print dfAnalysis
print type(dfAnalysis['Refined_Patient_Age'][1])
g = sns.swarmplot(x = dfAnalysis['Refined_ID'],y = dfAnalysis['Refined_Age'], hue = dfAnalysis['Name'], orient="v")
g.set_xticklabels(g.get_xticklabels(),rotation=30)
# print g

It's taking a crazy amount of time to run (14 hours and counting!). How can I speed it up? Also, why is the code so slow in the first place?

The 3 lists being included in the dataframe are from a Couchdb database with about 320k documents.

UPDATE 1

I had intended to view the first 20 categories only but excluded the code to do so.

The line should have been:

x = dfAnalysis['Refined_ID'].iloc[:20]

Do you really mean a swarmplot with several hundred thousand points? Besides it's gonna take forever, it's nonsense. Try with the first 1000 and see what kind of mess you get. Then use a boxplot or a violinplot instead. Try to understand your tools before using them.

From the docstring:

[...] it does not scale as well to large numbers of observations (both in terms of the ability to show all the points and in terms of the computation neededto arrange them).