How to deal with name column in Scikitlearn randomforest classifier. python 3
Well name is a unique thing and an id kind of use sklearn.preprocessing.LabelEncoder after storing the original to a separate list. It will automatically convert the names to a serial number. 
Also, note if it's a unique thing you should remove names during predicting.
I have a dataframe containing 13 columns. Among 13 three columns are string. One string column is simple male and female which I converted to 1 and 0 using

pd.get_dummies()

2nd column contains three different types of string so, easily converted to array using 

from sklearn.feature_extraction.text import CountVectorizer

No issue at all. Problem is my third and last column contains large number of names. if I try to convert using Countvectorizer it converts the names into long unreadable strings. 

df['name']=Countvectorizer.fit_transform(df.name)

if I try to convert back it to dataframe as shown in other examples on stackoverflow page in this case I get this

245376      (0, 14297)\t1\n  (1, 5843)\t1\n  (1, 13365)...
  245377      (0, 14297)\t1\n  (1, 5843)\t1\n  (1, 13365)...

Name: supplier_name, dtype: object
and this next code results Memory Error

df['name'] =pd.DataFrame(CV.fit_transform(df.name).toarray(),columns=CV.get_feature_names())

I have looked that issue as well. 
Question: is there any way best to use this name column in numeric forms except above mentioned. Or any other idea how to improve this so that data perfectly fit in Randomforest classifier. As, Dataframe is quit large containing 123790 rows. Thank you in advance for help or suggestion.