Python pandas - How to create separate lists of duplicates and uniques?

Question

I have list of uniques from column ACCOUNTMANAGER and I need to pick sorted duplicates to separate file.

Is it possible to use duplicates or something similar to pick identical column values and save them to separate lists?

Let's say ACCOUNTMANAGER contains list of names ['Jack', 'Jack', 'Dane', 'Jessica', 'Jessica', 'Jessica' ]. I would like to have jack list of all Jacks, Dane list oven if one value and list of Jessicas. How can I do this using uniques and duplicates? Here is my code:

uniques = df['ACCOUNTMANAGER'].unique()
print(uniques)

Answer 1

You can create dictionary of lists with repeated values is grouping by column and create lists for same column used for grouping:

d = df.groupby('ACCOUNTMANAGER')['ACCOUNTMANAGER'].agg(list).to_dict()

print (d['Jack'])
print (d['Dane'])
print (d['Jessica'])

In python string variables are not recommneded, but possible e.g. by globals:

for n, vals in df.groupby('ACCOUNTMANAGER')['ACCOUNTMANAGER'].agg(list).items():
    globals()[n] =  vals

print (Jessica)

Answer 2

If its for only one column, you can do this way as well (using pandas value_counts)

>>> df = pd.DataFrame({'ACCOUNTMANAGER': ['Jack', 'Jack', 'Dane', 'Jessica', 'Jessica', 'Jessica' ]})

>>> df.ACCOUNTMANAGER.value_counts()
Jessica    3
Jack       2
Dane       1
Name: ACCOUNTMANAGER, dtype: int64

>>> for key, value in df.ACCOUNTMANAGER.value_counts().items():
...     print([key] * value)

['Jessica', 'Jessica', 'Jessica']
['Jack', 'Jack']
['Dane']