Extract values in Pandas value_counts()

First you have to sort the dataframe by the count column max to min if it’s not sorted that way already. In your post, it is in the right order already but I will sort it anyways:

dataframe.sort_index(by='count', ascending=[False])
    col     count
0   apple   5
1   sausage 2
2   banana  2
3   cheese  1 

Then you can output the col column to a list:

dataframe['col'].tolist()
['apple', 'sausage', 'banana', 'cheese']

#!/usr/bin/env python

import pandas as pd

# Make example dataframe
df = pd.DataFrame([(1, 'Germany'),
                   (2, 'France'),
                   (3, 'Indonesia'),
                   (4, 'France'),
                   (5, 'France'),
                   (6, 'Germany'),
                   (7, 'UK'),
                   ],
                  columns=['groupid', 'country'],
                  index=['a', 'b', 'c', 'd', 'e', 'f', 'g'])

# What you're looking for
values = df['country'].value_counts().keys().tolist()
counts = df['country'].value_counts().tolist()

Now, print(df['country'].value_counts()) gives:

France       3
Germany      2
UK           1
Indonesia    1

and print(values) gives:

['France', 'Germany', 'UK', 'Indonesia']

and print(counts) gives:

[3, 2, 1, 1]

If anyone missed it out in the comments, try this:

dataframe[column].value_counts().to_frame()

The best way to extract the values is to just do the following

json.loads(dataframe[column].value_counts().to_json())

This returns a dictionary which you can use like any other dict. Using values or keys.

 {"apple": 5, "sausage": 2, "banana": 2, "cheese": 1}