Update 2022-03

This answer by caner using transform looks much better than my original answer!

df['sales'] / df.groupby('state')['sales'].transform('sum')

Thanks to this comment by Paul Rougieux for surfacing it.

Original Answer (2014)

Paul H's answer is right that you will have to make a second groupby object, but you can calculate the percentage in a simpler way -- just groupby the state_office and divide the sales column by its sum. Copying the beginning of Paul H's answer:

# From Paul H
import numpy as np
import pandas as pd
np.random.seed(0)
df = pd.DataFrame({'state': ['CA', 'WA', 'CO', 'AZ'] * 3,
                   'office_id': list(range(1, 7)) * 2,
                   'sales': [np.random.randint(100000, 999999)
                             for _ in range(12)]})
state_office = df.groupby(['state', 'office_id']).agg({'sales': 'sum'})
# Change: groupby state_office and divide by sum
state_pcts = state_office.groupby(level=0).apply(lambda x:
                                                 100 * x / float(x.sum()))

Returns:

                     sales
state office_id           
AZ    2          16.981365
      4          19.250033
      6          63.768601
CA    1          19.331879
      3          33.858747
      5          46.809373
CO    1          36.851857
      3          19.874290
      5          43.273852
WA    2          34.707233
      4          35.511259
      6          29.781508

I think your code is already nearly optimal and Pythonic. But there is some little things to improve:

• cluster_count.sum() returns you a Series object so if you are working with it outside the Pandas, it is better to specify the column: cluster_count.char.sum(). This way you will get an ordinary Python integer.
• Pandas has an ability to manipulate with columns directly so instead of apply function usage you can just write arithmetical operations with column itself: cluster_count.char = cluster_count.char * 100 / cluster_sum (note that this line of code is in-place work).

Here is the final code:

df = pd.DataFrame({'char':['a','b','c','d','e'], 'cluster':[1,1,2,2,2]})
cluster_count=df.groupby('cluster').count()
cluster_sum=sum(cluster_count.char)
cluster_count.char = cluster_count.char * 100 / cluster_sum

Edit 1: You can do the magic even without cluster_sum variable, just in one line of code:

cluster_count.char = cluster_count.char * 100 / cluster_count.char.sum()

But I am not sure about its perfomance (it can probably recalculate the sum for each group).

Here is the complete example based on pandas groupby, sum functions. The basic idea is to group data based on 'Localization' and to apply a function on group.

import pandas as pd
from io import StringIO
#For Python 2, replace previous line with: from StringIO import StringIO

data = \
"""Localization,RNA level,Size
cytoplasm                            ,1 Non-expressed, 7
cytoplasm                            ,2 Very low     ,13
cytoplasm                            ,3 Low          , 8
cytoplasm                            ,4 Medium       , 6
cytoplasm                            ,5 Moderate     , 8
cytoplasm                            ,6 High         , 2
cytoplasm                            ,7 Very high    , 6
cytoplasm & nucleus                  ,1 Non-expressed, 5
cytoplasm & nucleus                  ,2 Very low     , 8
cytoplasm & nucleus                  ,3 Low          , 2
cytoplasm & nucleus                  ,4 Medium       ,10
cytoplasm & nucleus                  ,5 Moderate     ,16
cytoplasm & nucleus                  ,6 High         , 6
cytoplasm & nucleus                  ,7 Very high    , 5
cytoplasm & nucleus & plasma membrane,1 Non-expressed, 6
cytoplasm & nucleus & plasma membrane,2 Very low     , 3
cytoplasm & nucleus & plasma membrane,3 Low          , 3
cytoplasm & nucleus & plasma membrane,4 Medium       , 7
cytoplasm & nucleus & plasma membrane,5 Moderate     , 8
cytoplasm & nucleus & plasma membrane,6 High         , 4
cytoplasm & nucleus & plasma membrane,7 Very high    , 1"""

# Create the dataframe
df = pd.read_csv(StringIO(data))
df['Localization'].str.strip()
df['RNA level'].str.strip()
df['Size'].astype(int)
df['Percent'] = df.groupby('Localization')['Size'].transform(lambda x: x/sum(x))