sorting by a custom list in pandas

I just discovered that with pandas 15.1 it is possible to use categorical series (http://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html#categoricals)

As for your example, lets define the same data-frame and sorter:

import pandas as pd

data = {
    'id': [2967, 5335, 13950, 6141, 6169],
    'Player': ['Cedric Hunter', 'Maurice Baker', 
               'Ratko Varda' ,'Ryan Bowen' ,'Adrian Caldwell'],
    'Year': [1991, 2004, 2001, 2009, 1997],
    'Age': [27, 25, 22, 34, 31],
    'Tm': ['CHH', 'VAN', 'TOT', 'OKC', 'DAL'],
    'G': [6, 7, 60, 52, 81]
}

# Create DataFrame
df = pd.DataFrame(data)

# Define the sorter
sorter = ['TOT', 'ATL', 'BOS', 'BRK', 'CHA', 'CHH', 'CHI', 'CLE', 'DAL', 'DEN',
          'DET', 'GSW', 'HOU', 'IND', 'LAC', 'LAL', 'MEM', 'MIA', 'MIL',
          'MIN', 'NJN', 'NOH', 'NOK', 'NOP', 'NYK', 'OKC', 'ORL', 'PHI',
          'PHO', 'POR', 'SAC', 'SAS', 'SEA', 'TOR', 'UTA', 'VAN', 'WAS', 'WSB']

With the data-frame and sorter, which is a category-order, we can do the following in pandas 15.1:

# Convert Tm-column to category and in set the sorter as categories hierarchy
# Youc could also do both lines in one just appending the cat.set_categories()
df.Tm = df.Tm.astype("category")
df.Tm.cat.set_categories(sorter, inplace=True)

print(df.Tm)
Out[48]: 
0    CHH
1    VAN
2    TOT
3    OKC
4    DAL
Name: Tm, dtype: category
Categories (38, object): [TOT < ATL < BOS < BRK ... UTA < VAN < WAS < WSB]

df.sort_values(["Tm"])  ## 'sort' changed to 'sort_values'
Out[49]: 
   Age   G           Player   Tm  Year     id
2   22  60      Ratko Varda  TOT  2001  13950
0   27   6    Cedric Hunter  CHH  1991   2967
4   31  81  Adrian Caldwell  DAL  1997   6169
3   34  52       Ryan Bowen  OKC  2009   6141
1   25   7    Maurice Baker  VAN  2004   5335