How to sort pandas dataframe by custom order on string index

Question

I have the following data frame:

import pandas as pd

# Create DataFrame
df = pd.DataFrame(
{\'id\':[2967, 5335, 13950, 6141, 6169],\\
 \'Player\': [\'Cedric         


        

Answer 1

Just reindex

df.reindex(reorderlist)
Out[89]: 
                 Age   G   Tm  Year     id
Player                                    
Maurice Baker     25   7  VAN  2004   5335
Adrian Caldwell   31  81  DAL  1997   6169
Ratko Varda       22  60  TOT  2001  13950
Ryan Bowen        34  52  OKC  2009   6141
Cedric Hunter     27   6  CHH  1991   2967

Answer 2

To get a custom sort-order on your list of strings, declare it as a categorical and manually specify that order in a sort:

player_order = pd.Categorical([ 'Maurice Baker', 'Adrian Caldwell','Ratko Varda' ,'Ryan Bowen' ,'Cedric Hunter'],
              ordered=True)

This is since pandas does not yet allow Categoricals as indices: df.set_index(keys=player_order, inplace=True) TypeError: unhashable type: 'Categorical'

So you'll want to do a manual custom sort using df.sort_index(level=player_order)

Answer 3

As of Pandas 1.1 DataFrame.sort_values has a key param that takes a callable to control sorting. So you could use an approach like the following:

def sorter(column):
    reorder = [
        "Maurice Baker",
        "Adrian Caldwell",
        "Ratko Varda",
        "Ryan Bowen",
        "Cedric Hunter",
    ]
    # This also works:
    # mapper = {name: order for order, name in enumerate(reorder)}
    # return column.map(mapper)
    cat = pd.Categorical(column, categories=reorder, ordered=True)
    return pd.Series(cat)

df_sorted = df.sort_values(by="Player", key=sorter)

There may be some practical differences between using pd.Categorical and the column.map alternative I put in the comments. For example, see these caveats. I'm showing both for completeness. I also haven't tested how this compares performance-wise to the current accepted solution that uses df.reindex. The best approach might be different when you have a MultiIndex in play too.