How to sort a pandas dataframe by a custom order on a string index

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.

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)

If there are more than one columns that need to be sort, in my experience, I use map to convert string value to number. Then use sort_values:

# Step 1/3: create dictionary to convert any string to number
convert_dict = {'Maurice Baker':1,
                'Adrian Caldwell':2,
                'Ratko Varda':3} # You can start filling till the end

# Step 2/3: Create column `new` that mapping from `Player`:
df['new'] = df['Player'].map(convert_dict)

# Step 3/3: sort
df.sort_values(by=['new'], ignore_index=True, inplace=True)
df.drop(columns=['new'], inplace=True)

To sort in arbirtary order while not including blank rows I found df.filter to work while testing out BENYS answer . It sorts as desired, ignores missing keys like df.reindex, but helpfully does not include empty rows for keys that have no data.

df.filter(reorderlist, axis=0)

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

#Extra keys dont add empty rows, missing keys ignored
reorderlist.append('LeBron James')
reorderlist.remove('Adrian Caldwell')
df.filter(reorderlist, axis=0)

                  id  Year  Age   Tm   G
Player                                  
Maurice Baker   5335  2004   25  VAN   7
Ratko Varda    13950  2001   22  TOT  60
Ryan Bowen      6141  2009   34  OKC  52
Cedric Hunter   2967  1991   27  CHH   6

Simplest way I've found is to just pass the list to .loc, but this won't work if the index is not unique.

df = df.loc[reorderlist, :]