Pandas DataFrame sort by categorical column but by specific class ordering

Question

I would like to select the top entries in a Pandas dataframe base on the entries of a specific column by using df_selected = df_targets.head(N).

Each

Answer 1

The method shown in my previous answer is now deprecated.

In stead it is best to use pandas.Categorical as shown here.

So:

list_ordering = ["Likely Supporter","GOTV","Persuasion","Persuasion+GOTV"]  
df["target"] = pd.Categorical(df["target"], categories=list_ordering) 

Answer 2

Thanks to jerzrael's input and references,

I like this sliced solution:

list_ordering = ["Likely Supporter","GOTV","Persuasion","Persuasion+GOTV"]  

df["target"] = df["target"].astype("category", categories=list_ordering, ordered=True)

Answer 3

I think you need Categorical with parameter ordered=True and then sorting by sort_values works very nice:

If check documentation of Categorical:

Ordered Categoricals can be sorted according to the custom order of the categories and can have a min and max value.

import pandas as pd

df = pd.DataFrame({'a': ['GOTV', 'Persuasion', 'Likely Supporter', 
                         'GOTV', 'Persuasion', 'Persuasion+GOTV']})

df.a = pd.Categorical(df.a, 
                      categories=["Likely Supporter","GOTV","Persuasion","Persuasion+GOTV"],
                      ordered=True)

print (df)
                  a
0              GOTV
1        Persuasion
2  Likely Supporter
3              GOTV
4        Persuasion
5   Persuasion+GOTV

print (df.a)
0                GOTV
1          Persuasion
2    Likely Supporter
3                GOTV
4          Persuasion
5     Persuasion+GOTV
Name: a, dtype: category
Categories (4, object): [Likely Supporter < GOTV < Persuasion < Persuasion+GOTV]

df.sort_values('a', inplace=True)
print (df)
                  a
2  Likely Supporter
0              GOTV
3              GOTV
1        Persuasion
4        Persuasion
5   Persuasion+GOTV