df = pd.DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})

# Convert the raw grades to a categorical data type.
df["grade"] = df["raw_grade"].astype("category")

df["grade"]
0  a
1  b
2  b
3  a
4  a
5  e
Name: grade, dtype: category 
Categories (3, object): [a, b, e]

# df["grade"].cat.categories


# Rename the categories to more meaningful names (assigning to Series.cat.categories is inplace!)
df["grade"].cat.categories = ["very good", "good", "very bad"]
df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"])

df["grade"]:
0 very good
1      good
2      good
3 very good
4 very good
5 very bad

## Sorting is per order in the categories, not lexical order.
df.sort_values(by="grade")
Out[133]:
   id  raw_grade  grade
5   6          e  very bad
1   2          b  good  
2   3          b  good
0   1          a  good
3   4          a  very good
4   5          a  very good

## Grouping by a categorical column shows also empty categories.
df.groupby("grade").size()
grade
very bad     1
bad          0
medium       0
good         2
very good    3