python - Get mapping of categorical variables in pandas

Question

I'm doing this to make categorical variables numbers

>>> df = pd.DataFrame({'x':['good', 'bad', 'good', 'great']}, dtype='category')

       x
0   good
1    bad
2   good
3  great

How can I get the mapping between the original values and the new values?

Answer 1

Method 1

You can create a dictionary mapping by enumerating (similar to creating a dictionary from a list by creating dictionary keys from the list indices):

dict( enumerate(df['x'].cat.categories ) )

# {0: 'bad', 1: 'good', 2: 'great'}

Method 2

Alternatively, you could map the values and codes in every row:

dict( zip( df['x'].cat.codes, df['x'] ) )

# {0: 'bad', 1: 'good', 2: 'great'}

It's a little more transparent what is happening here, and arguably safer for that reason. It is also much less efficient as the length of the arguments to zip() is len(df) whereas the length of df['x'].cat.categories is only the count of unique values and generally much shorter than len(df).

Additional Discussion

The reason Method 1 works is that the categories have type Index:

type( df['x'].cat.categories )

# pandas.core.indexes.base.Index

and in this case you look up values in an index just as you would a list.

There are a couple of ways to verify that Method 1 works. First, you can just check that a round trip retains the correct values:

(df['x'] == df['x'].cat.codes.map( dict( 
            enumerate(df['x'].cat.categories) ) ).astype('category')).all()
# True

or you can check that Method 1 and Method 2 give the same answer:

(dict( enumerate(df['x'].cat.categories ) ) == dict( zip( df['x'].cat.codes, df['x'] ) ))

# True