Any way to get mappings of a label encoder in Python pandas?

I am converting strings to categorical values in my dataset using the following piece of code.

data['weekday'] = pd.Categorical.from_array(data.weekday).labels 

For eg,

index    weekday
0        Sunday
1        Sunday
2        Wednesday
3        Monday
4        Monday
5        Thursday
6        Tuesday

After encoding the weekday, my dataset appears like this:

index    weekday
    0       3
    1       3
    2       6
    3       1
    4       1
    5       4
    6       5

Is there any way I can know that Sunday has been mapped to 3, Wednesday to 6 and so on?

You can create additional dictionary with mapping:

from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(data['name'])
le_name_mapping = dict(zip(le.classes_, le.transform(le.classes_)))
print(le_name_mapping)
{'Tom': 0, 'Nick': 1, 'Kate': 2}

The best way of doing this can be to use label encoder of sklearn library.

Something like this:

from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(["paris", "paris", "tokyo", "amsterdam"])
list(le.classes_)
le.transform(["tokyo", "tokyo", "paris"])
list(le.inverse_transform([2, 2, 1]))

A simple & elegant way to do the same.

cat_list = ['Sun', 'Sun', 'Wed', 'Mon', 'Mon']
encoded_data, mapping_index = pd.Series(cat_list).factorize()

and you are done, check below

print(encoded_data)
print(mapping_index)
print(mapping_index.get_loc("Mon"))