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

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}

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"))

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]))

There are many ways of doing this. You can consider pd.factorize, sklearn.preprocessing.LabelEncoder etc. However, in this specific case, you have two options which will suit you best:

Going by your own method, you can add the categories:

pd.Categorical( df.weekday, [ 
    'Sunday', 'Monday', 'Tuesday', 
    'Wednesday', 'Thursday', 'Friday', 
    'Saturday']  ).labels

The other option is to map values directly using a dict

df.weekday.map({
    'Sunday': 0,
    'Monday': 1,
     # ... and so on. You get the idea ...
})