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

First, make a categorical series:

weekdays = pd.Series(['Sun', 'Sun', 'Wed', 'Mon', 'Mon']).astype('category')

Then, inspect its “categories”:

weekdays.cat.categories.get_loc('Sun')

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}

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 ...
})

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"))

If you have numerical and categorical both type of data in dataframe
You can use : here X is my dataframe having categorical and numerical both variables

from sklearn import preprocessing
le = preprocessing.LabelEncoder()

for i in range(0,X.shape[1]):
    if X.dtypes[i]=='object':
        X[X.columns[i]] = le.fit_transform(X[X.columns[i]])

Or you can try this:

from sklearn.preprocessing import LabelEncoder

le = LabelEncoder()
data = data.apply(le.fit_transform)

Note: This technique is good if you are not interested in converting them back.

train['cat'] = train['cat'].map(list(train['cat'].value_counts().to_frame().reset_index().reset_index().set_index('index').to_dict().values())[0])

Its very simple, they have a built-in function for this.

from sklearn import preprocessing
le = preprocessing.LabelEncoder()
..
# your model steps and when you have results
..

prediction_decoded = le.inverse_transform(prediction_encoded)
print(prediction_decoded)

I am adding my answer even after lot of answers are there to answer this OP’ed question specifically as:

If have you already label encoded your values as:

from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit('column-or-list-of-values')

You can get back the mapping of value to integer codes as:

dict(zip(le.classes_,range(len(le.classes_))))

You can create another column with the indexed values after converting from categorical to numeric. Think of it as primary key column.

#new column,index contains converted values of column weekday
data[‘index’] = pd.Categorical.from_array(data.weekday).labels

#view the corresponding values
df.groupby([‘index’,’weekday’]).first()