How does LabelEncoder() encode values?

Question

I want to know how does LabelEncoder() function. This is a part of my code

for att in all_features_test:
if (str(test_home_data[att].dtypes) == 'object'):
    test_home_data[att].fillna( 'Nothing', inplace = True)
    train_home_data[att].fillna( 'Nothing', inplace = True)

    train_home_data[att] = LabelEncoder().fit_transform(train_home_data[att])
    test_home_data[att] = LabelEncoder().fit_transform(test_home_data[att])
else:
    test_home_data[att].fillna( 0, inplace = True)
    train_home_data[att].fillna( 0, inplace = True)

Both train and test data set has an attribute 'Condition' which can hold values - Bad, Average and Good

Lets say LabelEncoder() would encode Bad as 0, Average as 2, and Good as 1 in train_home_data. Now would that be same for test_home data?

If not, then what should I do?

Answer 1

I got the answer for this I guess.

Code

data1 = [('A', 1), ('B', 2),('C', 3) ,('D', 4)]
data2 = [('D', 1), ('A', 2),('A', 3) ,('B', 4)]

df1 = pd.DataFrame(data1, columns = ['col1', 'col2'])
df2 = pd.DataFrame(data2, columns = ['col1', 'col2'])

print(df1['col1'])
print(df2['col1'])

df1['col1'] = LabelEncoder().fit_transform(df1['col1'])
df2['col1'] = LabelEncoder().fit_transform(df2['col1'])

print(df1['col1'])
print(df2['col1'])

Output

0    A
1    B
2    C
3    D
Name: col1, dtype: object # df1
0    D
1    A
2    A
3    B
Name: col1, dtype: object # df2
0    0
1    1
2    2
3    3
Name: col1, dtype: int64 #df1 encoded
0    2
1    0
2    0
3    1
Name: col1, dtype: int64 #df2 encoded

B of df1 is encoded to 1.

and,

B of df2 is encoded to 1 as well

So if I encode training and testing data sets, then the encoded values in training set would reflect in testing data set (only if both are label encoded)