How to convert string categorical variables into numerical variables using Label Encoder?

This recipe helps you convert string categorical variables into numerical variables using Label Encoder
In [1]:
## How to convert string categorical variables into numerical variables using Label Encoder
def Kickstarter_Example_78():
    print()
    print(format('How to convert strings into numerical variables using Label Encoder','*^82'))

    import warnings
    warnings.filterwarnings("ignore")

    # load libraries
    import pandas as pd
    from sklearn.preprocessing import LabelEncoder

    # Create dataframe
    raw_data = {'patient': [1, 1, 1, 2, 2],
                'obs': [1, 2, 3, 1, 2],
                'treatment': [0, 1, 0, 1, 0],
                'score': ['strong', 'weak', 'normal', 'weak', 'strong']}

    df = pd.DataFrame(raw_data, columns = ['patient', 'obs', 'treatment', 'score'])
    print(); print(df)

    # Create a function that converts all values of df['score'] into numbers
    def dummyEncode(df):
          columnsToEncode = list(df.select_dtypes(include=['category','object']))
          le = LabelEncoder()
          for feature in columnsToEncode:
              try:
                  df[feature] = le.fit_transform(df[feature])
              except:
                  print('Error encoding '+feature)
          return df
    df = dummyEncode(df)

    print(); print(df)

Kickstarter_Example_78()
*******How to convert strings into numerical variables using Label Encoder********

   patient  obs  treatment   score
0        1    1          0  strong
1        1    2          1    weak
2        1    3          0  normal
3        2    1          1    weak
4        2    2          0  strong

   patient  obs  treatment  score
0        1    1          0      1
1        1    2          1      2
2        1    3          0      0
3        2    1          1      2
4        2    2          0      1