How to process categorical features in Python?

This recipe helps you process categorical features in Python
In [1]:
## How to process categorical features in Python 
def Kickstarter_Example_38():
    print()
    print(format('How to process categorical features in Python', '*^82'))

    import warnings
    warnings.filterwarnings("ignore")

    # load libraries
    from sklearn import preprocessing

    #from sklearn.pipeline import Pipeline
    import pandas as pd

    # Create Data
    raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'],
                'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'],
                'age': [42, 52, 36, 24, 73],
                'city': ['San Francisco', 'Baltimore', 'Miami', 'Douglas', 'Boston']}
    df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'city'])
    print(); print(df)

    # Convert Nominal Categorical Feature Into Dummy Variables Using Pandas
    # Create dummy variables for every unique category in df.city
    print(); print(pd.get_dummies(df["city"]))

    # Convert Nominal Categorical Data Into Dummy (OneHot) Features Using Scikit
    # Convert strings categorical names to integers
    integerized_data = preprocessing.LabelEncoder().fit_transform(df["city"])

    # View data
    print(); print(integerized_data)

    # Convert integer categorical representations to OneHot encodings
    output = preprocessing.OneHotEncoder().fit_transform(integerized_data.reshape(-1,1)).toarray()
    print(); print(output)

Kickstarter_Example_38()
******************How to process categorical features in Python*******************

  first_name last_name  age           city
0      Jason    Miller   42  San Francisco
1      Molly  Jacobson   52      Baltimore
2       Tina       Ali   36          Miami
3       Jake    Milner   24        Douglas
4        Amy     Cooze   73         Boston

   Baltimore  Boston  Douglas  Miami  San Francisco
0          0       0        0      0              1
1          1       0        0      0              0
2          0       0        0      1              0
3          0       0        1      0              0
4          0       1        0      0              0

[4 0 3 2 1]

[[0. 0. 0. 0. 1.]
 [1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0.]
 [0. 1. 0. 0. 0.]]