How to Normalise a Pandas DataFrame Column?

How to Normalise a Pandas DataFrame Column?

This recipe helps you Normalise a Pandas DataFrame Column
In [1]:
## How to Normalise a Pandas DataFrame Column
def Kickstarter_Example_96():
    print()
    print(format('How to Normalise a Pandas DataFrame Column','*^82'))

    import warnings
    warnings.filterwarnings("ignore")

    # load libraries
    import pandas as pd
    from sklearn import preprocessing

    # Create an example dataframe with a column of unnormalized data
    data = {'score': [234,24,14,27,-74,46,73,-18,59,160]}
    df = pd.DataFrame(data)
    print(); print(df)

    # Normalize The Column
    # Create x, where x the 'scores' column's values as floats
    x = df[['score']].values.astype(float)
    print(); print(x)

    # Create a minimum and maximum processor object
    min_max_scaler = preprocessing.MinMaxScaler()

    # Create an object to transform the data to fit minmax processor
    x_scaled = min_max_scaler.fit_transform(x)

    # Run the normalizer on the dataframe
    df_normalized = pd.DataFrame(x_scaled)

    # View the dataframe
    print(); print(df_normalized)

Kickstarter_Example_96()
********************How to Normalise a Pandas DataFrame Column********************

   score
0    234
1     24
2     14
3     27
4    -74
5     46
6     73
7    -18
8     59
9    160

[[234.]
 [ 24.]
 [ 14.]
 [ 27.]
 [-74.]
 [ 46.]
 [ 73.]
 [-18.]
 [ 59.]
 [160.]]

          0
0  1.000000
1  0.318182
2  0.285714
3  0.327922
4  0.000000
5  0.389610
6  0.477273
7  0.181818
8  0.431818
9  0.759740