How to deal with Rolling Time Window in Python?

This recipe helps you deal with Rolling Time Window in Python
In [1]:
## How to deal with Rolling Tine Window in Python 
def Kickstarter_Example_47():
    print()
    print(format('How to deal with Rolling Time Window in Python','*^82'))
    import warnings
    warnings.filterwarnings("ignore")

    # Load library
    import pandas as pd

    # Create datetimes
    time_index = pd.date_range('01/01/2010', periods=5, freq='M')

    # Create data frame, set index
    df = pd.DataFrame(index=time_index)

    # Create feature
    df['Stock_Price'] = [1,2,3,4,5]
    print(); print(df)

    # Create A Rolling Time Window Of Two Rows
    # Calculate rolling mean
    df1 = df.rolling(window=2).mean()
    print(); print(df1)

    # Identify max value in rolling time window
    df2 = df.rolling(window=2).max()
    print(); print(df2)

Kickstarter_Example_47()
******************How to deal with Rolling Time Window in Python******************

            Stock_Price
2010-01-31            1
2010-02-28            2
2010-03-31            3
2010-04-30            4
2010-05-31            5

            Stock_Price
2010-01-31          NaN
2010-02-28          1.5
2010-03-31          2.5
2010-04-30          3.5
2010-05-31          4.5

            Stock_Price
2010-01-31          NaN
2010-02-28          2.0
2010-03-31          3.0
2010-04-30          4.0
2010-05-31          5.0