How to deal with Rolling Time Window in Python?
DATA MUNGING

How to deal with Rolling Time Window in Python?

How to deal with Rolling Time Window in Python?

This recipe helps you deal with Rolling Time Window in Python

0
This python source code does the following : 1. Creates your own time series data. 2. Adding new columns to datagram 3. Finds mean and max for rolling window
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

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