Rolling Window Calculations On A Pandas DataFrame

Overview:

Rolling window calculations involve taking subsets of data, where subsets are of the same or varying length and performing mathematical calculations on them.
The pandas Rolling class supports rolling window calculations on Series and DataFrame classes.
Rolling class has the popular math functions like sum(), mean() and other related functions implemented. Through the apply() method custom math operations can be performed on a rolling window.

Example:

# Example Python program that performs rolling
# window calculations on a pandas DataFrame

import pandas as pds

metrics = {"Pressure":(90, 92, 91, 95, 94),
"FlowRate":(35, 36, 36, 38, 37)};

df = pds.DataFrame(data = metrics,
index = ("Sensor 1", "Sensor 2", "Sensor 3",
"Sensor 4", "Sensor 5"));

# Sum every two metrics
print(df.rolling(window=2).sum());

Output:

          Pressure FlowRate
Sensor 1       NaN       NaN
Sensor 2     182.0      71.0
Sensor 3     183.0      72.0
Sensor 4     186.0      74.0
Sensor 5     189.0      75.0

Rolling Window Calculations On A Pandas DataFrame

Overview:

Example:

# Sum every two metrics print(df.rolling(window=2).sum());

Output:

# Sum every two metrics
print(df.rolling(window=2).sum());