How to Calculate Rolling Correlation in Python? (original) (raw)

Last Updated : 29 Jan, 2022

Correlation generally determines the relationship between two variables. The rolling correlation measure the correlation between two-time series data on a rolling window Rolling correlation can be applied to a specific window width to determine short-term correlations.

Calculating Rolling Correlation in Python

Let's use sales data of two products A and B in the last 60 months to calculate the rolling correlation. Pandas package provides a function called rolling.corr() to calculate the rolling correlation.

Syntax:

data1.rolling(width).corr(data2)

Where,

• data1, data2 - data/column of interest (type series)
• width - Rolling window width (int)

Note: The width of the rolling window should be 3 or greater in order to calculate correlations.

Data Used:

Python3 `

import pandas module

import pandas as pd

read the data

data = pd.read_csv('product_sales.csv')

display top 10 rows

print(data.head(10))

display column names

print(data.columns)

`

Output:

Example 2:

Here, we used the window width of 6, which shows the successive 6 months rolling correlation. We could see a significant correlation between two products sales any sudden dip or rise in correlation signals an unusual event, that caused the dip.

Python3 `

data['Product A'].rolling(6).corr(data['Product B'])

formatting the output

k = 1 for i, j in enumerate(data['Product A'].rolling(6).corr(data['Product B'])): if (i >= 5 and i < 12): print(f'The correlation in sales during months
{k} through {i+1} is {j}') i = 0 k += 1

`

Output:

Now's let us try the same for 3-month correlation as shown below,

Example 3:

Python3 `

data['Product A'].rolling(3).corr(data['Product B'])

formatting the output

k = 1 for i, j in enumerate(data['Product A'].rolling(3).corr(data['Product B'])): if (i >= 3 and i < 12): print( f'The correlation in sales during months {k}
through {i+1} is {j}') i = 0 k += 1

`

Output: