Pandas DataFrame: Calculate the rolling weighted window standard deviation

Updated: February 21, 2024 By: Guest Contributor Post a comment


In data analysis, understanding trends and patterns is vital. One way to analyze these trends is by calculating the standard deviation over a rolling window, which can reveal the variability of a dataset within that window. However, to give more importance to certain data points, a weighted standard deviation can be employed. This tutorial will guide you through calculating the rolling weighted window standard deviation in a Pandas DataFrame, starting from the basics and moving towards more advanced techniques.

The rolling weighted window standard deviation integrates the importance of different data points based on their weights, offering a nuanced view of data variability over time. We’ll explore this using Python’s Pandas library, a powerhouse for data manipulation and analysis.

Getting Started

First, ensure you have Pandas installed in your environment:

pip install pandas

Next, import Pandas and create a simple DataFrame to work with:

import pandas as pd
import numpy as np
# Sample DataFrame
data = {'value': [10, 20, 30, 40, 50]}
df = pd.DataFrame(data)

The output will be a simple table with one column:

0     10
1     20
2     30
3     40
4     50

Basic Rolling Standard Deviation

Before diving into weighted calculations, let’s understand the basic rolling standard deviation:

df['rolling_std'] = df['value'].rolling(window=3).std()

This will output:

   value  rolling_std
0     10          NaN
1     20          NaN
2     30     10.000000
3     40     10.000000
4     50     10.000000

As seen, the rolling standard deviation over a 3-row window provides insights into data variability.

Calculating Weighted Rolling Standard Deviation

To calculate the weighted rolling standard deviation, we need to incorporate weights. Pandas doesn’t have a built-in method for this, but we can achieve it through a custom function:

def weighted_rolling_std(values, weights):
    weighted_mean = np.sum(weights * values) / np.sum(weights)
    variance = np.sum(weights * (values - weighted_mean)**2) / np.sum(weights)
    return np.sqrt(variance)

# Example usage:
window_size = 3
weights = np.array([0.5, 1, 1.5])
df['weighted_rolling_std'] = df['value'].rolling(window=window_size).apply(lambda x: weighted_rolling_std(x, weights), raw=True)

This code snippet calculates the weighted rolling standard deviation over a 3-row window. The output demonstrates how incorporating weights modifies the standard deviation:

   value  rolling_std  weighted_rolling_std
0     10          NaN                  NaN
1     20          NaN                  NaN
2     30     10.000000           12.909944
3     40     10.000000           12.909944
4     50     10.000000           12.909944

Time-Weighted Rolling Window

In many cases, your DataFrame’s index may be datetime values, and you might want to weight the entries by time. Here’s how to perform a time-weighted rolling standard deviation:

df['date'] = pd.date_range(start='1/1/2022', periods=len(df), freq='D')
df.set_index('date', inplace=True)
# Assuming equal weights for simplicity. You can modify as needed.
df['time_weighted_roll_std'] = df['value'].rolling('3D').std()

This utilizes Pandas’ capability to handle rolling windows based on time, perfectly suited for time series analysis.

Advanced: Custom Weight Functions

For more sophisticated analyses, you may want to define custom weight functions based on your specific criteria. Whether adjusting for volatility, market sentiment, or other factors, the flexibility to define your weighting scheme enables nuanced analysis beyond standard metrics.

As an extension, you can leverage the `apply` function along with your custom weighting function to perform complex rolling calculations. Employing the power of NumPy within these calculations ensures efficient data processing.


Calculating the rolling weighted window standard deviation provides deeper insights into your data’s variability, allowing for more refined analysis. Starting with basic techniques and advancing towards custom solutions, this tutorial has equipped you with the knowledge to apply these methods within your data analysis workflows, unlocking new dimensions of data interpretation.