# Pandas: Calculate the rolling weighted window mean of a DataFrame

Updated: February 23, 2024 By: Guest Contributor Post a comment

## Introduction

Working with time series data introduces specific statistical tools to efficiently analyze and transform the data, one of which is the rolling window operations. These operations are crucial when the data points are serially correlated. Particularly, in finance, economics, and weather forecasting, rolling window operations, such as weighted moving averages, are widely used for smoothing the data or generating trading signals.

This tutorial will guide you through the process of computing the rolling window weighted mean with the Pandas library in Python. By the end of this tutorial, you should be able to apply these techniques to your DataFrame and understand how to customize these for different analytical needs.

Prerequisite

Before diving into the calculations, ensure you have Pandas installed. If not, you can install it using pip:

``pip install pandas``

## Introduction to Pandas Rolling Window

Pandas provides robust methods for rolling window calculations, among them `.rolling()`, which sets the window and prepares the data for the operation. However, for weighted mean, we require an additional method: `.apply()`, with a lambda or predefined function to incorporate weights into our calculation.

## Basic Rolling Window Calculation

``````import pandas as pd
import numpy as np
# Sample Data
s = pd.Series([1, 2, 3, 4, 5])
# Simple rolling mean without weights
df_rolling = s.rolling(window=3).mean()
print(df_rolling)``````

The output,

``````0    NaN
1    NaN
2    2.0
3    3.0
4    4.0
dtype: float64``````

shows the simple moving average for a 3-day window. Since the first two data points do not have two preceding values, Pandas returns NaN values.

## Rolling Window with Weights

The concept of weighted means adds importance to certain values within your window, offering a more nuanced approach than the simple mean. In the case of a rolling weighted mean, weights are generally assigned so that more recent observations contribute more to the mean than older observations.

### Implementation Steps

1. Define your window size and weights.
2. Use the `.rolling()` with `.apply()` to implement the weighted mean calculation.

### Example

``````def weighted_mean(series, weights):
return np.average(series, weights=weights)

s = pd.Series([10, 20, 30, 40, 50])
window_size = 3
weights = np.array([0.5, 1, 1.5])  # More recent dates have higher weight

# Applying the weighted mean calculation
result = s.rolling(window=window_size).apply(lambda x: weighted_mean(x, weights), raw=True)
print(result)``````

The output,

``````0     NaN
1     NaN
2    28.0
3    38.0
4    48.0
dtype: float64``````

displays a weighted mean that increases over time, reflecting the increased weighting of more recent data. Itâ€™s crucial to ensure that the sum of your weights equals the window size or is normalized accordingly to reflect the distribution accurately.

## Advanced Scenarios

Rolling window calculations can be applied to DataFrame objects as well, enabling the examination of multiple time series or attributes simultaneously.

### Example with a DataFrame

``````df = pd.DataFrame({
'A': [1, 2, 3, 4, 5],
'B': [5, 4, 3, 2, 1]
})

# Assigning different weights to each column
weights_A = np.array([0.5, 1, 1.5])
weights_B = np.array([1, 1, 1])  # Equal weight for simplicity

# Function to apply different weights per column
def custom_weighted_mean(df, weights_dict):
results = {}
for column in df.columns:
weighted_means = df[column].rolling(window=3).apply(lambda x: np.average(x, weights=weights_dict[column]), raw=True)
results[column] = weighted_means
return pd.DataFrame(results)

weights_dict = {'A': weights_A, 'B': weights_B}
result_df = custom_weighted_mean(df, weights_dict)
print(result_df)``````

Such a tailored application allows for complex analysis across multiple columns with different weighting schemes, demonstrating the versatility of Pandas for rolling window operations.

## Handling Missing Data

Rolling window operations with Pandas handle missing data by default as NaN values. However, you might want to adjust this behavior depending on your analysis goals. Using parameters such as `min_periods` within `.rolling()` can control how the method deals with NaN values.

## Conclusion

Calculating the rolling weighted window mean with Pandas is an effective method to analyze time series data, offering insights into the dataâ€™s trends and patterns by assigning different importance to various points in the series. By mastering these techniques, you can unlock powerful data analysis capabilities for your projects, making your analyses more nuanced and impactful.

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