Pandas Rolling Window Over Irregular Series with Float Index

In this article, we will explore how to perform a rolling window operation on an irregular series with a float index. The series in question has observations that are not perfectly equally spaced, which makes it challenging to work with traditional rolling window functions.

We will first delve into the limitations of using the rolling method for this purpose and then discuss a manual approach that involves creating a new column to store the neighboring indices.

Limitations of Rolling Method

The rolling method in pandas is designed to work with datetime indexes, which are inherently evenly spaced. When working with float indexes, the rolling method does not guarantee equal spacing between observations, making it difficult to use for this purpose.

In fact, the author of the original Stack Overflow question concludes that the rolling method cannot be used to achieve the desired result without converting the index to datetime and back again.

Manual Approach

Fortunately, there is a manual approach that can be used to create a rolling window over an irregular series with a float index. This approach involves creating a new column to store the neighboring indices and then calculating the mean of these neighbors using a triangular weighting function.

Triangular Weighting Function

The triangular weighting function is used to assign weights to each neighbor based on its distance from the current observation. The function triang from scipy’s signal module returns an array of coefficients that represent the triangular weighting pattern.

from scipy.signal.windows import triang
import numpy as np
import pandas as pd

def triangular(a):
    n = a.size
    b = triang(n) / (n - 1)
    return b @ a

Creating Neighboring Indices Column

To create the neighboring indices column, we need to iterate over each index in the original series and find all the neighboring indices that fall within a certain distance of the current index.

df = pd.DataFrame({'S': S})
df['neighbours'] = df.index.to_series().apply(lambda x: [df.loc[index][0] for index in df.index if x - 0.15 < index < x + 0.15])

In this code, we use the to_series method to convert the index to a series and then apply a lambda function to each index. The lambda function uses list comprehension to find all the neighboring indices that fall within a distance of 0.15 from the current index.

Calculating Rolling Mean

Once we have created the neighboring indices column, we can calculate the rolling mean using the triangular weighting function.

df['rolling_mean'] = df.neighbours.apply(lambda x: triangular(np.array(x)))

In this code, we use the apply method to apply a lambda function to each row in the neighboring indices column. The lambda function uses the triangular weighting function to calculate the mean of the neighbors.

Dropping Neighboring Indices Column

Finally, we drop the neighboring indices column as it is no longer needed.

df.drop('neighbours', axis=1, inplace=True)

Conclusion

In conclusion, while the rolling method cannot be used to create a rolling window over an irregular series with a float index, there is a manual approach that can be used to achieve this result. By creating a new column to store the neighboring indices and using a triangular weighting function, we can calculate the rolling mean of the neighbors.

This approach may not be as straightforward as using the rolling method, but it provides a flexible way to work with irregular series with float indexes.

Additional Considerations

One additional consideration when working with irregular series is to ensure that the window size is consistent across all observations. If the window size varies, the rolling mean will also vary, which may not be desirable in certain applications.

To address this issue, you can use a fixed window size and pad the edges of the series with NaN values if necessary. This ensures that all observations are treated consistently when calculating the rolling mean.

window_size = 10

df['rolling_mean'] = df.neighbours.apply(lambda x: triangular(np.array(x)))
df.loc[~df.index.isin(range(window_size, len(df) - window_size + 1)), 'rolling_mean'] = np.nan

In this code, we use a fixed window size of 10 and pad the edges of the series with NaN values if necessary. This ensures that all observations are treated consistently when calculating the rolling mean.

By using a consistent window size and handling edge cases appropriately, you can create a reliable rolling window over an irregular series with a float index.

Last modified on 2024-06-21