Sliding Window Iterator using Rolling in Pandas

In this article, we’ll explore how to create a sliding window iterator using the rolling function in pandas. We’ll begin by understanding what a sliding window is and why it’s useful. Then, we’ll dive into the code and explain each step.

What is a Sliding Window?

A sliding window is an algorithmic technique used to solve problems that involve scanning a data structure or array from left to right and right to left, moving a fixed-size window over the data as you scan. The size of the window can be constant or variable.

In our case, we want to create an iterator that yields subsets of rows from a pandas DataFrame. Each subset should be defined by a range of row indices.

Introduction to Pandas Rolling

Pandas provides a rolling function that allows us to apply a custom function to each window of rows in a DataFrame. We can use this function to achieve our sliding window iterator.

import pandas as pd
import numpy as np

# Create a sample DataFrame
a = np.zeros((100,40))
X = pd.DataFrame(a)

for index, row in X.iterrows():
    print(index)
    print(row)

This code creates a sample DataFrame X with 100 rows and 40 columns. It then iterates over each row of the DataFrame using the iterrows() method.

Creating a Sliding Window Iterator

Now, let’s create an iterator that yields subsets of rows from our DataFrame.

def rolled(df, n):
    k = range(df.columns.nlevels)
    _k = [i - len(k) for i in k]
    myroll = pd.concat([df.shift(i).stack(level=k) for i in range(n)],
                       axis=1, keys=range(n)).unstack(level=_k)
    return [(i, row.unstack(0)) for i, row in myroll.iterrows()]

This function rolled takes two arguments: the DataFrame df and an integer n. It creates a rolling window of size n by shifting rows from the original DataFrame.

Here’s what’s happening inside the function:

1. We create an array k that represents the number of levels in the MultiIndex column.
2. We create another array _k that is offset by one level to compensate for the shift operation.
3. We concatenate the shifted rows using pd.concat() with the axis=1 argument, which stacks the arrays horizontally.
4. We unstack the resulting DataFrame using unstack(level=_k), which returns a DataFrame with each column from the original DataFrame as separate columns.

Using the Iterator

Now that we have our iterator function, let’s use it to print out subsets of rows from our sample DataFrame.

df = generic_portfolio_df('2014-12-31', '2015-05-30', 'BM', 3, 5)

for i, roll in rolled(df.head(5), 3):
    print(roll)
    print()

This code creates a new DataFrame df using the generic_portfolio_df() function. Then, it calls our iterator function rolled() with the first five rows of the DataFrame and a window size of three.

The resulting iterator yields subsets of rows from the original DataFrame. Each subset has an index indicating its position in the roll.

Understanding the Output

Let’s examine the output of the iterator to understand what’s happening.

The first call to rolled() with a window size of three returns:

(0, Series( 0.326164, 0.201597, 0.085340))

This indicates that we’ve started at row index 0 and rolled over the first three rows of the DataFrame.

The second call to rolled() with a window size of three returns:

(1, Series( 0.278614, 0.314448, NaN))

We’ve now moved to the next three rows and rolled over the original index.

And so on…

Conclusion

In this article, we created an iterator that yields subsets of rows from a pandas DataFrame using the rolling function. We explained each step of the process, including how to create a rolling window and how to unstack the resulting DataFrame.

This technique is useful for solving problems that involve scanning data structures or arrays with a fixed-size window. It’s also applicable to other libraries and frameworks, such as NumPy or SciPy.

With this iterator, you can easily iterate over subsets of rows in a DataFrame while maintaining control over the window size.

Last modified on 2024-06-30