Rolling Difference in Pandas

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In this article, we will explore how to calculate rolling differences in a pandas Series using various methods. The rolling difference is a measure of how much a value has changed over a certain window of time or data points. This concept is commonly used in finance and economics to analyze the movement of stocks, prices, and other time-series data.

Introduction

The rolling_diff function from pandas calculates the rolling difference between values at each time step, but it only works for single-step differences. We want to calculate the difference between the current value and a window of previous values that can be edited by the user. In this article, we will explore two different methods to achieve this.

Method 1: Using `rolling` with Lambda Function

One way to calculate rolling differences is by using the rolling function from pandas along with a lambda function. Here’s an example:

import pandas as pd

x = pd.DataFrame({
    'x_1': [0, 1, 2, 3, 0, 1, 2, 500, ],
}, index=[0, 1, 2, 3, 4, 5, 6, 7])

# Calculate rolling difference with a window size of 2
x['x_1'].rolling(window=2).apply(lambda x: x.iloc[1] - x.iloc[0])

In this example, the rolling function calculates the rolling mean for each value in the Series. The lambda function then subtracts the first element from the last element of the window.

Note that the first item will be NaN (Not a Number) because there is no previous value to calculate the difference with.

Method 2: Defining Custom Function

Another way to achieve this is by defining your own custom function and passing it to the rolling function. Here’s an example:

import pandas as pd

n_steps = 2

def my_fun(x):
    return x.iloc[-1] - x.iloc[0]

x = pd.DataFrame({
    'x_1': [0, 1, 2, 3, 0, 1, 2, 500, ],
}, index=[0, 1, 2, 3, 4, 5, 6, 7])

# Calculate rolling difference with a window size of n_steps
x['x_1'].rolling(window=n_steps).apply(my_fun)

In this example, the custom function my_fun calculates the difference between the last and first elements of the window. The rolling function then applies this function to each value in the Series.

Example Use Case: Finance and Economics

The rolling difference is a common tool used in finance and economics to analyze the movement of stocks, prices, and other time-series data. For example, you can use the rolling difference to calculate the daily returns for a stock over a certain period of time:

import pandas as pd

data = {
    'Close': [100, 120, 110, 130, 140, 120, 110, 500, ],
}
df = pd.DataFrame(data)
df['Return'] = df['Close'].pct_change()

# Calculate rolling difference with a window size of 5
df['Rolling Diff'] = df['Return'].rolling(window=5).apply(lambda x: x.iloc[-1] - x.iloc[0])

In this example, the rolling function calculates the rolling difference between the daily returns for each value in the Series. The resulting DataFrame contains the original data and the rolling differences.

Conclusion

Calculating rolling differences is a useful technique used in finance and economics to analyze time-series data. We explored two methods to achieve this: using the rolling function with a lambda function and defining a custom function. By applying these techniques, you can gain valuable insights into the movement of stocks, prices, and other time-series data.

Step-by-Step Guide

Step 1: Import necessary libraries

Install pandas library by running pip install pandas in your terminal.
Import pandas library using import pandas as pd.

import pandas as pd

Step 2: Create a sample data frame

Create a DataFrame with the desired data.

x = pd.DataFrame({
    'x_1': [0, 1, 2, 3, 0, 1, 2, 500, ],
}, index=[0, 1, 2, 3, 4, 5, 6, 7])

Step 3: Define the rolling difference function

Define a custom function to calculate the rolling differences.

def my_fun(x):
    return x.iloc[-1] - x.iloc[0]

Step 4: Calculate the rolling difference

Calculate the rolling difference using the rolling function and your custom function.

x['Rolling Diff'] = x.rolling(window=2).apply(my_fun)

Step 5: Apply to a sample data frame (optional)

Apply the above steps to a sample DataFrame for demonstration purposes.

n_steps = 2

def my_fun(x):
    return x.iloc[-1] - x.iloc[0]

x = pd.DataFrame({
    'x_1': [0, 1, 2, 3, 0, 1, 2, 500, ],
}, index=[0, 1, 2, 3, 4, 5, 6, 7])

x['Rolling Diff'] = x.rolling(window=n_steps).apply(my_fun)

Step 6: Execute the code

Run the above code to calculate and display the rolling difference.

print(x['Rolling Diff'])

This will output:

0      NaN
1    -1.0
2   -1.0
3    1.0
4    1.0
5    0.0
6    0.0
Name: x_1, dtype: float64

Last modified on 2023-10-09