Rolling Sum Calculation in Python using Pandas and Timedelta

The problem at hand involves calculating the rolling sum of a column in a pandas DataFrame, with some conditions applied to it. In this case, we want to calculate the rolling sum based on minutes in the dateTime column, while ignoring changes in the minute value.

Background

To approach this problem, we first need to understand how the cumsum() function works in pandas, as well as how the Timedelta class can be used to represent time intervals. We also need to be familiar with how to use these functions together to achieve our desired outcome.

Solution

The solution to this problem lies in using the shift() and eq() functions to create a mask for when to reset the rolling sum, based on changes in the minute value.

Here is an example of how we can implement this solution:

import pandas as pd
import numpy as np

# Create a sample DataFrame
df = pd.DataFrame({
    'dateTime': pd.date_range('2017-09-19 02:00:00', periods=32, freq='2min'),
    'minute': np.random.randint(0, 60, size=32),
    'X': np.random.randint(1, 100, size=32)
})

# Calculate the rolling sum without resetting it
df['CumX'] = df['X'].cumsum()

# Create a mask for when to reset the rolling sum
s = df['dateTime'].dt.floor('T').diff().shift(-1).eq(pd.Timedelta('1 minute'))

# Apply the mask to create the final result
df['Rolling_X'] = s.mul(df['CumX']).diff().where(lambda x: x < 0).ffill().add(s, fill_value=0)

print(df)

This code creates a sample DataFrame with dateTime and minute columns, as well as an X column. It then calculates the rolling sum of X using the cumsum() function. Next, it creates a mask for when to reset the rolling sum based on changes in the minute value. Finally, it applies this mask to create the final result.

Explanation

The key to this solution lies in understanding how the shift() and eq() functions work together to create a mask for when to reset the rolling sum. Here’s a step-by-step explanation of what happens:

1. df['dateTime'].dt.floor('T').diff(): This line calculates the difference between consecutive minutes.
2. .shift(-1): This line shifts the result one time unit back, effectively aligning it with the previous minute value.
3. .eq(pd.Timedelta('1 minute')): This line checks if the difference is equal to a timedelta of 1 minute (i.e., when there’s been a change in minutes).
4. mul(df['CumX']): This line multiplies the mask by the current rolling sum, effectively applying it to the result.
5. .diff(): This line calculates the difference between the current and previous results, effectively resetting the rolling sum for each time unit.

By combining these steps, we’re able to create a mask that allows us to reset the rolling sum when there’s been a change in minutes, while still applying the sum for all other cases.

Example Use Cases

This solution can be used in a variety of scenarios where you need to calculate a rolling sum with some conditions applied. Here are a few examples:

• Calculating the average temperature over the past hour, but resetting the calculation when the temperature changes by more than 5 degrees.
• Tracking the total value of sales over the past day, but resetting the calculation when the sale amount changes by more than $10.
• Analyzing stock prices over the past week, but resetting the calculation when the price changes by more than 1%.

In each case, we can use the same basic approach to create a mask for when to reset the rolling sum, and then apply it to the result.

Last modified on 2024-01-22