Understanding Rolling Sum in Pandas: A Deep Dive into Window Functions

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As a data analyst or scientist working with pandas, you’re likely familiar with the concept of window functions. These functions allow you to perform calculations on groups of rows that are related by some condition, such as aggregating values based on a time period or grouping rows by a specific column. In this article, we’ll delve into the specifics of using rolling sum in pandas and explore why it might not be working correctly.

Introduction to Window Functions

Window functions in pandas allow you to apply an aggregation function to groups of rows that are related by some condition. Unlike aggregate functions like mean() or sum(), which operate on entire dataframes, window functions calculate the result based on a specific range of rows.

The most commonly used window functions in pandas include:

row_number(): assigns a unique row number to each group
rank(): ranks rows within each group based on an aggregation function
lag() and lead(): access values from previous or next rows within the same group
sum(), mean(), max(), and min(): apply the specified aggregation function to groups

Rolling Sum: A Window Function for Calculating Sums Over Time or Rows

rolling() is a window function in pandas that allows you to calculate sums over time or rows. It returns an iterator over aggregates of the input DataFrame, applying the rolling operation across each row.

The general syntax for rolling() is as follows:

df['column_name'].rolling(window_size, closed='both') \
    .agg(function)(input_df)

window_size: specifies the size of the window. It can be a string or an integer representing days (e.g., '365d', 360), hours (e.g., '1h', 3600)), or even minutes (e.g., '5m', 300) in pandas version 0.25 and later.
closed: specifies the edge behavior for the window.

Common values include:

left: right edge is included in calculations
right: left edge is included in calculations
both: both edges are included (i.e., start and end dates) if window_size includes a unit (like 'd', 'h', etc.)

When working with the rolling function, keep in mind that there’s a difference between time-based and row-based windows. Time-based windows operate across consecutive rows of the input DataFrame, while row-based windows compute sums within each group defined by a condition.

Implementing Rolling Sum for Calculating Sums Over Time

Let’s look at an example where we want to calculate the rolling sum over time:

import pandas as pd

# Sample DataFrame with timestamps and amount data
data = {
    'customer': ['A', 'B', 'C', 'D'],
    'trade_created_at': [pd.Timestamp('2022-01-01'), pd.Timestamp('2022-01-15'), 
                         pd.Timestamp('2022-02-01'), pd.Timestamp('2022-03-01')],
    'amount_for_window_calc': [100, 200, 300, 400]
}

large_basket = pd.DataFrame(data)

# Function to calculate rolling sum
def sum_of_last_n_days(df: pd.DataFrame, identifier: str, timestamp: str, n: int) -> pd.DataFrame:

    col_name = f"sum_{identifier}"
    temp_df = df.set_index(timestamp)
    temp_df[identifier] = temp_df[identifier].shift(n - 1)
    
    # Perform rolling sum
    temp_df[col_name] = (temp_df[identifier] + 
                         temp_df.temp_shift).rolling('D', min_periods=1, closed='both') \
                          .sum()
    
    return df.merge(temp_df[['customer', col_name]], how="left", left_on=['customer'], right_index=True)

df_result = sum_of_last_n_days(large_basket, "customer", "trade_created_at", 365)

In this example, n represents the number of days to calculate the rolling sum over. We use the shift() function to shift rows by -n+1 periods before performing the rolling sum.

The Mysterious Case of Incorrect Rolling Sums

Now that we have an understanding of how to implement a rolling sum, let’s go back to our original example and examine why it wasn’t working correctly.

When looking at the provided sum_of_last_365_days() function, there are several factors that might lead to incorrect results:

Incorrect Edge Behavior: Using closed='both' ensures both the start and end dates of the window are included in calculations. If you’re unsure about edge behavior for your use case, it’s best to try with different settings.
Missing Data Handling: In some cases, missing data can affect rolling sum accuracy. When dealing with such scenarios, ensure that any necessary handling is applied (e.g., filling missing values or dropping them).
Incorrect Window Size or Timing: Misinterpretation of time intervals (window_size) could result in incorrect calculations.

Conclusion and Best Practices

Rolling sums are powerful tools for analyzing time-based data. However, there’s a need to ensure correct implementation due to edge cases.

Best practices:

Always check the documentation for any functions you plan to use.
Understand how the window_size parameter is interpreted in different situations and units of measurement.
Verify whether your calculations require edge behavior or not.

Last modified on 2024-10-08