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Cumulative Sum in Pandas – cumsum(), cummax(), cummin() & More in Python 2026

Cumulative calculations are extremely useful in data manipulation for running totals, growth analysis, ranking over time, and creating useful features. In 2026, Pandas provides fast and flexible cumulative functions like cumsum(), cummax(), cummin(), and cumprod().

TL;DR — Key Cumulative Methods

.cumsum() – Running total
.cummax() – Running maximum
.cummin() – Running minimum
.cumprod() – Running product
.cummax() / .cummin() with groupby() for segmented analysis

1. Basic Cumulative Sum

import pandas as pd

df = pd.read_csv("sales_data.csv", parse_dates=["order_date"])

# Simple running total
df["cumulative_sales"] = df["amount"].cumsum()

# Running total within each region
df = df.sort_values("order_date")
df["cumulative_sales_by_region"] = df.groupby("region")["amount"].cumsum()

print(df[["order_date", "region", "amount", "cumulative_sales_by_region"]].head(10))

2. Multiple Cumulative Operations

df["cum_sales"] = df["amount"].cumsum()
df["cum_max"] = df["amount"].cummax()
df["cum_min"] = df["amount"].cummin()
df["running_avg"] = df["amount"].expanding().mean()   # expanding window average

3. Real-World Example: Monthly Running Totals

monthly = (
    df
    .groupby([df["order_date"].dt.to_period("M"), "region"])
    .agg(total_sales=("amount", "sum"))
    .reset_index()
)

# Calculate cumulative sales per region over months
monthly["cumulative_sales"] = monthly.groupby("region")["total_sales"].cumsum()

print(monthly)

4. Best Practices in 2026

Always sort your DataFrame by the time column before applying cumulative functions
Use groupby() + cumsum() for segmented running totals
Combine cumsum() with expanding() for running statistics
Use .cumsum() to create useful features like "days since first purchase" or "cumulative revenue"
Be careful with NaN values — they can break cumulative calculations (use .fillna(0) when appropriate)

Conclusion

Cumulative operations like cumsum(), cummax(), and cummin() are powerful tools for time-based data manipulation. In 2026, combining them with groupby() allows you to create insightful running totals, growth metrics, and historical comparisons with very little code. These functions turn raw transactional data into valuable business intelligence.

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