Compute Group-Wise Running Totals with cumsum() in Pandas

Question

Given `transactions` (customer_id, txn_date, amount), calculate a running cumulative spend `cumulative_spend` for each customer over time.

Accepted Answer

Optimal Solution:
import pandas as pd

# Sort chronologically first
df = df.sort_values(['customer_id', 'txn_date'])

# Groupby cumsum
df['cumulative_spend'] = df.groupby('customer_id')['amount'].cumsum()

result_df = df

Technical Explanation:
After sorting by `['customer_id', 'txn_date']`, `groupby('customer_id')['amount'].cumsum()` computes progressive cumulative sums per partition.

customer_id	txn_date	amount
1	2026-01-01	100
1	2026-01-05	200
2	2026-01-02	500

customer_id	txn_date	amount	cumulative_spend
1	2026-01-01	100	100
1	2026-01-05	200	300
2	2026-01-02	500	500