Ever Added Up Pocket Money?

Remember those days when you saved coins in a jar and tracked how much you had after each addition? 🪙 That feeling of watching your savings grow is exactly what we’re tackling in this post—with arrays!

In programming, that growing collection is called a Running Sum or Cumulative Sum. And today, we’ll learn how to build it using Python—no finance degree required. 😉

What is a Running Sum?

Think of a running sum like stacking blocks. Each new block sits on top of the last one. This process makes the tower taller. In programming, we take a list of numbers. We make a new list where each number is the total of all numbers before it, including itself.

Example:

Input: [1, 2, 3, 4]

Running Sum Output: [1, 3, 6, 10]

Because:

• 1 → 1
• 1 + 2 → 3
• 1 + 2 + 3 → 6
• 1 + 2 + 3 + 4 → 10

Why Does It Matter?

Real-Life Use Cases

• 📊 Finance: Calculating a bank balance after each transaction.
• 🛒 E-commerce: Summing cart value as items are added.
• 🧠 AI & ML: Normalizing data or calculating rolling averages.
• 🎮 Gaming: Keeping track of score progression.

Solution 1: Basic For Loop in Python

This is the most intuitive and beginner-friendly method.

Walkthrough

• We initialize a total as 0.
• As we loop through each number, we add it to the total.
• We append the total to our running_sum list.

Solution 2: In-place Modification (More Efficient)

This version modifies the original list, which saves space!

Solution 3: Using itertools.accumulate() (Pythonic)

Super clean and readable. Great for professionals and in interviews!

Pro Tips & Tricks 🧠

✅ Use Lists Wisely: If you don’t need the original array, modify in-place to save memory.

⚡ Use accumulate when working with large datasets—it’s optimized and clean.

🧪 Test With Negatives: Arrays aren’t always positive. Always test with negative numbers.

💡 Know the Use Case: Choose your approach based on your needs. Determine if you need to return a new array. Alternatively, decide if you need to modify the original.

⏱️ Time Complexity: All the above methods are O(n)—which is pretty efficient!

Common Mistakes To Avoid ❌

• ❗ Forgetting to Initialize total: This causes crashes or wrong output.
• 🔁 Using += on a string or wrong type: Make sure your data type supports addition.
• 🔄 Overwriting the original list when you need it later.

Expert Insights 🧠

🔍 In-place vs Out-of-place: If memory is tight (like in embedded systems), prefer the in-place solution.

📈 Great for Interview Questions: This problem is often asked in tech interviews. It tests basic loops, array manipulation, and logic.

Conclusion & What’s Next? 🚀

Congratulations—you just ran through running sums like a pro! You might be prepping for interviews. Perhaps you are building a dashboard. Or you may be just starting your Python journey. Regardless, you need to understand the running sum. This is crucial. This understanding builds a solid foundation.

You got this! 💪 Happy coding!