How I Used NumPy to Make Sense of My Fitness Progress (And You Can Too!)

Last month, I started tracking my daily step count with a fitness band.

Some days I crushed it with 10,000+ steps.
Other days? Let’s just say... the couch won.

After a couple of weeks, I had a list of step counts but no idea what it all meant.

Was I doing okay overall? Was I improving? How consistent was I?

That’s when I turned to NumPy: a Python library that helps you make sense of numbers with just a few lines of code.

Today, I’ll show you how to use NumPy to perform basic statistical analysis on a dataset (like your own fitness steps!) using max, min, mean, and standard deviation.

And don’t worry, no math degree needed.

📦 Step 1: Import NumPy

First things first, we import the library.

📊 Step 2: Create a NumPy Array

Let’s say I tracked my steps for 10 days:

Now we’ve stored all our data in a NumPy array, which is like a supercharged list—faster and built for number-crunching.

🔍 Step 3: Find the Maximum and Minimum

Highest steps in a day:

👉 Output:

Lowest steps in a day:

👉 Output:

That gives us the best and worst days.

📈 Step 4: Calculate the Average (Mean)

The average tells us how we’re doing overall.

👉 Output:

So on most days, I’m getting close to 9,000 steps. Not bad!

🎯 Step 5: Check for Consistency (Standard Deviation)

The standard deviation shows how much your daily steps fluctuate.

👉 Output:

A smaller number means more consistency.
A larger number means your days swing between couch-potato and marathon-runner 😅

🧠 Why This Matters

Without stats, numbers are just noise.

But with a few NumPy functions, you can:

• See where you’re peaking

• Spot your off-days

• Understand your trends

• Build real awareness around your habits

This can apply to anything—calories, sleep, expenses, grades, sales... anything that has numbers.

🧰 Recap: Your NumPy Stat Tools

✅ Final Thought

If you’ve got a list of numbers and a curious mind, NumPy can give you real answers—fast.

So go ahead, pull up that data you’ve been collecting and run the numbers.
You might be surprised by what you learn about yourself (or your project)!

👉 Just starting with NumPy?
Play around with your own numbers using the examples above—and see what you discover!