Using NumPy to Analyze My Daily Habits (Sleep, Screen Time & Mood)

a small NumPy mission collection the place I attempt to really construct one thing with NumPy as an alternative of simply going by means of random features and documentation. I’ve all the time felt that one of the simplest ways to be taught is by doing, so on this mission, I wished to create one thing each sensible and private.

The concept was easy: analyze my each day habits — sleep, examine hours, display time, train, and temper — and see how they have an effect on my productiveness and basic well-being. The info isn’t actual; it’s fictional, simulated over 30 days. However the aim isn’t the accuracy of the information — it’s studying methods to use NumPy meaningfully.

So let’s stroll by means of the method step-by-step.

Step 1 — Loading and Understanding the Knowledge

I began by making a easy NumPy array that contained 30 rows (one for every day) and 6 columns — every column representing a unique behavior metric. Then I saved it as a .npy file so I might simply load it later.

# TODO: Import NumPy and cargo the .npy knowledge file
import numpy as np
knowledge = np.load(‘activity_data.npy’)

As soon as loaded, I wished to verify that all the things regarded as anticipated. So I checked the form (to know what number of rows and columns there have been) and the variety of dimensions (to verify it’s a 2D desk, not a 1D checklist).

# TODO: Print array form, first few rows, and so forth.
knowledge.form
knowledge.ndim

OUTPUT: 30 rows, 6 columns, and ndim=2

I additionally printed out the primary few rows simply to visually verify that every worth regarded nice — for example, that sleep hours weren’t unfavorable or that the temper values had been inside an inexpensive vary.

# TODO: Prime 5 rows
knowledge[:5]

Output:

array([[ 1. , 6.5, 5. , 4.2, 20. , 6. ],
[ 2. , 7.2, 6. , 3.1, 35. , 7. ],
[ 3. , 5.8, 4. , 5.5, 0. , 5. ],
[ 4. , 8. , 7. , 2.5, 30. , 8. ],
[ 5. , 6. , 5. , 4.8, 10. , 6. ]])

Step 2 — Validating the Knowledge

Earlier than doing any evaluation, I wished to verify the information made sense. It’s one thing we regularly skip when working with fictional knowledge, but it surely’s nonetheless good follow.

So I checked:

• No unfavorable sleep hours
• No temper scores lower than 1 or higher than 10

For sleep, that meant choosing the sleep column (index 1 in my array) and checking if any values had been under zero.

# Be sure values are affordable (no unfavorable sleep)
knowledge[:, 1] < 0

Output:

array([False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False])

This implies no negatives. Then I did the identical for temper. I counted to seek out that the temper column was at index 5, and checked if any had been under 1 or above 10.

# Is temper out of vary?
knowledge[:, 5] < 1
knowledge[:, 5] > 10

We received the identical output.

The whole lot regarded good, so we might transfer on.

Step 3 — Splitting the Knowledge into Weeks

I had 30 days of knowledge, and I wished to investigate it week by week. The primary intuition was to make use of NumPy’s break up() perform, however that failed as a result of 30 isn’t evenly divisible by 4. So as an alternative, I used np.array_split(), which permits uneven splits.

That gave me:

• Week 1 → 8 days
• Week 2 → 8 days
• Week 3 → 7 days
• Week 4 → 7 days

# TODO: Slice knowledge into week 1, week 2, week 3, week 4
weekly_data = np.array_split(knowledge, 4)
weekly_data

Output:

[array([[ 1. , 6.5, 5. , 4.2, 20. , 6. ],
[ 2. , 7.2, 6. , 3.1, 35. , 7. ],
[ 3. , 5.8, 4. , 5.5, 0. , 5. ],
[ 4. , 8. , 7. , 2.5, 30. , 8. ],
[ 5. , 6. , 5. , 4.8, 10. , 6. ],
[ 6. , 7.5, 6. , 3.3, 25. , 7. ],
[ 7. , 8.2, 3. , 6.1, 40. , 7. ],
[ 8. , 6.3, 4. , 5. , 15. , 6. ]]),

array([[ 9. , 7. , 6. , 3.2, 30. , 7. ],
[10. , 5.5, 3. , 6.8, 0. , 5. ],
[11. , 7.8, 7. , 2.9, 25. , 8. ],
[12. , 6.1, 5. , 4.5, 15. , 6. ],
[13. , 7.4, 6. , 3.7, 30. , 7. ],
[14. , 8.1, 2. , 6.5, 50. , 7. ],
[15. , 6.6, 5. , 4.1, 20. , 6. ],
[16. , 7.3, 6. , 3.4, 35. , 7. ]]),

array([[17. , 5.9, 4. , 5.6, 5. , 5. ],
[18. , 8.3, 7. , 2.6, 30. , 8. ],
[19. , 6.2, 5. , 4.3, 10. , 6. ],
[20. , 7.6, 6. , 3.1, 25. , 7. ],
[21. , 8.4, 3. , 6.3, 40. , 7. ],
[22. , 6.4, 4. , 5.1, 15. , 6. ],
[23. , 7.1, 6. , 3.3, 30. , 7. ]]),

array([[24. , 5.7, 3. , 6.7, 0. , 5. ],
[25. , 7.9, 7. , 2.8, 25. , 8. ],
[26. , 6.2, 5. , 4.4, 15. , 6. ],
[27. , 7.5, 6. , 3.5, 30. , 7. ],
[28. , 8. , 2. , 6.4, 50. , 7. ],
[29. , 6.5, 5. , 4.2, 20. , 6. ],
[30. , 7.4, 6. , 3.6, 35. , 7. ]])]

Now the information was in 4 chunks, and I might simply analyze every one individually.

Step 4 — Calculating Weekly Metrics

I wished to get a way of how every behavior modified from week to week. So I centered on 4 major issues:

• Common sleep
• Common examine hours
• Common display time
• Common temper rating

I saved every week’s array in a separate variable, then used np.imply() to calculate the averages for every metric.

Common sleep hours

# retailer into variables
week_1 = weekly_data[0]
week_2 = weekly_data[1]
week_3 = weekly_data[2]
week_4 = weekly_data[3]

# TODO: Compute common sleep
week1_avg_sleep = np.imply(week_1[:, 1])
week2_avg_sleep = np.imply(week_2[:, 1])
week3_avg_sleep = np.imply(week_3[:, 1])
week4_avg_sleep = np.imply(week_4[:, 1])

Common examine hours

# TODO: Compute common examine hours
week1_avg_study = np.imply(week_1[:, 2])
week2_avg_study = np.imply(week_2[:, 2])
week3_avg_study = np.imply(week_3[:, 2])
week4_avg_study = np.imply(week_4[:, 2])

Common display time

# TODO: Compute common display time
week1_avg_screen = np.imply(week_1[:, 3])
week2_avg_screen = np.imply(week_2[:, 3])
week3_avg_screen = np.imply(week_3[:, 3])
week4_avg_screen = np.imply(week_4[:, 3])

Common temper rating

# TODO: Compute common temper rating
week1_avg_mood = np.imply(week_1[:, 5])
week2_avg_mood = np.imply(week_2[:, 5])
week3_avg_mood = np.imply(week_3[:, 5])
week4_avg_mood = np.imply(week_4[:, 5])

Then, to make all the things simpler to learn, I formatted the outcomes properly.

# TODO: Show weekly outcomes clearly
print(f”Week 1 — Common sleep: {week1_avg_sleep:.2f} hrs, Examine: {week1_avg_study:.2f} hrs, “
f”Display screen time: {week1_avg_screen:.2f} hrs, Temper rating: {week1_avg_mood:.2f}”)

print(f”Week 2 — Common sleep: {week2_avg_sleep:.2f} hrs, Examine: {week2_avg_study:.2f} hrs, “
f”Display screen time: {week2_avg_screen:.2f} hrs, Temper rating: {week2_avg_mood:.2f}”)

print(f”Week 3 — Common sleep: {week3_avg_sleep:.2f} hrs, Examine: {week3_avg_study:.2f} hrs, “
f”Display screen time: {week3_avg_screen:.2f} hrs, Temper rating: {week3_avg_mood:.2f}”)

print(f”Week 4 — Common sleep: {week4_avg_sleep:.2f} hrs, Examine: {week4_avg_study:.2f} hrs, “
f”Display screen time: {week4_avg_screen:.2f} hrs, Temper rating: {week4_avg_mood:.2f}”)

Output:

Week 1 – Common sleep: 6.94 hrs, Examine: 5.00 hrs, Display screen time: 4.31 hrs, Temper rating: 6.50
Week 2 – Common sleep: 6.97 hrs, Examine: 5.00 hrs, Display screen time: 4.39 hrs, Temper rating: 6.62
Week 3 – Common sleep: 7.13 hrs, Examine: 5.00 hrs, Display screen time: 4.33 hrs, Temper rating: 6.57
Week 4 – Common sleep: 7.03 hrs, Examine: 4.86 hrs, Display screen time: 4.51 hrs, Temper rating: 6.57

Step 5 — Making Sense of the Outcomes

As soon as I printed out the numbers, some patterns began to point out up.

My sleep hours had been fairly regular for the primary two weeks (round 6.9 hours), however in week three, they jumped to round 7.1 hours. Meaning I used to be “sleeping higher” because the month went on. By week 4, it stayed roughly round 7.0 hours.

For examine hours, it was the other. Week one and two had a mean of round 5 hours per day, however by week 4, it had dropped to about 4 hours. Mainly, I began off sturdy however slowly misplaced momentum — which, truthfully, sounds about proper.

Then got here display time. This one damage a bit. In week one, it was roughly 4.3 hours per day, and it simply stored creeping up each week. The traditional cycle of being productive early on, then slowly drifting into extra “scrolling breaks” later within the month.

Lastly, there was temper. My temper rating began at round 6.5 in week one, went barely as much as 6.6 in week two, after which sort of hovered there for the remainder of the interval. It didn’t transfer dramatically, but it surely was fascinating to see a small spike in week two — proper earlier than my examine hours dropped and my display time elevated.

To make issues interactive, I assumed it’d be nice to visualise utilizing matplotlib.

Step 6 — On the lookout for Patterns

Now that I had the numbers, I wished to know why my temper went up in week two.

So I in contrast the weeks facet by facet. Week two had respectable sleep, excessive examine hours, and comparatively low display time in comparison with the later weeks.

Which may clarify why my temper rating peaked there. By week three, despite the fact that I slept extra, my examine hours had began to dip — possibly I used to be resting extra however getting much less carried out, which didn’t enhance my temper as a lot as I anticipated.

That is what I favored in regards to the mission: it’s not in regards to the knowledge being actual, however about how one can use NumPy to discover patterns, relationships, and small insights. Even fictional knowledge can inform a narrative whenever you have a look at it the proper method.

Step 7 — Wrapping Up and Subsequent Steps

On this little mission, I realized a number of key issues — each about NumPy and about structuring evaluation like this.

We began with a uncooked array of fictional each day habits, realized methods to verify its construction and validity, break up it into significant chunks (weeks), after which used easy NumPy operations to investigate every phase.

It’s the sort of small mission that reminds you that knowledge evaluation doesn’t all the time should be complicated. Typically it’s nearly asking easy questions like “How is my display time altering over time?” or “When do I really feel one of the best?”

If I wished to take this additional (which I most likely will), there are such a lot of instructions to go:

• Discover the greatest and worst days total
• Evaluate weekdays vs weekends
• And even create a easy “wellbeing rating” based mostly on a number of habits mixed

However that’ll most likely be for the following a part of the collection.

For now, I’m completely satisfied that I received to use NumPy to one thing that feels actual and relatable — not simply summary arrays and numbers, however habits and feelings. That’s the sort of studying that sticks.

Thanks for studying.

When you’re following together with the collection, attempt recreating this by yourself fictional knowledge. Even when your numbers are random, the method will train you methods to slice, break up, and analyze arrays like a professional.