Utilizing NumPy to Analyze My Each day Habits (Sleep, Display Time & Temper)

a small NumPy undertaking sequence the place I attempt to truly construct one thing with NumPy as an alternative of simply going by random capabilities and documentation. I’ve at all times felt that one of the best ways to study is by doing, so on this undertaking, I wished to create one thing each sensible and private.

The thought was easy: analyze my every 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 objective isn’t the accuracy of the information — it’s studying the right way to use NumPy meaningfully.

So let’s stroll by 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 special behavior metric. Then I saved it as a .npy file so I may simply load it later.

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

As soon as loaded, I wished to verify that every little thing seemed 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 record).

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

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

I additionally printed out the primary few rows simply to visually affirm that every worth seemed advantageous — as an example, that sleep hours weren’t destructive or that the temper values have been inside an inexpensive vary.

# TODO: Prime 5 rows
information[: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 frequently skip when working with fictional information, however it’s nonetheless good follow.

So I checked:

• No destructive sleep hours
• No temper scores lower than 1 or better than 10

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

# Be certain values are cheap (no destructive sleep)
information[:, 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 search out that the temper column was at index 5, and checked if any have been under 1 or above 10.

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

We bought the identical output.

Every little thing seemed good, so we may transfer on.

Step 3 — Splitting the Knowledge into Weeks

I had 30 days of information, and I wished to investigate it week by week. The primary intuition was to make use of NumPy’s cut up() operate, 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 information into week 1, week 2, week 3, week 4
weekly_data = np.array_split(information, 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 may 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 targeted on 4 important 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 every little thing simpler to learn, I formatted the outcomes properly.

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

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

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

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

Output:

Week 1 – Common sleep: 6.94 hrs, Research: 5.00 hrs, Display time: 4.31 hrs, Temper rating: 6.50
Week 2 – Common sleep: 6.97 hrs, Research: 5.00 hrs, Display time: 4.39 hrs, Temper rating: 6.62
Week 3 – Common sleep: 7.13 hrs, Research: 5.00 hrs, Display time: 4.33 hrs, Temper rating: 6.57
Week 4 – Common sleep: 7.03 hrs, Research: 4.86 hrs, Display 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 have been fairly regular for the primary two weeks (round 6.9 hours), however in week three, they jumped to round 7.1 hours. Which means 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 alternative. Week one and two had a median of round 5 hours per day, however by week 4, it had dropped to about 4 hours. Principally, I began off robust however slowly misplaced momentum — which, truthfully, sounds about proper.

Then got here display time. This one harm a bit. In week one, it was roughly 4.3 hours per day, and it simply stored creeping up each week. The basic 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 type of hovered there for the remainder of the interval. It didn’t transfer dramatically, however it was attention-grabbing 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 believed 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, although I slept extra, my examine hours had began to dip — perhaps I used to be resting extra however getting much less carried out, which didn’t increase my temper as a lot as I anticipated.

That is what I preferred concerning the undertaking: it’s not concerning the information being actual, however about how one can use NumPy to discover patterns, relationships, and small insights. Even fictional information can inform a narrative once you take a look at it the proper means.

Step 7 — Wrapping Up and Subsequent Steps

On this little undertaking, I realized just a few key issues — each about NumPy and about structuring evaluation like this.

We began with a uncooked array of fictional every day habits, realized the right way to test its construction and validity, cut up it into significant chunks (weeks), after which used easy NumPy operations to investigate every phase.

It’s the type of small undertaking that reminds you that information evaluation doesn’t at all times must be advanced. Typically it’s nearly asking easy questions like “How is my display time altering over time?” or “When do I really feel the most effective?”

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
• Examine weekdays vs weekends
• And even create a easy “wellbeing rating” primarily based on a number of habits mixed

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

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

Thanks for studying.

When you’re following together with the sequence, attempt recreating this by yourself fictional information. Even when your numbers are random, the method will train you the right way to slice, cut up, and analyze arrays like a professional.