Module 4 · Lesson 5

Putting It Together: Analyzing Daily Temperatures

Let’s generate a month of synthetic daily high temperatures (°C) and use everything from this module — aggregation, argmin/argmax, sorting, and counting — to analyze it in one exercise.

rng = np.random.default_rng(seed=7)
days = np.arange(1, 31)
# a gentle seasonal wave plus daily noise, just to have realistic-looking data
temps = 18 + 4 * np.sin(np.linspace(0, np.pi, 30)) + rng.normal(0, 1.5, size=30)
temps = np.round(temps, 1)

plt.figure(figsize=(8, 3))
plt.plot(days, temps, marker="o")
plt.xlabel("Day of month")
plt.ylabel("High temp (°C)")
plt.title("Synthetic daily high temperatures")
plt.show()

print(temps)

Output:

[18.  18.9 18.4 17.9 19.  18.6 20.5 22.8 20.3 20.4 22.3 22.3 22.  20.6
 22.  23.  19.9 21.2 18.9 19.6 18.5 20.7 18.8 20.8 20.3 19.4 15.5 18.1
 18.4 18.2]

Write temperature_report(temps, threshold) that takes the 1-D array of daily temperatures and a threshold value, and returns a dict with:

"hottest_day": the 1-based day number (int) with the highest temp (day 1 = temps[0])
"coldest_day": the 1-based day number (int) with the lowest temp
"mean": the mean temperature (float)
"std": the standard deviation (float)
"days_above_threshold": how many days exceeded threshold (int)
"sorted_desc": the temperatures sorted from hottest to coldest (array)

Use argmax/argmin, mean/std, boolean indexing + .sum() (or np.count_nonzero), and np.sort — don't write a loop.

💡 Hint

temps.argmax() is a 0-based flat index — add 1 to get the day number. (temps > threshold).sum() counts how many exceed the threshold. np.sort(temps)[::-1] sorts hottest-first.

✅ Solution

def temperature_report(temps, threshold):
    return {
        "hottest_day": int(temps.argmax()) + 1,
        "coldest_day": int(temps.argmin()) + 1,
        "mean": float(temps.mean()),
        "std": float(temps.std()),
        "days_above_threshold": int((temps > threshold).sum()),
        "sorted_desc": np.sort(temps)[::-1],
    }

🎯 Key Takeaways

axis=0 reduces down columns (one result per column); axis=1 reduces across rows (one result per row).
argmin/argmax locate where an extreme value is; np.unravel_index turns a flat index back into (row, col) coordinates.
np.sort returns a copy, arr.sort() sorts in place, and np.argsort gives you an ordering you can apply to another array.
np.unique(..., return_counts=True) is your go-to for frequency analysis without writing a loop.
Real analysis is usually a short chain of these tools, not a single call.

Next up: Module 5 — Linear Algebra & Random, where you’ll work with vectors, matrices, and NumPy’s modern random number generator.

Putting It Together: Analyzing Daily Temperatures

Temperature report

🎯 Key Takeaways