Chapter 15: SciPy Exercises

SciPy Exercises  most often refers to the small topic-wise practice sets on W3Schools (the same site that has the well-known SciPy Quiz and the Try-it-Yourself editor).

These are not deep programming projects or long Jupyter notebooks — they are quick, browser-based micro-exercises designed to reinforce what you just read in their tutorial chapters.

Where do you find the main “SciPy Exercises”?

Direct link (2026 working address): https://www.w3schools.com/python/scipy/scipy_exercises.php

From any page in their SciPy tutorial (e.g. https://www.w3schools.com/python/scipy/index.php) you usually see a sidebar or bottom links: SciPy Quiz • SciPy Exercises • SciPy Editor • SciPy Certificate

Click SciPy Exercises → you land on the overview page.

What do these exercises actually look like? (very detailed)

• Format: Browser-based, no installation needed

• Question types: Mix of

  • Multiple choice (4 options)
  • Fill-in-the-blank (type the missing word/code snippet)

• Number per topic: 3–9 questions (usually short sets)

• Topics / categories: One small set for almost every chapter in their tutorial Examples you’ll see listed:

  • SciPy Introduction / Getting Started
  • SciPy Constants
  • SciPy Optimizers
  • SciPy Sparse Data
  • SciPy Graphs (csgraph)
  • SciPy Spatial Data
  • SciPy Matlab Arrays (io.loadmat / savemat)
  • SciPy Interpolation
  • SciPy Statistical Significance Tests

• How it works:

  1. Click a category (e.g. “SCIPY Interpolation”)
  2. 4–7 short questions appear one by one
  3. Answer → click “Submit Answer” or “Next”
  4. Immediate feedback: green = correct, red + correct answer shown if wrong
  5. At the end of the set → your score for that topic (e.g. 6/7)
  6. Optional: sign in (free) → track progress across visits

• Difficulty: Beginner to lower-intermediate Mostly testing recognition and basic syntax recall (not deep problem-solving or debugging).

Typical question examples (real style from W3Schools SciPy exercises)

Example from “SCIPY Optimizers” set Which function is most commonly used to fit a model to data points? A) scipy.optimize.root B) scipy.optimize.minimize C) scipy.optimize.curve_fit D) scipy.optimize.linprog

Correct: C

Example from “SCIPY Interpolation” Which interpolator preserves monotonicity and avoids overshoot? A) interp1d(kind=’cubic’) B) PchipInterpolator C) Akima1DInterpolator D) Both B and C

Correct: D (both are shape-preserving)

Example from “SCIPY Constants” What is the correct way to get the speed of light in m/s? (from scipy import constants) A) constants.c B) constants.speed_of_light C) constants.C D) scipy.constants.light

Correct: A

Fill-in-the-blank style Complete the import so that quad can be used directly: from scipy.______ import quad

Answer: integrate

Strengths & realistic teacher view

Good for

• Quick reinforcement right after reading a chapter
• Checking whether you remember key function names & submodule locations
• Zero setup (browser only)
• Motivational small wins (finishing 5–7 questions feels good)

Not good for

• Real coding practice (no writing full functions, no debugging, no plots to interpret)
• Advanced usage (e.g. bounds in optimization, custom ODE solvers, sparse solver tuning)
• Understanding error messages or edge cases
• Building intuition through experimentation

My recommended learning path (how I guide students)

1. Read the W3Schools chapter (e.g. SciPy Interpolation)
2. Try the examples in their SciPy Editor (compiler)
3. Do the corresponding Exercises set (3–9 questions)
4. If you score < 80% → go back to the tutorial + editor
5. Then (this is the important jump): Switch to Jupyter / Google Colab and do real mini-coding tasks yourself

Quick real coding exercise examples (better than fill-in-the-blanks)

Since the W3Schools ones are recognition-focused, here are three tiny copy-paste coding exercises you can do in any Python environment (Colab, local Jupyter, etc.) to actually practice:

Exercise 1 – Constants + simple calculation Compute the thermal de Broglie wavelength of a hydrogen atom at 300 K. Use scipy.constants for h, m_p (or m_H ≈ m_p), k. Formula: λ = h / sqrt(2 π m k T) Print the result in nanometers.

Exercise 2 – Curve fitting practice Generate x = np.linspace(0,5,60) y = 4.2 * np.exp(-1.1x) + 0.8 + noise (normal scale=0.4) Fit with curve_fit using def model(x,a,tau,b): return anp.exp(-x/tau)+b Print fitted parameters + plot data vs fit.

Exercise 3 – Interpolation choice Take these points: x = [0, 1.5, 3, 4.2, 6] y = [2.1, 3.8, 3.2, 4.9, 5.1] Create 4 interpolators: linear, cubic (make_interp_spline), Pchip, Akima Plot all on a fine grid → observe which one overshoots least.

Final teacher summary

“SciPy Exercises” = → mainly the small browser-based MCQ + fill-in sets on W3Schools → link → https://www.w3schools.com/python/scipy/scipy_exercises.php → purpose → fast check after reading their short tutorial pages → good starting point, but switch to real coding as soon as possible

Finished any of them yet? What score did you get on a particular topic? Or do you want me to create 6–8 custom mini-coding exercises on one submodule (optimization? interpolation? stats? sparse?) right now?

Just tell me — we’ll make it practical and fun! 🚀