{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# Iteterables II - Code Cards\n",
    "## Try me\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ffraile/computer_science_tutorials/blob/main/source/Introduction/exercises/iterables%20II%20code%20cards.ipynb)[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/ffraile/computer_science_tutorials/main?labpath=source%2FIntroduction%2Fexercises%2Fiterables%20II%20code%20cards.ipynb)\n",
    "\n",
    "## How to use\n",
    "- Each card mirrors an A4 classroom prompt. **Predict first** (or discuss), then run the cell to check.\n",
    "- Detective cards show a buggy idea in Markdown; the code cell shows a **fixed** version.\n",
    "- Keep explanations short and schematic (*what* → *why*).\n",
    "\n",
    "### Turn Gemini into a coding tutor (no direct answers)\n",
    "Paste this in your first chat with Gemini to keep it in “tutor mode”:\n",
    "\n",
    "```markdown\n",
    "You are a **coding tutor** for Python in Jupyter/Colab. Follow the **course motto** “do not give up learning.”\n",
    "\n",
    "### Role & Goals\n",
    "- Use **Socratic guidance** and **test-first thinking** to help me solve problems myself.\n",
    "- Help me read errors, reason about state, and make small, safe iterations.\n",
    "\n",
    "### Strict Rules\n",
    "1) **Do not** provide full working solutions or paste complete functions/programs.\n",
    "   - You may show **tiny illustrative fragments (≤3 lines)** or **pseudo-code with TODOs**, but not a drop-in answer.\n",
    "2) Prefer **questions over answers**; offer **one small next step** at a time.\n",
    "3) When debugging, explain **what the traceback says**, give **2–3 hypotheses**, and propose the **smallest diff** in *plain English* first.\n",
    "4) Encourage **TDD**: ask me to write/assert a test, predict, run, and report outputs.\n",
    "5) Keep responses concise (≈120–150 words) unless I ask for a deeper explanation or code review.\n",
    "6) Ask me to **run code and share results**; adapt based on the output.\n",
    "7) If I request the full solution, remind me of the rules and offer a **higher-tier hint** instead.\n",
    "8) When I finalize an exercise, reinforce learning lessons and suggest additional exercises\n",
    "\n",
    "### Interaction Loop (use this structure)\n",
    "- **Restate goal:** what I’m trying to accomplish in one line.\n",
    "- **Diagnose:** key assumption to check or error to interpret.\n",
    "- **Hint (tiered):**\n",
    "  - Tier 1: Conceptual nudge (no code).\n",
    "  - Tier 2: Directed hint (identify line/construct to change).\n",
    "  - Tier 3: Pseudo-code with TODOs or a **1–3 line** pattern (still not a full solution).\n",
    "- **Next action:** one concrete step for me to try now.\n",
    "- **Ask back:** what to run/paste (output, test result, or traceback).\n",
    "\n",
    "### When reviewing my code\n",
    "- Comment on **correctness, clarity, naming, and complexity (big-O)**.\n",
    "- Suggest **tests** I’m missing (boundaries, empty cases, error paths).\n",
    "\n",
    "### Safety & Ethics\n",
    "- No secrets or private data in prompts.\n",
    "- avoid library functions/APIs unless I ask.\n",
    "\n",
    "Stay in tutor mode for the whole session.\n",
    "```\n",
    "\n",
    "## Code Cards\n",
    "1. Does any statement raise an exception? (Explain why)\n",
    "```python\n",
    "t = (\"A\", \"B\", \"C\")\n",
    "y = [\"D\", \"E\", \"F\"]\n",
    "y = y + [\"G\"]\n",
    "t[0] = \"Z\"\n",
    "```"
   ],
   "id": "d9e1354908cad180"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "",
   "id": "c9e5e981bf9bbfab",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "2. Does any statement raise an exception? (Explain why)\n",
    "```python\n",
    "t = (\"A\", \"B\", \"C\")\n",
    "a = t[0]\n",
    "a += \"DD\"\n",
    "t[0] = \"Z\"\n",
    "```"
   ],
   "id": "3608a5e446587830"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "4494afe78d65cdb4"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "\n",
    "3. Given:\n",
    "\n",
    "```python\n",
    "real_names = {\"Homelander\": \"John\", \"Queen Maeve\": \"Maggie\", \"A-Train\": \"Reggie\", \"Starlight\": \"Annie\"}\n",
    "```\n",
    "Choose all expressions below that evaluate to True:\n",
    "```python\n",
    "\"A-Train\" in real_names\n",
    "\"John\" in real_names\n",
    "\"Maggie\" in real_names.values()\n",
    "\"Annie\" in real_names.items()\n",
    "```"
   ],
   "id": "be30cefb031715b9"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "19b153aad0773b89"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "4. Given:\n",
    "```python\n",
    "supes = {\"Homelander\": {\"real_name\": \"John\", \"power_level\": 100},\n",
    "         \"Queen Maeve\": {\"real_name\": \"Maggie\", \"power_level\": 85},\n",
    "         \"A-Train\": {\"real_name\": \"Reggie\", \"power_level\": 75},\n",
    "         \"Starlight\": {\"real_name\": \"Annie\", \"power_level\": 80}}\n",
    "```\n",
    "Choose all expressions below that evaluate to True:\n",
    "```python\n",
    "\"power_level\" in supes[\"A-Train\"]\n",
    "\"Reggie\" in supes[\"A-Train\"].values()\n",
    "\"Annie\" in supes[\"Starlight\"].keys()\n",
    "supes[\"Homelander\"][\"power_level\"] < supes[\"Queen Maeve\"][\"power_level\"]\n",
    "```\n"
   ],
   "id": "2d17833c50fff38c"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "c147764e1b8d1f20"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "5. Given:\n",
    "```python\n",
    "students = {\"001\": {\"name\": \"Peter Parker\", \"grades\": [85, 90, 78]},\n",
    "            \"002\": {\"name\": \"Jean Grey\", \"grades\": [88, 76, 92]},\n",
    "            \"003\": {\"name\": \"Alonzo Thompson\", \"grades\": [95, 85, 87]}}\n",
    "```\n",
    "Select the best way to iterate through the students dictionary to calculate and print each student's average grade together with their id and name. Motivate your response.\n",
    "```python\n",
    "# Option A\n",
    "for student_id in students:\n",
    "    info = students[student_id]\n",
    "    for grade in info[\"grades\"]:\n",
    "        avg_grade = sum(info[\"grades\"]) / len(info[\"grades\"])\n",
    "    print(f\"ID: {student_id}, Name: {info['name']}, Average Grade: {avg_grade:.2f}\")\n",
    "# Option B\n",
    "for student_id, info in students.items():\n",
    "    avg_grade = sum(info[\"grades\"]) / len(info[\"grades\"])\n",
    "    print(f\"ID: {student_id}, Name: {info['name']}, Average Grade: {avg_grade:.2f}\")\n",
    "\n",
    "# Option C\n",
    "for info in students.values():\n",
    "    avg_grade = sum(info[\"grades\"]) / len(info[\"grades\"])\n",
    "    print(f\"Name: {info['name']}, Average Grade: {avg_grade:.2f}\")\n",
    "```\n",
    "\n"
   ],
   "id": "5aef896e67b6cb87"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "7e981139fb2c90f4"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "6. Given:\n",
    "```python\n",
    "students = {\"001\": {\"name\": \"Peter Parker\", \"grades\": [85, 90, 78]},\n",
    "            \"002\": {\"name\": \"Jean Grey\", \"grades\": [88, 76, 92]},\n",
    "            \"003\": {\"name\": \"Alonzo Thompson\", \"grades\": [95, 85, 87]}}\n",
    "```\n",
    "\n",
    "Predict the output of the following code snippet:\n",
    "```python\n",
    "print(students[\"002\"][\"grades\"][1])\n",
    "print(len(students))\n",
    "print(\"Jean Grey\" in students[\"002\"])\n",
    "```\n"
   ],
   "id": "65c6895dc0e37429"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "6f98ce8f2a3d008c"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "7. Given:\n",
    "```python\n",
    "supes = {\"Homelander\": {\"real_name\": \"John\", \"power_level\": 100},\n",
    "         \"Queen Maeve\": {\"real_name\": \"Maggie\", \"power_level\": 85},\n",
    "         \"A-Train\": {\"real_name\": \"Reggie\", \"power_level\": 75},\n",
    "         \"Starlight\": {\"real_name\": \"Annie\", \"power_level\": 80}}\n",
    "```\n",
    "Predict the output of the following code snippet:\n",
    "```python\n",
    "print(supes[\"Startlight\"][\"power_level\"])\n",
    "print(supes[\"A-Train\"][\"real_name\"])\n",
    "for supe in supes:\n",
    "    if supes[supe][\"power_level\"] > 80:\n",
    "        print(f\"{supe} is powerful\")\n",
    "```\n"
   ],
   "id": "e3f579b21561fd63"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "e899dac818c5bf7c"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "8. Given:\n",
    "```python\n",
    "pilots = [{\"name\": \"Han Solo\", \"ship\": \"Millennium Falcon\", \"speed\": 1050, \"badges\": [\"Smuggler\", \"Gunner\"]},\n",
    "          {\"name\": \"Luke Skywalker\", \"ship\": \"X-Wing\", \"speed\": 950, \"badges\": [\"Jedi\", \"Pilot\"]},\n",
    "          {\"name\": \"Darth Vader\", \"ship\": \"TIE Fighter\", \"speed\": 900, \"badges\": [\"Sith Lord\", \"Commander\"]}]\n",
    "```\n",
    "Select the best way to iterate through the pilots list to print each pilot's name, ship, and badges. Motivate your response.\n",
    "```python\n",
    "# Option A\n",
    "for pilot in pilots:\n",
    "    print(f\"Name: {pilot['name']}, Ship: {pilot['ship']}, Badges: {', '.join(pilot['badges'])}\")\n",
    "# Option B\n",
    "for i in range(len(pilots)):\n",
    "    pilot = pilots[i]\n",
    "    print(f\"Name: {pilot['name']}, Ship: {pilot['ship']}, Badges: {', '.join(pilot['badges'])}\")\n",
    "# Option C\n",
    "for pilot in pilots:\n",
    "    for k, v in pilot.items():\n",
    "        print(f\"{k}: {v}\")\n",
    "```\n"
   ],
   "id": "fce8ecebc4fd2525"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "519b6dccef4f8d25"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "9. Predict the output of the following code snippet:\n",
    "```python\n",
    "pilots = [{\"name\": \"Han Solo\", \"ship\": \"Millennium Falcon\", \"speed\": 1050, \"badges\": [\"Smuggler\", \"Gunner\"]},\n",
    "          {\"name\": \"Luke Skywalker\", \"ship\": \"X-Wing\", \"speed\": 950, \"badges\": [\"Jedi\", \"Pilot\"]},\n",
    "          {\"name\": \"Darth Vader\", \"ship\": \"TIE Fighter\", \"speed\": 900, \"badges\": [\"Sith Lord\", \"Commander\"]}]\n",
    "print(pilots[1][\"badges\"][0])\n",
    "print(pilots[2][\"speed\"] > pilots[0][\"speed\"])\n",
    "print(len(pilots[0][\"badges\"][1]))\n",
    "```\n",
    "\n"
   ],
   "id": "8a4c4c8f7562aea6"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "",
   "id": "76385679641faeb1",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "10. Which of the following snippets produces the same result as the others?\n",
    "```python\n",
    "# Option A\n",
    "series = []\n",
    "for i in range(10):\n",
    "    if i > 0 and i % 3 == 0:\n",
    "        series.append(1/i**3)\n",
    "print(series)\n",
    "\n",
    "# Option B\n",
    "series = [i%3 == 0 for i in range(10) if i > 0 and 1/i**3 == 0]\n",
    "print(series)\n",
    "\n",
    "# Option C\n",
    "series = [1/i**3 for i in range(10) if i > 0 and i % 3 == 0]\n",
    "print(series)\n",
    "\n",
    "# Option D\n",
    "series = [1/i**3 if i > 0 and i % 3 == 0 else 0 for i in range(10)]\n",
    "print(series)\n",
    "```"
   ],
   "id": "69b37dcef9fc38ce"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "",
   "id": "b333f95ce012babf",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "11. What is the series of numbers generated by the following code snippet? Briefly explain how it works.\n",
    "```python\n",
    "s = [i**2 for i in range(5) if i % 2 == 0]\n",
    "```\n",
    "**A** The series is: ```[0, 1, 4, 9, 16]```\n",
    "\n",
    "**B** The series is: ```[1, 4, 9, 16, 25]```\n",
    "\n",
    "**C** The series is: ```[0, 4, 16]```\n",
    "\n",
    "**D** The series is Fall Out season 3 premiering November 2026."
   ],
   "id": "3388b99c3e0230f4"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "1c335920478c42ec"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "12. What is the series of numbers generated by the following code snippet? Briefly explain how it works.\n",
    "```python\n",
    "fib = [0, 1] + [fib.append(sum(fib[-2:])) for _ in range(5)]\n",
    "```\n",
    "**A** The series is: ```[0, 1, 2, 3, 4, 5, 6]```\n",
    "\n",
    "**B** The series is: ```[0, 1, 1, 2, 3, 5, 8]```\n",
    "\n",
    "**C** The series is: ```[0, 1, 1, 2, 4, 8, 16```\n",
    "\n",
    "**D** The code raises an exception."
   ],
   "id": "cc19f41e143cde83"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "43310a432ab96f3d"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    " 13. What is the series of numbers generated by the following code snippet? Briefly explain how it works.\n",
    "```python\n",
    "series = [2**i for i in range(6) if i % 2 == 1]\n",
    "```\n",
    "**A** The series is: ```[1, 2, 4, 8, 16, 32]```\n",
    "\n",
    "**B** The series is: ```[2, 8, 32]```\n",
    "\n",
    "**C** The series is: ```[2, 4, 6, 8, 10]```\n",
    "\n",
    "**D** The code raises an exception."
   ],
   "id": "80e85953d87146b1"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "8f4740648320b2af"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}