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+# Lab-P2: Python Modes and Programming
+
+In the lecture this week, we learned about three ways to run Python: interactive mode, script mode, and notebook "mode" (people outside of CS220 won't use that vocabulary for notebooks, hence the quotes). In this lab, you'll practice those three modes. You'll also get practice with operators, modular arithmetic, and Boolean logic.
+
+To get started, please create a `lab-p2` directory inside your `cs220` directory (if you haven't already).  Then, open a terminal and use `cd` to navigate to `lab-p2` (review the steps from [lab-p1](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-s23-projects/-/tree/main/lab-p1) in case you are unsure of how to use cd and/or get the pathname of the `lab-p2` directory).
+
+## Learning Objectives
+
+After completing this lab, you will be able to...
+
+* Run Python code using interactive mode, script mode, and notebook "mode",
+* Write Python expressions containing mathematical, comparison, and Boolean operators,
+* Identify correct operator precedence order,
+* Apply parentheses to override operator precedence in your expression when needed,
+* Translate English statements into Python expressions,
+* Write correct Boolean expressions with subparts separated by Boolean operators of `or` and `and`.
+
+------------------------------
+## Segment 1: Interactive Mode (Python Shell)
+
+Let's start by looking at interactive mode, where code is executed one line at a time. Interactive mode is typically used for doing quick syntax checks. For a new Python programmer, the interactive mode is very helpful to try out simple examples.
+
+### Task 1.1: Determine your Python version.
+
+Run `python --version` in the terminal.  You might see something like this:
+
+```
+Python 3.9.13
+```
+
+If it says something like 2.X.X, try running `python3 --version` instead. If you need to run the latter, please use `python3` whenever we say `python` in the directions this semester.  
+
+Many commands support some type of version argument. How do you think you could figure out the version of Jupyter?
+
+**NOTE:** If your Python version is **NOT** 3.9.X, then you have failed to install Python as per the specifications of this course, and this may cause some unexpected errors in future projects. It is recommended that you attend Office Hours and install the correct version of Python.
+
+### Task 1.2: Use `pwd` to verify that you are in the `lab-p2` directory.
+
+The command `pwd` is a command line argument that stands for **P**rint **W**orking **D**irectory.
+If the `pwd` command prints out a different directory path than your `lab-p2` directory path, use `cd` commands to move to the correct working directory. See [lab-p1](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-s23-projects/-/tree/main/lab-p1#task-15-navigate-to-the-lab1-directory) if you don’t recall how to use `cd` commands to navigate to a directory.
+
+### Task 1.3: Start Python in **interactive** mode.
+
+Type `python` and press Enter.
+
+You should see something roughly like this (details will vary):
+
+```
+Python 3.9.13 (main, Aug 25 2022, 23:51:50) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win3
+Type "help", "copyright", "credits" or "license" for more information.
+>>>
+```
+
+Those `>>>` symbols are a Python prompt. This means you can type Python code, but your shell commands will not work until you exit Python again. Recall that we learned about shell commands in [lab-p1](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-s23-projects/-/tree/main/lab-p1#commonly-used-terminalpowershell-commands). Examples of shell commands include `cd`, `ls`, `mkdir`, etc.
+
+### Task 1.4: Run Python in interactive mode.
+
+Try typing this Python code:
+```python
+print("hi")
+```
+then press Enter.  The message `hi` should be printed.
+
+### Task 1.5: Try running a shell command in interactive mode.
+
+Ensure you still see the `>>>` prompt, then type `pwd` again and press Enter. This should give you the following error because `pwd` is only valid in the shell (not Python):
+
+```
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+  NameError: name 'pwd' is not defined
+```
+
+### Task 1.6: Exit interactive mode.
+
+You can exit the interactive mode by typing in `exit()` and pressing Enter (which works on both Mac and Windows).
+
+Alternatively, on Mac, you can do this with Control + D (hold down the Control key, then press the D key at the same time).  
+Alternatively, on Windows, you can use Control + Z and Enter (hold down the Control key, then press the Z key at the same time; release both, then press the Enter key).
+
+### Task 1.7: Try running Python code in the shell.
+
+Now that you've exited, try running both `pwd` and `print("hi")` again.  This time, the former should work and the latter should fail (because we're in the shell, and the former is a shell command whereas the latter is Python code).
+
+### Task 1.8: Re-enter interactive mode
+
+Type `python` and press Enter.
+
+### Task 1.9: Evaluate Python expressions
+
+Type each of the below expressions, predict the output and then press Enter, to confirm that you are getting the expected output.
+
+* `10/4`
+* `10//4`
+* `10%4`
+* `not False`
+* `not not True`
+* `not not not True`
+* `2*1000`
+* `"2"*1000` (what's the difference between this one and the previous one?)
+* `2**1000` (and what about this one?)
+* `1/0` (should fail)
+* `"ha"*100`
+* `"ha\n"*100`
+* `print("ha\n"*100)`
+* `print("ha\n\n"*100)`
+
+### Task 1.10: Exit interactive mode.
+
+That's the end of the first segment! Take a moment to summarize for yourself what you learned. If you aren't sure about anything above, feel free to ask your TA/PM for help. If you feel good, move on.
+
+------------------------------
+## Segment 2: Boolean Logic, Order of Operations, and Modular Arithmetic
+
+In this section, you'll get more practice downloading and running existing notebooks.
+
+### Task 2.1: Download `bool.ipynb`, `ops.ipynb`, and `mod.ipynb`
+
+You can find them in the `lab-p2` folder of this GitLab Repo - which is where you are now. Download these files to your local `lab-p2` folder.
+
+You need to follow the same procedure as you did to download files in `lab-p1`. In other words, do the following:
+
+1. At the top of this page, Left-click on the file you want to download (for example, say `bool.ipynb`).
+2. Right-click on the `Open raw` (<img src="images/raw_gitlab_button.png" width="30">) button.
+3. Choose `Save Link As...` (or similar) as in this image below.
+<img src="images/raw_gitlab.png" width="300">
+
+4. Navigate to your `lab-p2` folder in the pop-up.
+5. Ensure that you download the file with the proper extension:
+   * Windows users: ensure that `Save as type` is **not** `Text Document`, but "All Files".
+   * MAC users: replace the `.txt` extension with `.ipynb`.
+6. Press Enter
+
+**Warning**: Verify that your file is saved as `bool.ipynb` and not `bool.txt` or `bool.ipynb.txt`. Reminder: we recommend you use the Chrome browser to avoid issues (other browsers might automatically change extensions of files to .txt when downloaded).
+
+
+### Task 2.2: Open Jupyter in your `lab-p2` folder.
+
+Go back to your open terminal. If you have closed it, review Task 1.2 to verify you are in the `lab-p2` directory. Now run `jupyter notebook`. You should see something like the following:
+
+<img src="images/notebooks.png" width="1000" alt="The file tab opened in Jupyter listing the bool.ipynb, ops.ipynb, and mod.ipynb files">
+
+You can now click on any of the three notebooks you've downloaded to view the contents. The exercises you should do with each notebook are described below.
+
+**WARNING:** Your Terminal window should now look something like this:
+
+<img src="images/jupyter_shell.PNG" width="700">
+
+Even though we'll be working in the web browser now, **NEVER** close this terminal window where you typed `jupyter notebook` until you're done -- if you do, Jupyter will crash and you will lose any unsaved work.
+If you need to use other Terminal/PowerShell commands, **open a new Terminal/PowerShell window**.
+
+### Task 2.3: Complete the `bool.ipynb` notebook.
+
+Open the notebook, complete the directions in each cell, and run the cell. If you are unsure of what to do, ask your TA/PM.
+
+### Task 2.4: Complete the `ops.ipynb` notebook.
+
+The `ops.ipynb` notebook is split into 2 sections. It is **very important** for you to
+carefully go through the second section in particular. As a new programmer, you will learn some very important lessons in this section, which will help you avoid some nasty bugs in your code.
+
+### Task 2.5: Complete the `mod.ipynb` notebook.
+
+The `mod.ipynb` notebook will teach you about 'modular arithmetic' and is also split into 2 sections. In the first section, you will get acquainted with modular arithmetic, and in the second section, you will use modular arithmetic to solve a few simple word problems.
+
+------------------------------
+## Segment 3: Script mode (IDLE editor)
+
+Script mode is the most commonly used mode for writing production Python code (that is code written at a company). In this course, we will only be writing code in notebook "mode". So this section will be the only place where you will briefly learn about script mode.
+
+Now let's look at IDLE, which will help us write a Python script.
+
+### Task 3.1: Open IDLE
+
+Remember that you are currently running Jupyter on your previous Terminal window.
+So, you cannot execute any more Shell commands from that window. Instead, you must
+**open a new Terminal/PowerShell window**, and navigate back to the `lab-p2` directory
+on the new Terminal. Do **not** close the old Terminal/PowerShell window unless you want
+to close your Jupyter notebook.
+
+We will now create a new file called `laugh.py` in IDLE (short for Integrated Development and Learning Environment, but it's a fancy text editor). From Shell mode (that is, not Python interactive mode), type `idle laugh.py`.  This would normally open up a file named `laugh.py` in IDLE if it already existed, but since it doesn't, it will create a new empty file named `laugh.py`.
+
+If you are using macOS, try the command `idle3 laugh.py`
+
+**Warning**: If you are using macOS and the `idle3 laugh.py` command did not work, then directly open IDLE from `Finder` and save the new file as `laugh.py`.
+
+### Task 3.2: Write some code in IDLE.
+
+Paste the following into the editor:
+
+```python
+print("ha " * 10 + "!")
+```
+
+### Task 3.3: Run code in IDLE.
+
+From the run menu, click `Run Module` (saving your file if necessary); or, you can accomplish the same by pressing `F5` on your keyboard.
+
+You should see a new window pop up. In this window, you should see:
+```
+ha ha ha ha ha ha ha ha ha ha !
+```
+along with `>>>` underneath. What do you think those arrows signify?
+
+### Task 3.4: Use Python interactive mode in IDLE.
+
+Type or paste `print("hello")` in the pop-up `>>>` prompt and press Enter.
+
+Now close the pop-up.
+
+### Task 3.5: Investigate how script mode handles code that doesn't use print().
+
+Remove the print in your file, so it looks like this:
+
+```python
+"ha " * 10 + "!"
+```
+
+Run your code again (e.g., with `F5`).  Notice how it doesn't show any of your output? In interactive mode, prints usually aren't necessary, but they are in script mode. Add back the print, save, then close any IDLE windows that are open.
+
+### Task 3.6: Run the Python program you wrote from the shell (back to your terminal).
+
+```
+python laugh.py
+```
+Did it work? If you are on a MAC, try `python3 laugh.py`.
+
+### Task 3.7: `circle.py` program
+Let's try to create a second program:
+
+1. Run `idle circle.py` (`idle3 circle.py` on macOS)
+2. Paste `print((8/2)**2 * 3.14)`
+3. Run the program (either in idle with `F5`, or by exiting idle and running `python circle.py` in the shell)
+
+The program computes the area of a circle. *Can you figure out what the diameter of that circle is by reading the code?*
+
+That's the end of this segment! Take a moment to summarize for yourself what you learned.
+
+---
+
+## Segment 4: Otter tests check for project submission
+
+This segment is informational only and has no tasks. Starting with project P2, your work is not complete when you submit the project on Gradescope. It is your responsibility to make sure that your project clears auto-grader tests on the Gradescope test system. Otter test results should be available in a few minutes after your submission. You should be able to see both PASS / FAIL results for the 20 test cases and your total score, which is accessible via Gradescope Dashboard.
+
+You **must** review the project's rubric and make sure that you have followed the directions provided in the project to solve the questions. The rubric is meant to reinforce your understanding of the project's directions. TAs and graders will be following the rubric to make deductions on your project submission during manual grading. Rubrics will become progressively stricter as we make progress during this semester.
+
+To get full credit for this lab, you must acknowledge to the TA that you understand Segment 4's instructions.
+
+If you finished early, you can get started on [P2](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-s23-projects/-/tree/main/p2)! Good luck!
diff --git a/lab-p2/bool.ipynb b/lab-p2/bool.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Boolean Logic\n",
+    "\n",
+    "Let's get practice with boolean data types and boolean operators. \n",
+    "\n",
+    "Each input cell will contain a statement in English and the corresponding Python variables to represent the sentence. The variables will either have pre-assigned Boolean values (`True` / `False`) or you will have to fill out the values, as per the provided direction.\n",
+    "\n",
+    "Recall that in Python, we use the following syntax for assigning values to variables\n",
+    "`some_variable = some_value`\n",
+    "The `=` is the assignment operator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To pass a class, you must\n",
+    "# a) Show up to lectures\n",
+    "# AND\n",
+    "# b) Do the assignments\n",
+    "\n",
+    "showed_to_lectures = True\n",
+    "did_assignments = True\n",
+    "passed = showed_to_lectures and did_assignments\n",
+    "\n",
+    "# what do you think the value of passed will be?\n",
+    "# your answer here: \n",
+    "passed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "showed_to_lectures = False\n",
+    "did_assignments = False\n",
+    "passed = showed_to_lectures and did_assignments\n",
+    "\n",
+    "# what do you think the value of passed will be this time?\n",
+    "# your answer here: \n",
+    "passed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "showed_to_lectures = True\n",
+    "did_assignments = False\n",
+    "passed = showed_to_lectures and did_assignments\n",
+    "\n",
+    "# what do you think the value of passed will be this time?\n",
+    "# your answer here: \n",
+    "passed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To take this class, you must\n",
+    "# a) take all the prerequisites\n",
+    "# OR\n",
+    "# b) have an exemption from the professor\n",
+    "\n",
+    "took_prereqs = True\n",
+    "has_exemption = True\n",
+    "\n",
+    "can_take = took_prereqs or has_exemption\n",
+    "\n",
+    "# what do you think the value of can_take will be this time?\n",
+    "# your answer here: \n",
+    "can_take"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "took_prereqs = False\n",
+    "has_exemption = False\n",
+    "\n",
+    "can_take = took_prereqs or has_exemption\n",
+    "\n",
+    "# what do you think the value of can_take will be this time?\n",
+    "# your answer here: \n",
+    "can_take"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "took_prereqs = True\n",
+    "has_exemption = False\n",
+    "\n",
+    "can_take = took_prereqs or has_exemption\n",
+    "\n",
+    "# what do you think the value of can_take will be this time?\n",
+    "# your answer here: \n",
+    "can_take"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A car passes its smog test if\n",
+    "# a) Its tailpipe emissions are clean \n",
+    "# AND\n",
+    "# b) It does NOT have a Check Engine Light\n",
+    "\n",
+    "# What values of these boolean variables will result in a pass?\n",
+    "# replace the ... with your code\n",
+    "clean_tailpipe = ...\n",
+    "has_CEL = ...\n",
+    "\n",
+    "# DO NOT EDIT THIS LINE\n",
+    "passes_smog = clean_tailpipe and not has_CEL\n",
+    "\n",
+    "# TODO: Display passes_smog\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You should take your jacket off when going through airport security if\n",
+    "# a) The jacket contains metal\n",
+    "# OR\n",
+    "# b) You do not have a Known Traveler account\n",
+    "\n",
+    "# What values of these variables will \n",
+    "# force this traveler to take off their jacket?\n",
+    "# replace the ... with your code\n",
+    "jacket_contains_metal = ...\n",
+    "is_known_traveler = ...\n",
+    "\n",
+    "# DO NOT EDIT THIS LINE\n",
+    "should_remove_jacket = jacket_contains_metal or not is_known_traveler\n",
+    "\n",
+    "# TODO: Display should_remove_jacket\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# replace the ... with <, >, or == to make the whole statement True\n",
+    "\n",
+    "((5 ... -10) or (20 ... -20)) and (34 ... 34)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# replace the ... with <, >, or == to make the whole statement True\n",
+    "\n",
+    "((1 ... -10) and (30 ... -35)) and not ((356 ... 366) or (-24 ... 37))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check in with your TA and show the last expression. If you have any questions, please ask the TA / PM."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lab-p2/images/README.md b/lab-p2/images/README.md
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+# Images
+
+Images from lab-p2 are stored here.
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diff --git a/lab-p2/mod.ipynb b/lab-p2/mod.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modular Arithmetic\n",
+    "\n",
+    "What time does an analog clock show one hour after twelve o'clock?  Not 13 o'clock (that doesn't exist) -- instead, it wraps back around to 1 o'clock.  This is a weird kind of arithmetic, where adding doesn't always make a number larger.\n",
+    "\n",
+    "This alternative arithmetic is called **modular arithmetic**, and we can use the modulo operator (`%`) in Python to perform modular addition.  However, there's a twist concerning the clock: in CS, we count from 0, so if we were to have a **CS clock**, it would go from 0 o'clock to 11 o'clock (instead of from 1 o'clock to 12 o'clock)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Exploring modular arithmetic\n",
+    "\n",
+    "You'll see many cells of the form `X%12`.  This computation is answering the question: if we start at 0 o'clock and wait X hours, what time is it? The `%12` part means time wraps around at 12 o'clock, meaning that there is no 12 o'clock, just 0 o'clock again (remember we have a CS clock that goes from 0 to 11 o'clock).\n",
+    "\n",
+    "Run the cells in this section to get a sense of how this CS clock works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "0 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "1 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "2 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "3 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "4 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "5 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "6 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "7 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "8 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "9 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "10 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "11 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# wraps back to 0!\n",
+    "12 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "13 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "14 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "15 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "16 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "17 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "18 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "19 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "20 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "21 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "22 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "23 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# wraps back to 0!\n",
+    "24 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "25 % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "26 % 12"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Solve math problems by using the modulo operator\n",
+    "\n",
+    "For each question below, write a Python expression using `%` to answer the question. The first two questions here have already been answered. Answer the remaining questions in this section by yourself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# What time will it be 6 hours after 9 o'clock?\n",
+    "\n",
+    "(9+6) % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# What time was 2 hours before 1 o'clock?\n",
+    "\n",
+    "(1-2) % 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What time was 4 hours before 2 o'clock?\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What time will it be 12 hours after 6 o'clock?\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What time will it be 13 hours after 6 o'clock?\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What time will it be 24 hours after 6 o'clock?\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What time will it be 25 hours after 6 o'clock?\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check in with your TA and show the last expression. If you have any questions, please ask the TA / PM."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lab-p2/ops.ipynb b/lab-p2/ops.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..56511037a93a57686acbd264898b81692dd0fbfe
--- /dev/null
+++ b/lab-p2/ops.ipynb
@@ -0,0 +1,318 @@
+{
+ "cells": [
+  {
+   "attachments": {
+    "precedence.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Order of Simplification / Precedence\n",
+    "\n",
+    "Python has operator-precedence rules to determine which operator to execute first if an expression contains multiple operators.\n",
+    "\n",
+    "Ordered from highest to lowest precedence:\n",
+    "\n",
+    "<div>\n",
+    "<img src=\"attachment:precedence.png\" width=\"600\"/>\n",
+    "</div>\n",
+    "\n",
+    "Of course, the Python programmer (you!) can add parentheses to change the order in which the operators are executed, thereby changing the result."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rules for order of Simplification / Precedence:\n",
+    "    1. First work within parentheses\n",
+    "    2. Do higher precedence first\n",
+    "    3. Break ties left to right (exception: exponent ** operator)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Using parentheses, fix each cell to get the correct result.\n",
+    "\n",
+    "For each of the following problems, you are only allowed to add parentheses, and you need to get the expression to evaluate to the required value.\n",
+    "\n",
+    "For example, for the first problem below, `3 ** 4 - 1` evaluates to `80`, so you should change it to `3 ** (4 - 1)`, thereby getting `27`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "80"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Add parentheses to get 27\n",
+    "3 ** 4 - 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-4"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Add parentheses to get 0\n",
+    "-2 - 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Add parentheses to get 0.5\n",
+    "1 / 1 + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "101"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Add parentheses to get 1\n",
+    "100 + 5 % 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Add parentheses to get False\n",
+    "not True or True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Add parentheses to get True\n",
+    "False == True and False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Correct way to write boolean expressions.\n",
+    "\n",
+    "You should always write boolean expressions in an expanded manner (examples below).\n",
+    "This is a **very important** lesson to learn early as a new programmer.\n",
+    "Make sure you go through this section very carefully, and flag your TA/PM if you have any difficulty with this section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Is 3 + 4 equal to 6 or 7? In English, you would say that this statement is True.\n",
+    "# Now let's learn the improper way of translating this into Python expression\n",
+    "\n",
+    "# Incorrect way\n",
+    "3 + 4 == 6 or 7\n",
+    "\n",
+    "# In the above expression, what is the operator precedence? Please go back and refer to the precedence table\n",
+    "# Now, run this cell and see the strange answer you are getting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Why did we get 7 for the above cell's output?\n",
+    "\n",
+    "# Operator with the highest precedence: +\n",
+    "# So, you evaluate 3 + 4 first and now you get:\n",
+    "7 == 6 or 7\n",
+    "\n",
+    "# Operator with te next highest precedence: ==\n",
+    "# So, you now evaluate 7 == 6.\n",
+    "# 7 == 6 gives you False\n",
+    "\n",
+    "# Operator left behind: or\n",
+    "False or 7\n",
+    "# This here is bad! You should never compare a non-boolean value with a boolean operator!\n",
+    "# So now you know why you got 7 when you executed `3+4 == 6 or 7`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What will be the output?\n",
+    "print(False or True)\n",
+    "print(False or False)\n",
+    "print(False or \"hi\") # bad comparison\n",
+    "print(False or 7) # bad comparison"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Important lesson: never use boolean operator on a non-boolean value\n",
+    "\n",
+    "So, always write boolean expressions with proper expansion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Is 3 + 4 equal to 6 or 7? In English, you would say that this statement is True.\n",
+    "# Now let's learn the proper way of translating this into Python expression\n",
+    "\n",
+    "# Correct way\n",
+    "3 + 4 == 6 or 3 + 4 == 7"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Translate this into Python expression\n",
+    "# Was today's lecture instructed by Mike or Gurmail?\n",
+    "\n",
+    "instructor = \"Mike\"\n",
+    "... == ... or ...\n",
+    "\n",
+    "# Try changing the instructor variable assignment to \"Gurmail\" or \"Andy\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**LESSON**: It's tempting to chain a bunch of `or` operators together when trying to compare one value against a bunch of values. But this:  \n",
+    "\n",
+    "```\n",
+    "x == a or b or c or d\n",
+    "```\n",
+    "doesn't work. You need to individually compare each value:\n",
+    "\n",
+    "```\n",
+    "x == a or x == b or x == c or x == d\n",
+    "```\n",
+    "\n",
+    "The same applies for the `and` operator as well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check in with your TA and show the last expression. If you have any questions, please ask the TA / PM."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/p2/README.md b/p2/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..c6c35f81750866729c3b32f29fe7e0468a132f9d
--- /dev/null
+++ b/p2/README.md
@@ -0,0 +1,43 @@
+# Project 2 (P2)
+
+## Clarifications/Corrections:
+
+* None yet.
+
+**Find any issues?** Report to us:
+
+- Ashwin Maran <amaran@wisc.edu>
+- Brandon Tran <bqtran2@wisc.edu>
+
+
+## Note on Academic Misconduct:
+Starting from P2, you are **allowed** to work with a partner on your projects. While it is not required that you work with a partner, it is **recommended** that you find a project partner as soon as possible as the projects will get progressively harder. Be careful **not** to work with more than one partner. If you worked with a partner on Lab-P2, you are **not** allowed to finish your project with a different partner. You may either continue to work with the same partner, or work on P2 alone. Now may be a good time to review our [course policies](https://cs220.cs.wisc.edu/s23/syllabus.html).
+
+
+## Instructions:
+
+In this project, we will focus on types, operators, and boolean logic. To start, create a `p2` directory, and download `p2.ipynb` and `p2_test.py`. Make sure to follow the steps mentioned in [lab-p2](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-s23-projects/-/tree/main/lab-p2#task-21-download-boolipynb-opsipynb-and-modipynb) to download these files.
+
+You will work on `p2.ipynb` and hand it in. You should follow the provided directions for each question. Questions have **specific** directions on what **to do** and what **not to do**.
+
+After you've downloaded the file to your `p2` directory, open a terminal window and use `cd` to navigate to that directory. To make sure you're in the correct directory in the terminal, type `pwd`. To make sure you've downloaded the notebook file, type `ls` to ensure that `p2.ipynb` is listed. Then run the command `jupyter notebook` to start Jupyter, and get started on the project!
+
+**IMPORTANT**: You should **NOT** terminate/close the session where you run the above command. If you need to use any other Terminal/PowerShell commands, open a new window instead. Keep constantly saving your notebook file, by either clicking the "Save and Checkpoint" button (floppy disk) or using the appropriate keyboard shortcut.
+
+------------------------------
+
+## IMPORTANT Submission instructions:
+- Review the [Grading Rubric](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-s23-projects/-/tree/main/p2/rubric.md), to ensure that you don't lose points during code review.
+- Login to [Gradescope](https://www.gradescope.com/) and upload the zip file into the P2 assignment.
+- If you completed the project with a **partner**, make sure to **add their name** by clicking "Add Group Member"
+in Gradescope when uploading the P2 zip file.
+
+   <img src="images/add_group_member.png" width="400">
+
+   **Warning:** You will have to add your partner on Gradescope even if you have filled out this information in your `p2.ipynb` notebook.
+
+- It is **your responsibility** to make sure that your project clears auto-grader tests on the Gradescope test system. Otter test results should be available in a few minutes after your submission. You should be able to see both PASS / FAIL results for the 20 test cases, which is accessible via Gradescope Dashboard (as in the image below):
+
+    <img src="images/gradescope.png" width="400">
+
+   Note that you **cannot** view your final score, as TAs haven't manually reviewed your code yet. So, do not worry if you see `-/100.0` as your score.
diff --git a/p2/images/README.md b/p2/images/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..f3a4efb2ee19e093a93488e1d3a14cd4d0d87af8
--- /dev/null
+++ b/p2/images/README.md
@@ -0,0 +1,3 @@
+# Images
+
+Images from p2 are stored here.
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diff --git a/p2/images/syntax_error.PNG b/p2/images/syntax_error.PNG
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diff --git a/p2/p2.ipynb b/p2/p2.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..132261d6091673293f8cd288289e70ddf2ce88e4
--- /dev/null
+++ b/p2/p2.ipynb
@@ -0,0 +1,1517 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "045e54a0",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# import and initialize otter\n",
+    "import otter\n",
+    "grader = otter.Notebook(\"p2.ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2cd9484e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "import p2_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8010d415",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# PLEASE FILL IN THE DETAILS\n",
+    "# enter none if you don't have a project partner\n",
+    "\n",
+    "# project: p2\n",
+    "# submitter: NETID1\n",
+    "# partner: NETID2\n",
+    "# hours: ????"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f792915f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "# Project 2: Operators, expressions, and variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "035a6df2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Learning Objectives:\n",
+    "In this project you will demonstrate your ability to:\n",
+    "\n",
+    "- Use arithmetic operators, including the floor division operator.\n",
+    "- Call the type function on an expression\n",
+    "- Use logical operators such as `and`, `or`, and `not`.\n",
+    "- Use comparison operators.\n",
+    "- Store values and results of expressions into variables."
+   ]
+  },
+  {
+   "attachments": {
+    "correct.PNG": {
+     "image/png": 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+    }
+   },
+   "cell_type": "markdown",
+   "id": "0a949a1b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Testing your code:\n",
+    "\n",
+    "Along with this notebook, you must have downloaded the file `p2_test.py`. If you are curious about how we test your code, you can explore this file, and specifically the value of the variable `expected_json`, to understand the expected answers to the questions. It is okay if you do not understand how this file works for now. We promise that you will be able to understand everything going on in that file by the end of this semester.\n",
+    "\n",
+    "In the meantime, after answering each question (say Question 1), you can test your answer directly on the notebook by running the cell below that question which says (in the case of Question 1) `grader.check(\"q1\")`. If you have answered the question correctly, you will see the following:\n",
+    "\n",
+    "![correct.PNG](attachment:correct.PNG)"
+   ]
+  },
+  {
+   "attachments": {
+    "semantic_error.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "4992071a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Instead, if you make a semantic error, you might see an error message similar to the one below:\n",
+    "\n",
+    "![semantic_error.PNG](attachment:semantic_error.PNG)\n",
+    "\n",
+    "You can ignore the first few lines of this message. You need to focus on the very last line here. That is the line that begins with <b style=\"color:red\">ERROR:</b>. This message will tell you what is wrong with your code so that you can hopefully fix it."
+   ]
+  },
+  {
+   "attachments": {
+    "syntax_error.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "c467a7d7",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "On the other hand, if you make any syntax or runtime errors, you might see an error message similar to the one below:\n",
+    "\n",
+    "![syntax_error.PNG](attachment:syntax_error.PNG)\n",
+    "\n",
+    "Try figuring out by yourself, what this error message is telling you. As the course progresses, you will learn how to read the Traceback from your error messages. For now, try to avoid making syntax errors, and if you are unable to fix your code, attend office hours and have a TA or Peer Mentor look at your code."
+   ]
+  },
+  {
+   "attachments": {
+    "add_group_member.png": {
+     "image/png": 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OAzRAzfTv4uioqqJCZ4k9zPoPAAAAAADQibQxXsaXkLiTmNjaBTEpZurqG4cMGWdnt2noUHdDQzpXQoLH599LSmrh2E4+n3/dz2/Vpk2hERH9e/Wa98UXMyZPlpOT23f06O4DByqrquhy7cDj8U7++29BYeHksWPnTJ+uqaFRLZbd3yJjYysqK2t4vICgoFaNjSU3GB4ZGRMfT6cBOolONNO/MMz6DwAAAAAA0Fm0MV6WU16eVFREJ1rDTF193aBBGvLyZF9OWvr7/v1dDAyoQwSpk9RMJ5p16969sxcv9nRx+XXt2rEjR/Z0de3Xs+d3ixbNnjYtNiHh6bNndLl24FZXl5aVWZiaDh4wwMHWVkdLa/OaNbOmTmUymXQJMVBZVfUkOLirhYWbs3N4ZGRefj59AODD5XPzZmeZ6V8YaTBpNmb9BwAAAAAAEH/MtWvX0rutEZOXdyshgVe/fxmDwVBgsZrpdCYcLKNIM5lx+fkxr6I85Fw7bW2jN42xys3LO3LqlHmXLnOmT2exWHRuXQMM9fU9Bw4069KFyqmqqjp76dJfx46dvXjx9v37PB7P1NhYEPDae/hwUGiomYnJwX/+OfDPP5d9fdMzM8l3WmlpaZK/dsuWwqKijKysi9eukZpNjIx+3bWrsLjY2tKSOj06Lm73wYPHTp/29fMrLS9nSkr++MsvOlpa+rq6gsrdnJ2pwgSp5/yVK67OztJSUtS+uanp4ZMn7zx86OLkRDL5fP7TZ892Hzhw4t9/r9y4kZKWZmFqKi8nR5/fmBcxMeS+Ph02zN7G5v7jxyoqKuQU+lj9K1I5pEnb9+whrXoRHS1yg9R9Nf/EiJbctYGe3o4//4yKjXV1ciI3RXb2HjlCTrl0/To5qqutrSU0FFekQk119achIc4ODlSFRHFJyd9nz5ImkXu57++voKBA3mXSYHIoMTl5w/btpIX+QUF/HjliaWampqpKnfUucTgc8pqdnU0l20a37n5reDxyg1SO+Cgtq1RRev1j2yrkjh74+ysqKLh3705ntQ/5PJCfGo/+/e07W7xMSkqqrLz8go/PJ336yMrK0rntwOZwyE8N+ckV/FLqEG+p2s4lKyfn7qNHwwcP7thfKW+p2s4lJj6e/Nof5uHRIT8FAAAA7deev3XfHuorRodgMNq1yh/AR6uNPzlZ5eWc+jP9y0pJLe7V68zkyV84O0tJNlJtw2AZEV9Q8EhodFI1j1fYgqGUYZGRpWVlg/r3Fw6WURgMBvlSSu0XFRf/smvX46dPB/btO++LL1ydnK7cuLHzr7+ER2smJCeTMgZ6erOmTiXf54PDws5fuULyzUxMZk6ZoqOlZWFqSs51cXSkygs8j4zc8eef1dXV0yZMmDphQk5u7l/HjnG5XPpwC+QVFPy2b5+KktIoT0+WtDSfz7/s63v45EkbK6u5n38+eezY3Pz8X3bvbqbLGDklIChIU13dzsami5GRWZcuz1+8aGEbGr3BNz6xltw1eaQ79+2zt7YmbxBJ3rhzZ8fevVoaGl9OnUpuisPl7j18OOnVm05VSOqfMm4cqbC4pESkQnL7m3fuTE1P/8zbmzTJ0c7un7NnSasEI095PN7RU6deREePHzVKW0uLyoQPWKeb6V8YZv0HAAAAAADoFNoYL8soKaH36shKSc13dx9qacmQkJjg4DDNyUkkZNZUsOwnP7/8yko6XUek5kYlpaQoKykZCQ3kbNSt+/fzCwu/mT+fGrD5xaRJX06ZEp+Y+OjJE7qEhASTyVz+9ddUAXK0m61tanp6FZutrqbm5uxMrkJ2yCFDfX36hDpcLvf67dt6OjorFi/26Nevj7v74nnzPh02jD7cMjU1NbOmTiWtsrO2Js2IS0y8ff/+wlmzSA65Iql2zbJl+jo6py9c4DWxwkBefn5sQkJ3R0dVZWUWi+XcrVticnJyWhp9uFmN3mDzT6yFd03uZcmCBaQGSzMzknSwtf2/uXMXzJxJyg8eMGDJ/PmSkpLP64akCSr837Jl5BApMH/GjPGfflpXzUvkxsntk4dAHgW5ItUk8ojIgyKPiypDHiO5kW8XLuzt7q4ofj2zoGN1xpn+hVGz/pNbIDdCZwEAAAAAfDT4fP5//g8Gr1l+/vF9OqsJF588OuZ3g1dbm5CVOWbjGo9VSxtuK47tZ1c3+Xc1t7r6QsDDKVs3kpLD1/3w08lj6fm59LH6SKv2Xb+889K/ZCcwLkb4EsLbbxfP0Sc0porLOXDj6rjN/yMlR61fufvqhfzSUvqYhMTDqPBfz58iTaLT0Bl0TM/MwRYWQ1+N3GkYMjNXV98wZIhIsCwqL29dg2BZC/F4PEkGg2x0ujGlZWUhz593d3Q0fzU2k3B2cDA3NQ178ULwMe1iZCQYG8hkMrsYG1P7zcvMyUlNT+/To4eioiKVw2AwSFUN+7s1Q09Hx1houYNnz58bGRhYWVjQaQkJUlv/Xr3IhUqEfsyEPQsPr6ysdLSzo5KOtrby8vKPnj4V9L1qlTc+sRbeNcnR1damExIS+rq6Dra2dEJCQlVFRUdLK7Nu6GJufn56ZqZIhfo6OoIK8wsLk1NTB/brJ3wJSzMz8uiiYmOppIyMTDdbW8nGujTCh0dWRmb9ypULZ8+m050QaTy5BXIjdBoAAAAA4ONAvqg+jn7x142rtW9aOTAtP+/kfT9FWVnmqy96blbWG6fPEtmmfTJYmilNFRBRyeGsO3Vs99ULXbR1vh83eVzPfs9TEhbs/S0gppHZhEsqK0KTEnpb21HT/hAjXHuKXItsI9x6UkcbyispXvzX7jMP77pb2ayaMM2jm7Pvs6cL9+1MyMqkCqgrKj+Kingc84JKQqfQMVGG/IoKnlCMRjhkZq6uvn7IENX605RE5eVtunu3oE3BMoLJZNby+WSj040pLSsrr6iwMDUVfOIJFotlaWaWm5fXzgU0SeXk6iKdztqDW12dnpn5Ijp63tKlMxctEmy/799fXllZ3FifO2qmf7MuXboYGVE5WpqaDra2pJKi4mIqp1Xe+MTadtek8eQuZi1eTG5nxfr1kTEx9AEJicLi4hoer5kKc/LyiktLd+zdK/xM5i9fHhMfT0XcAOAduHjtmoWra1PbuBkzRHIE25pNm959T7rdBw6INEOweX322WezZ4tkCjZxa63nhAme48eLZAo2ciJdxbtCfpmT38AizRBsi1esGDFpkkimYHv3rU1MSRk1ZYpIMwTbwu+/F8kR3lZu2CCSI9iaf1Oar7aZnxRxe3rNv9dt/kkRwzclOCyMbhwAALwTAbFRG878I0++ZEo3HuSi8Pl832dPWVLSfW0d6CwJCT1V9V7WdiKbnbGpIKAmjFdbe9TPNzghdsW4KRunz/Ls7jbbc8TeBUv01DR+u/RvWn4eXe6VpJxsJoNhafC6O4upjq7Itchmqf+6gDB2Nef3y+fzSop/mTn/+3GTBzl1XzJ6wvZZX9fW8rZdOFNSWUHKWOjqO5paXHn6uBIDTToPRtv6Il2JidkTEEAn6roFjbK2nuPmRj5kdBb5lEtIBKanW2lqtipY9lXPniO7dqUTTbh5797p//5bOHu2k709ndVAembmL7t2TRg9ul/PejHg/65e9Q8MXLl0qaqy8t7Dh0nOgpkzqUME+Vr4Ijp6yYIFcrKyVWz2jr171VRVqQJU0s7aevTw4c8jI/88cmTRnDk2QvNhJyYnb92z58spU1ydnEiy+cqF98khbnX173/9Rd6LmVOmiPzAMyQlFRUUpBosyhmbkLB9zx4dbW0joXhTQVFRTHz8rKlT+/ToQZIiVyGCQkNPnT+/aulScl8iN/jGJ5aant7au67h8fYcOhQdFzdy6NButrbZubn3/f3jk5IcbGxImTc+xpcFDh+eMXkyNbRTGPkdqyAvTwr/cejQ119+KVjh4b0oresAGBoaSiXbxqnuAZIvIWI4C1taZr6R/uslGlqF3NHGbdvIB7UNncLIN5m1W7bs3LzZzMSEzvoQtfk2yXfaJatXe3t5kV9KdJYYe0ut/Ug+JM17Sw+B/Atywcdnx4YNnWL4c+dqbZu9pdtsc7UfzA9ge/6pAoAPQHv+1n17qK8YHYIhSU/w/X7FpKeuOX5IU1l1+sAhv/x7iryO7f1ywuuGcouLlh/e18fGbq7nSAaDkZCVufzQ3gH2jt+MHk+XeJPUvNzlh/a4WHRd5j1R+Kt0SGL8imP7x/fuP2uIl6CbCPka/vvl87IsFnW5wLiY74/sWzjCu6nmNURVS+5oSv9Bwr1PfIKfbL9w9tsxn3l2dyPJh5HhG88e3zR9trPZ61FlIM7a2L9MT1FR5tW0+gT5hF2Ojt4fGCjSy8zd0LBVwTJSJ6mZTjTN0dZWWUnp9v37IjPNE6QlNXULEZACigoK8UlJwgFBUj4uMVFbS6v5RSffiFQuyWCkZ9JdK9uPJS2tpaGRk5dHqlVTVRXeVJWVGwbLyE09evqUw+WmpqeTHcEWHRdHHWr4ZN7ojU+sDXddUFiYlJz86bBhXoMHG+rruzo5LZgxQ19Hhzr6xgrVVVWlpKSycnJEngnZFOoP7wUAAAAxxOZw1mza9O57yQEAwLuXnp/708ljw9f9MHjN8q/2/haekkhNPRYY93KMEaem2kRbd92UGdoqalT5pkSlp+aWFLlb2QjHnlolKC46v7R0gL2jyFdpK31DawPj4IS4cvbrAWdF5eUvUpPbfDny9fl+RJistHSPBjW4mFnpqKg9iYni1sUoLPUM1RWUSGHhb9wgztoYLzNWVVWpHwgjb3nDkJmINw7DVGSxDFrwf5vaWlrDBw2KjIn5+8wZ4cAQaUNAcPDSNWvuPnqkrKTk3K3bs7Aw4aXoQsLDE5KSHO3smu//+Ub6dVOPPXrypLy8nMohl05OSxOJUhUVF1ex2dR+ZVWVYMqtRvVycyO1kZYLj+VOSU9vdOhoXn5+eGSkk739gd9+O7xrl/Dm6eEhPOt/IWnDqxpqeLzQ8HBqv6E3PrEW3rUwKSkpRtMzizWskMjOzRVUqKejY2VuTgpk5eRQOQT5yzs5NZVOAAAAgBjLzM5Oy8z0GjKETgMAwAcqLjNj6cE9D6PC+9rYL/Oe2NXAaO2Jo6cf3uG/HHj2Urcu5j9/MVdLRZVKNoV8xwxNjNdQVNZX06Cz6mNXc8qqKkkxOt0AOZSUk62mqKSrpk5nvSIvI2OkqZVdWJArNIVRZFqyFJNpodf4NEGVHE7zIyjZ1dyMwnwNJWVNZdFQhrKCvIGmZmJOZkVdeE5NSYk8luiMNOFoHYizNsbLNBUUjBsEtsjnspmQWUvmLDNVU9NpQf8yYvCAARNGjw4IDv527drzV64EBAU9CAj4Zdeu/ceOWZiaunfvTsoM9/DQ1tT87c8/qQJHT506dOKEhZkZNVaxPVgs1jAPj4ysrJ+2bbt1796jp093/Pnn7fv3hYdSmpqYxCclHTt9mlz62u3b6379tUwoKtSQpZkZ+Wvy6s2bv+7eTe6F1Ln38OENW7cGNTbKLyYhobikxNXJidmg61n3bt3Ia9iLl/MImhgZkWLkrkltfg8erN+6lfzNWlfqJZa0tIK8fEJSEmn5nYcPydvX/BNryV2LUFVWtrO2/u/qVVIVqZC0YeuePelZWdTRhhWSWz55/jx1lCB395m3N0NScuP27VSTyJNcvXHj0dOnyyteDgIHAAAAceZz86aRvr6+ri6dBgCAD1Elh7Pv+kWys2vuolUTpw13cV/86bijS34gOaVCEYBmvjkKkKqScrKNtbVVFOuNKKrm1fgEP5m8db3X2hWjN6z2+mnFft8rVdxGIlmcGm5+WYmSnJyKvAKd9QqDwdBWVSuprCgsL6NyeLW1AdGRzmYWSnL1LldVzT3md8N74+qRP60gG9m5EPCw0dUt2VxubnERqVaWJdophyUlra6oVFJRUVj28nIsKSlTXb2swvyc4iKqAIi5NsbLGBISA83MBCtgCjQVMmtJsIzJYAwwNW1hD0jyQR/m4bFx5Uone/v7/v77jh49cvJkaVnZnOnTF86eTQ23VFRUXP71173d3e88fEgKBIWGjhw6dPHcue0cjEnpZme3ZP58aSmpE//+e/zsWWMDg+kTJzKFxqh+0qcP2chF9//9d2BIyMzJk6koXlPIHY3y9Jz7+edFxcWHT5w4dPx4XkHBkgULRGYTI7hcrn9goI6Wlp2NDZ0lpIuRkVmXLs/CwopLS+1tbLy9vBKTkw/+88+NO3e8Bg8ml6DL1UWjRnp61vL5x8+di4qNJdW+8Ym98a5FkEtMHT++T48ejwMDyXMgzZ46bhw5iz5cV+Gyr74i9ZMKj50+raCgsGDmTJbQaphamporFi92tLf3vXOHNIn82d3d0fG7hQsVFUR/9wEAAIBYKS4pCQ4Lc3V2xqLAAAAftvis9IjUlBFuvawM6MXoCEVZuYl9P1Fu5UQ65VVVeSXFmkoqMlKvvxUS14KfXnnq//lAz43TZ62aMM1Sz+Dkfb8Np/9p7fT5CvX/ScovKYnOSO1r93phAcrhW9ej01MXjRhLLrdszERVBcXfL5//68bVGh6PLtECTElJWel6d6GtolpaWVnUbE8aEB9tnO+fqKquXn3zZlSe6NISxMvQj9D0/y0JlhE2WlobhgyRa99IyfcoPilp5759cz7/vJutLZ31Efg471oE5vtvBub7f6M23ybm+yc+kg9J897SQ8B8/2Ko083331neF8z3D/CRw3z/7Xf+8f091y79MmNed/PXK7kR1FT9KydOc7N8vaYfldnUfP8NZ/fncLkPoyIkJRkD7J0kX00QVsPj/Xn9Mrnu8rGfebnUG0DGruasO3ksq7Bgx+yv1RSV6NxX/r5z8/Cta1tmzKOadC8i7D//hxunz1J4Nd9UWVWl3/MQE21dxy5mgvnIKsm/FGf+CU6I3fz5HJHZ+ovKy5Yc+ENPXePHyZ/LStcLxvFqa389f+pJTNTWLxeY1433pBYTWPzpuNE9+lBlQJy1sX8ZISctPalbN9nG+hYJ9zJrYbCM1ENq60TBspfzcWRk0AkJidra2ocBAdLS0rriF+zoQB/nXQMAAEBrsTmcoJAQF0fHDzuICQAAFCXZRsY/dggZFmuQY/eBDs6CYBkhxWR+6t5bU1k5KDaGmk1fQEaKpamkIhgFKYzP5+cWF6kpKmnV/dtETrwXHtajq40gWEYoycmP7tHHydRcECwj5GVkxvcZQHaC4qKpHAFZFktbVS2rsKCKIzqvN7emurC8TF1JSUNZmc6qw2tNJzV4j9oeLyNcDQ2HWVkJf4wEyAfxUlTUZydPfn/9+huDZaQGUg+pjU6Lvdra2lv37m3Yvp2alutBQAA16dgnffpoaYrdf010lI/zrgEAAKANMNM/AMDHg8lklrGrSirf6RzTSnJy8jKyVdWcWn698BODwbDQNyCNSS8QHQxXyeGk5edpq6ppKL0MYOWVFCXlZrmY1esT1xRlOXk5aVbFqwX9BORYMkaa2nklJdnFhXTWK6UVlRn5+SZauqSddBZ0Ku2KlzEkJGZ0796j6ThXZXV1jdBqj00hNZB6Gom6iStJSclJY8aM8fIKe/FCMHXa7GnTRg4d2mj08MPwcd41AAAAtAFm+gcA+HiY6uhKMZmBcdEi0z3xJUi6dRNAybKkZRoMO2t0Qcy84uLCsjIVeQVppmh5ZzNLTWXlgJhIkenGYjPTozNS3S27UrP7hyUnaigpGWppU0cp5ELkcnRCSFZRQRm7SqPBIphEX1sHHr/2WUKcSCODE2NzSor62NizXg3L49W+bI++BrqbdA7tipcRLCZzad++zvqNr73aEuRcUgOph053Ei/Xdhw0aPv69Yd37Tq4c+fGVat6ublJtmC9j07t47xrAAAAaBVqpn/vESMw0z8AwMfAQs/Q3tjkdlhwbEYanSUhUc6uOvPwrvD6mC2hICunICubV1rC4dLDG0MS4+fs2no1KKBWKBpVyeEcvXODy6v5xMGJWnaTHK1gs6mIlZGm1lBntzvhoffCwwQxrLyS4r0+FzWVVLxcX853xq2ufhIT1dvaXr7+P1W+IYHkcoGx9WJ/5NxjfjeUZOV6W9tROdyaGnY1vdSAnZFJH2v7cw/vhSUnUjlEXGbGkVvXHEzM3Lu+XqYvPT9PjiWjgsXrOokOiHQoslg/enh429o2XC6zeaQ8OYucS2qgswAAAACgk7v3+DF5Ne/ShUoCAMCHTV5GZt6w0WRn0V+7Np7551rw052X/v1ix+ZaPr+162PKy8iaaOlmFxVUVdPxsi7auuZ6BtsvnJ26bcMfPhf9nocc8L06a9cvT2KjJvUd6Grxctp+bk3N5rPHR29c7RcWQpIMBmPKgMEu5lab/z2x6u+Dvs8CySkL9u7IKipYONJbR1WdlMkuLiJXcbW0fnkNIQ4mpioKit8f/WvO7q2HbvqQy5F7IftpBXlzho0009UjZTIK82f8tuXLnVvJDkmypKXnDx+lpaL63eE/t/x78nbosx0Xzy49+IekJHPhCG8lObm6il/2XEvJzVFVUFRXqDedGYitjukZxGIy57q5bfH0NGzxlK6kJClPzup0PcsAAAAAoCmY6R8A4CNkqW+wfdZXfW0cHkZFbLtwJiYjbe2UGZP7eTBeTuPUCiwpKXuTLtlFRVlFBVSOmqLi+mlfbvx8tr6axpVA/w2n//434IGhhtbvcxd+MchTqi6ewJSU1FRWUZCRVX7VdUteRubHSZ8vHOGdnJuz5d+T5JRuJuZ7F3zTs6stVSAoLlpXTUNXVY1KChhoaO2au+jbsZNYUtKnHtwhl7sVFtzd3Gr/10u9XHpQMxHJSrPUFRXJRnaos7RUVHfOXTix7ydPY6M2nv3H73mIZ3f33fMWU8tiUsrZVfHZmV0NjNSURFftBPHUkSPpbLS193l7rxs0qJuublN9zUg+OUrKkJKkPJ0LAAAAAB+EF9HRoRERmOkfAOBjY6ip/b/Jn1/78edb67fuWfCNg4kZfaA+cz39/1atH9u7P51uwNHUgiUlFZqYQKclJCQZjF5dbbfN+opU7rdxO3n9deZ8W6Mugnm0mZKS84aNurh6g5vly+5mFJa0tHfPvieWr6JOIW0jLaSPSUiQBqybMoOUodNCSOZwF3dyCzd++pWce3nNpv9NqneuhpLy7vmLyUatG0CRY8nMHjri3xU/UacsHOGtWX9ZzKScLLK5WnUVTGcGYq4j42UE+bS6GRr+7On537Rp+7y9F/fuvaBHD2oj+ySH5JOjpAzmhwcAAAD48PgHBjrZ22OmfwAAaBsjTa3u5lZPYqIaLkbZefH5fP/oSG0VNVdzKzoLxF4Hx8sEmAyGkYqKp6XlKGtraiP7JIfk0yUAAAAA4MOCmf4BAKCdWNLSn/boFZeZHpIYR2d1frklRQ9ehHt2d9VSUaWzQOy9rXgZAAAAAHxsMNM/AAC0n52x6VBn14eR4dW8Gjqrk3sUGaEgKzusu7tgDCmIP8TLAAAAAKBjjB4+/PCuXZjpHwAACGqqMuE5xVpIismcO2zkkk/HSzM/kKm+vHv12/rlAuH5zkD8IV4GAG8Rm8MJDAl5EhxMp1sjJj4+KTU1LTOTTn+g2nybJaWlEVFRwWFhdFq8vaXWfiQfkua9pYdA3izylpE3jk6Ltza3lpw4asqUxJQUOi0hsfvAAQtX10Y3zwkTPMePF8kUbAu//14kR3gTfPjJb8U1mzaRq1DJVnlLb4o4vNcXr10TeWLC28oNG0RyBFuHvCkAACCG5FgyMix69ckPgCSDoSQnRyegk2Dw+Xx6FwDapLTuO0ZoaCiVbBsnJyfySr5HaWtpUTniIy0z30hfk060Ermjjdu26WhrL5w9m85qMfJNZu2WLTs3bzYzMaGzPkRtvs3ikpIlq1d7e3mNHj6czhJjb6m1H8mHpHlv6SFcvHbtgo/Pjg0bOkVHoTa39t1/hNrzW/EtvSnv8b3GjzAAiIP2/K379lBfMToEQxKrMQK0BfqXAQAAAMB74B8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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "c69cd294",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Submission and Grading:\n",
+    "\n",
+    "After you finish this project, you will have to submit it via [Gradescope](https://www.gradescope.com/), just as you did for [P1](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-s23-projects/-/tree/main/p1). Remember that your final score for the project is **not** the score you see on Gradescope after the autograder runs. TAs and Graders will **manually review** your code, and deduct points if you do not satisfy the requirements of the Grading rubric. Grading rubric is available on the [Gradescope](https://www.gradescope.com/) project page. \n",
+    "\n",
+    "After you finish answering all the questions, just click on `Kernel` -> `Restart & Run All` instead of running the last couple of cells. This will ensure that all your cells are run fresh, and that your notebook is saved before it is exported for submission.\n",
+    "\n",
+    "If you completed the project with a **partner**, make sure to add their name by clicking \"Add Group Member\" in Gradescope when uploading the P2 zip file. It is **not** enough if you include this information in this notebook. You **must** also add them on Gradescope.\n",
+    "\n",
+    "<div><img src=\"attachment:add_group_member.png\" width=\"800\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21922cd2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Project questions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3349682e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This line is a comment because it starts with a pound sign (#). That \n",
+    "# means Python ignores it. A comment is just for a human reading the\n",
+    "# code. This project involves 20 small problems to give you practice\n",
+    "# with operators, types, boolean logic, and variables assignment. \n",
+    "# We'll give you directions on what to do for each problem."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3542575a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 1:** What does the expression `44 * 5` evaluate to?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "038b84b8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "course_num = 44 * 5 # we did this one for you\n",
+    "course_num"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40c6f679",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff5053e2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 2:** What does the expression `350 - 31` evaluate to?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e54e7955",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with the correct expression, similar to the answer for Question 1.\n",
+    "# INCORRECT ANSWER: grad_course_num = 319 --> this is considered HARDCODING.\n",
+    "grad_course_num = ...\n",
+    "grad_course_num"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9fd46a9f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cde80ba6",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 3:** If you have 2023 eggs, and can put 12 eggs in one carton, how many cartons can you fill completely? Write the appropriate expression to answer this question.\n",
+    "**Hint**: Use the floor division (`//`) operator to answer this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df17532a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with the correct expression, similar to the answer for Question 1.\n",
+    "# INCORRECT ANSWER: num_cartons = 168 --> this is considered HARDCODING.\n",
+    "num_cartons = ...\n",
+    "num_cartons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aab094d4",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d68c1d10",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 4:** What does `type` of `22 * 10` evaluate to?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e52c13d6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "data_type = type(22 * 10) # we did this one for you\n",
+    "data_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c22e35a0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78524e32",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 5:** What does `type` of `220 // 9` evaluate to?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b86d2ce0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with the correct answer, similar to the answer for Question 4.\n",
+    "# INCORRECT ANSWER: data_type = int --> this is considered HARDCODING.\n",
+    "data_type = ...\n",
+    "data_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b2133ada",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ea54254",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 6:** What does `type` of `2200 / 10` evaluate to?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9fd32976",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with the correct answer, similar to the answer for Question 4.\n",
+    "# INCORRECT ANSWER: data_type = float --> this is considered HARDCODING.\n",
+    "data_type = ...\n",
+    "data_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b80cefbb",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a23656a2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 7:** What does `type` of `\"220\"` evaluate to? Note the **quotes**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "98346c46",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with the correct answer, similar to the answer for Question 4.\n",
+    "# INCORRECT ANSWER: data_type = str --> this is considered HARDCODING.\n",
+    "data_type = ...\n",
+    "data_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eeb2f409",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fbbc910",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 8:** What does `type` of `True` evaluate to?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aedf1778",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with the correct answer, similar to the answer for Question 4.\n",
+    "# INCORRECT ANSWER: data_type = bool --> this is considered HARDCODING.\n",
+    "data_type = ...\n",
+    "data_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0e7bd41",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c7a2a55",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 9:** What does `type` of `\"True\"` evaluate to? Note the **quotes**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "abeebd46",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with the correct answer, similar to the answer for Question 4.\n",
+    "# DO NOT HARCODE the final type value.\n",
+    "# see questions 4 through 8 for examples of HARDCODING.\n",
+    "data_type = ...\n",
+    "data_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75e942f9",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b86bfcf",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 10:** What does `type` of `319 > 220` evaluate to?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5dd4582a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with the correct answer, similar to the answer for Question 4.\n",
+    "# DO NOT HARDCODE the final type value.\n",
+    "# see questions 4 through 8 for examples of HARDCODING.\n",
+    "data_type = ...\n",
+    "data_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52f31cfe",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "123f256a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 11:** Fix the expression `\":-(\" * 3 + \":-)\" * 5`, to display *2 sad smileys* \":-(\" and *20 happy smileys* \":-)\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5b6eab5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with the correct expression\n",
+    "# INCORRECT ANSWER (see below): \n",
+    "# smileys = ':-(:-(:-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-)' --> this is considered HARDCODING.\n",
+    "smileys = \":-(\" * 3 + \":-)\" * 5 # fix this expression\n",
+    "smileys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "48e9297a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q11\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a507e9d9",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 12:** Fix the expression `20 + 23` to use string concatenation to display `\"2023\"`. Note the **quotes**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ecba1846",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with the correct expression\n",
+    "# INCORRECT ANSWER: curr_year = \"2023\" --> this is considered HARDCODING.\n",
+    "curr_year = 20 + 23 # fix this expression\n",
+    "curr_year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "013918ff",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "070ae166",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 13:** What is the *volume* of a cube with a side length of 6? You **must** use the variable `cube_side` in your solution.\n",
+    "\n",
+    "**Hint**: Use the exponent (\\*\\*) operator to answer this. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dabf2eaf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "cube_side = 6\n",
+    "# replace the ... with the correct expression. We expect you to use the above variable.\n",
+    "# INCORRECT ANSWER: cube_volume = 216 --> this is considered HARDCODING.\n",
+    "cube_volume = ...\n",
+    "cube_volume"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7d2a9a7",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95561a7a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 14:** What is the *volume* of a cylinder with a **height** of *3* and **radius** of *19*? You **must** define, initialize, and use the variables `cylinder_height` and `cylinder_radius` in your solution. You **must** use the variable `pi` in your solution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4961694",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "pi = 3.14\n",
+    "# replace the ... with the correct expression. We expect you to use the above variable.\n",
+    "# we expect you to define, initalize, and use the variables cylinder_height and cylinder_radius.\n",
+    "# INCORRECT ANSWER: cylinder_volume = 3400.62 --> this is considered HARDCODING.\n",
+    "\n",
+    "# create the required variables here\n",
+    "\n",
+    "cylinder_volume = ...\n",
+    "cylinder_volume"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a08008e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02e9956a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Boolean Word Problems\n",
+    "\n",
+    "We're now going to do a few word problems. The most important skill you're going to learn in this class is translating English sentences to code. This will be good practice!\n",
+    "\n",
+    "Here are simple example translations between English phrases and comparison operators:\n",
+    "\n",
+    "\"x is at most y\" or \"x is no more than y\" &rarr; `x <= y`  \n",
+    "\"x is less than y\" or \"x is below y\" or \"x is under y\" &rarr; `x < y`  \n",
+    "\"x is at least y\" &rarr; `x >= y`  \n",
+    "\"x is more than y\" or \"x is above y\" &rarr; `x > y`  \n",
+    "\"x is equal to y\" &rarr; `x == y`  \n",
+    "\"y is within the range of x and z\" or \"y is in between x and z\" &rarr; `x <= y <= z`\n",
+    "\n",
+    "You can use the above translations as verification for your q15 and q16 solutions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4dc63106",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 15:** Suppose, the *safe operation weight limit* for a trailer is *3000 lbs*. Grace's trailer weighs *2000 lbs*. To safely operate the trailer, Grace needs to ensure that her trailer weight is *at most* the operation weight limit. How can Grace figure out if she can safely operate her truck? You **must not** change the variables' values.\n",
+    "\n",
+    "**Hint**: Use the appropriate comparison operator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "49f32809",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# DO NOT CHANGE the values of the below variables\n",
+    "TRAILER_LIMIT = 3000 # constants are typically stored in variable names with all capital case letters\n",
+    "trailer_weight = 2000\n",
+    "\n",
+    "# replace the ... with the correct expression\n",
+    "# we expect you to use the above variables\n",
+    "# INCORRECT ANSWER: safe_operation = True --> this is considered HARDCODING.\n",
+    "safe_operation = ...\n",
+    "safe_operation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f84d6ac",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "863226e7",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 16:** To safely pull a trailer of weight 2000 lbs, Rahul's truck should weigh between 1000 and 3000 lbs. How can Rahul figure out if his truck is heavy enough to operate the trailer? You **must not** change the variables' values.\n",
+    "\n",
+    "**Hint**: Use the appropriate comparison operator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d7a987dd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# DO NOT CHANGE the values of the below variables\n",
+    "LOWER_LIMIT = 1000 # constants are typically stored in variable names with all capital case letters\n",
+    "UPPER_LIMIT = 3000 # constants are typically stored in variable names with all capital case letters\n",
+    "truck_weight = 1500\n",
+    "\n",
+    "# replace the ... with the correct expression\n",
+    "# we expect you to use the above variables.\n",
+    "# INCORRECT ANSWER: safe_operation = True --> this is considered HARDCODING.\n",
+    "safe_operation = ...\n",
+    "safe_operation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e4677feb",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b7fc8cb",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 17:** Carlos wants to go trick-or-treating. To do so he must either make a costume *or* buy a costume. Also, he must walk around *and* have chocolates at home. Given the below variable initializations, Carlos currently isn't successful with trick-or-treating. Change exactly *one variable's initial value* to help Carlos go trick-or-treating. You **must not** change the expression.\n",
+    "\n",
+    "```\n",
+    "make_costume = False\n",
+    "buy_costume = True\n",
+    "\n",
+    "walk_around = False\n",
+    "have_chocolates = True\n",
+    "\n",
+    "success = (make_costume or buy_costume) and (walk_around and have_chocolates)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7d006a3f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# change exactly one variable's initial value to help Carlos go trick-or-treating\n",
+    "make_costume = False\n",
+    "buy_costume = True\n",
+    "\n",
+    "walk_around = False\n",
+    "have_chocolates = True\n",
+    "\n",
+    "\n",
+    "\n",
+    "# DO NOT CHANGE the expression\n",
+    "success = (make_costume or buy_costume) and (walk_around and have_chocolates)\n",
+    "success"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "601054bc",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90f34e67",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 18:** Angel wants to buy either a bright and long shirt or a short and dark shirt. Currently, they are getting `True` for *success*, even though they have only found a long and dark shirt. Fix the Boolean expression to help them make a correct shirt selection. You **must not** change the values of the variables.\n",
+    "\n",
+    "```\n",
+    "short = False\n",
+    "dark = True\n",
+    "\n",
+    "success = (dark and not short) or (short and dark)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0df69e6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# DO NOT CHANGE the values of the variables\n",
+    "short = False\n",
+    "dark = True\n",
+    "\n",
+    "# fix the below Boolean expression to help Angel make a correct shirt selection\n",
+    "success = (dark and not short) or (short and dark) \n",
+    "success"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a32b4f6f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b13450ce",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 19:** *red*, *green*, and *blue* are the primary colors. How can we correct the expression `color == \"red\" or \"green\" or \"blue\"` to correctly verify whether `color` is a primary color? You **must not** change the color variable's value.\n",
+    "\n",
+    "**Hint**: In lab-p2, there was a section on \"Correct way to write boolean expressions\". Now would be a good time to go back and refresh that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ac270d7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# DO NOT CHANGE the value of the below variable\n",
+    "color = \"blue\"\n",
+    "# INCORRECT ANSWER: primary_color = True --> this is considered HARDCODING.\n",
+    "primary_color = color == \"red\" or \"green\" or \"blue\" # fix this expression\n",
+    "primary_color"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d540cb9f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7774a143",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 20:** Students *Alice*, *Bob*, *Chang*, and *Divya* have exam scores of 31, 35, 34, and 35. The expression `alice_score + bob_score + chang_score + divya_score / 4` produces incorrect student average. How can we fix this expression to compute the correct average score? You **must** define, initialize, and use the score variables mentioned in the incorrect expression.\n",
+    "\n",
+    "**Hint**: To override default operator order precedence, parentheses can be used, similar to PEMDAS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82e444b3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the required variables here\n",
+    "\n",
+    "# we expect you to define, initialize, and use the score variables mentioned in the original expression\n",
+    "# INCORRECT ANSWER: average_score = 33.75 --> this is considered HARDCODING.\n",
+    "average_score = alice_score + bob_score + chang_score + divya_score / 4 # fix this expression\n",
+    "average_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4651330",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33e55350",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Submission\n",
+    "It is recommended that at this stage, you Restart and Run all Cells in your notebook.\n",
+    "Restart and Run All is found under Kernel at the top. \n",
+    "That will automatically save your work and generate a zip file for you to submit.\n",
+    "\n",
+    "**SUBMISSION INSTRUCTIONS**:\n",
+    "1. **Upload** the zipfile to Gradescope.\n",
+    "2. Check **Gradescope otter** results as soon as the auto-grader execution gets completed. Don't worry about the score showing up as -/100.0. You only need to check that the test cases passed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6f9450a",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# running this cell will create a new save checkpoint for your notebook\n",
+    "from IPython.display import display, Javascript\n",
+    "display(Javascript('IPython.notebook.save_checkpoint();'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ae66bdf4",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "p2_test.check_file_size(\"p2.ipynb\")\n",
+    "grader.export(pdf=False, run_tests=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6840252",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "otter": {
+   "OK_FORMAT": true,
+   "tests": {
+    "q1": {
+     "name": "q1",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p2_test.check(\"q1\", course_num)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10": {
+     "name": "q10",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p2_test.check(\"q10\", data_type)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q11": {
+     "name": "q11",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p2_test.check(\"q11\", smileys)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12": {
+     "name": "q12",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p2_test.check(\"q12\", curr_year)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13": {
+     "name": "q13",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p2_test.check(\"q13\", cube_volume)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14": {
+     "name": "q14",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p2_test.check(\"q14\", cylinder_volume)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15": {
+     "name": "q15",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p2_test.check(\"q15\", safe_operation)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q16": {
+     "name": "q16",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p2_test.check(\"q16\", safe_operation)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17": {
+     "name": "q17",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p2_test.check(\"q17\", success)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18": {
+     "name": "q18",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p2_test.check(\"q18\", success)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19": {
+     "name": "q19",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p2_test.check(\"q19\", primary_color)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2": {
+     "name": "q2",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p2_test.check(\"q2\", grad_course_num)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20": {
+     "name": "q20",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p2_test.check(\"q20\", average_score)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3": {
+     "name": "q3",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p2_test.check(\"q3\", num_cartons)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4": {
+     "name": "q4",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p2_test.check(\"q4\", data_type)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5": {
+     "name": "q5",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p2_test.check(\"q5\", data_type)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6": {
+     "name": "q6",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p2_test.check(\"q6\", data_type)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7": {
+     "name": "q7",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p2_test.check(\"q7\", data_type)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q8": {
+     "name": "q8",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p2_test.check(\"q8\", data_type)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9": {
+     "name": "q9",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p2_test.check(\"q9\", data_type)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/p2/p2_test.py b/p2/p2_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae82bc5173037fb1f1d533d343c63f7d1db0d542
--- /dev/null
+++ b/p2/p2_test.py
@@ -0,0 +1,80 @@
+#!/usr/bin/python
+
+import os, json, math
+
+
+MAX_FILE_SIZE = 500 # units - KB
+REL_TOL = 6e-04  # relative tolerance for floats
+ABS_TOL = 15e-03  # absolute tolerance for floats
+
+PASS = "PASS"
+
+TEXT_FORMAT = "text"  # question type when expected answer is a str, int, float, or bool
+
+expected_json =    {"1": (TEXT_FORMAT, 220),
+                    "2": (TEXT_FORMAT, 319),
+                    "3": (TEXT_FORMAT, 168),
+                    "4": (TEXT_FORMAT, int),
+                    "5": (TEXT_FORMAT, int),
+                    "6": (TEXT_FORMAT, float),
+                    "7": (TEXT_FORMAT, str),
+                    "8": (TEXT_FORMAT, bool),
+                    "9": (TEXT_FORMAT, str),
+                    "10": (TEXT_FORMAT, bool),
+                    "11": (TEXT_FORMAT, ':-(:-(:-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-)'),
+                    "12": (TEXT_FORMAT, '2023'),
+                    "13": (TEXT_FORMAT, 216),
+                    "14": (TEXT_FORMAT, 3400.62),
+                    "15": (TEXT_FORMAT, True),
+                    "16": (TEXT_FORMAT, True),
+                    "17": (TEXT_FORMAT, True),
+                    "18": (TEXT_FORMAT, False),
+                    "19": (TEXT_FORMAT, True),
+                    "20": (TEXT_FORMAT, 33.75),}
+
+def check_cell(qnum, actual):
+    format, expected = expected_json[qnum[1:]]
+    try:
+        if format == TEXT_FORMAT:
+            return simple_compare(expected, actual)
+        else:
+            if expected != actual:
+                return "expected %s but found %s " % (repr(expected), repr(actual))
+    except:
+        if expected != actual:
+            return "expected %s" % (repr(expected))
+    return PASS
+
+
+def simple_compare(expected, actual, complete_msg=True):
+    msg = PASS
+    if type(expected) == type:
+        if expected != actual:
+            if type(actual) == type:
+                msg = "expected %s but found %s" % (expected.__name__, actual.__name__)
+            else:
+                msg = "expected %s but found %s" % (expected.__name__, repr(actual))
+    elif type(expected) != type(actual) and not (type(expected) in [float, int] and type(actual) in [float, int]):
+        msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+    elif type(expected) == float:
+        if not math.isclose(actual, expected, rel_tol=REL_TOL, abs_tol=ABS_TOL):
+            msg = "expected %s" % (repr(expected))
+            if complete_msg:
+                msg = msg + " but found %s" % (repr(actual))
+    else:
+        if expected != actual:
+            msg = "expected %s" % (repr(expected))
+            if complete_msg:
+                msg = msg + " but found %s" % (repr(actual))
+    return msg
+
+def check(qnum, actual):
+    msg = check_cell(qnum, actual)
+    if msg == PASS:
+        return True
+    print("<b style='color: red;'>ERROR:</b> " + msg)
+
+
+def check_file_size(path):
+    size = os.path.getsize(path)
+    assert size < MAX_FILE_SIZE * 10**3, "Your file is too big to be processed by Gradescope; please delete unnecessary output cells so your file size is < %s KB" % MAX_FILE_SIZE
diff --git a/p2/rubric.md b/p2/rubric.md
new file mode 100644
index 0000000000000000000000000000000000000000..7e60ed953815429b3c413242fe41915ae9964d2e
--- /dev/null
+++ b/p2/rubric.md
@@ -0,0 +1,89 @@
+# Project 2 (P2) grading rubric
+
+## Code reviews
+
+- A TA / grader will be reviewing your code after the deadline.
+- They will make deductions based on the Rubric provided below.
+- To ensure that you don't lose any points in code review, you must review the rubric and make sure that you have followed the instructions provided in the project correctly.
+
+## Rubric
+
+### General guidelines:
+- Did not save the notebook file prior to running the cell containing "export". We cannot see your output if you do not save before generating the zip file. This deduction will become stricter for future projects. (-1)
+
+
+### Question specific guidelines:
+
+- Q2 deduction
+	- `grad_course_num` variable is hardcoded as 319 (-5)
+
+- Q3 deduction 
+	- `num_cartons` variable is hardcoded as 168 (-5)
+
+- Q5 deduction 
+	- `data_type` variable is hardcoded as int (-5)
+
+- Q6 deduction
+	- `data_type` variable is hardcoded as float (-5)
+
+- Q7 deduction
+	- `data_type` variable is hardcoded as str (-5)
+
+- Q8 deduction
+	- `data_type` variable is hardcoded as bool (-5)
+
+- Q9 deduction
+	- `data_type` variable is hardcoded as str (-5)
+
+- Q10 deduction
+	- `data_type` variable is hardcoded as bool (-5)
+
+- Q11 deduction
+	- `smileys` variable is hardcoded (-5)
+
+- Q12 deduction 
+	- `curr_year` variable is hardcoded as "2023" (-5)
+
+- Q13 deductions
+	- `cube_volume` variable is hardcoded as 216 (-5)
+	- `cube_side` variable is not used in `cube_volume` computation (-1)
+
+- Q14 deductions
+	- `cylinder_volume` variable is hardcoded as 3400.62 (-5)
+	- `cylinder_radius` variable is not defined, initialized, and used in `cube_volume` computation (-1)
+	- `cylinder_height` variable is not defined, initialized, and used in `cube_volume` computation (-1)
+
+- Q15 deductions
+	- `safe_operation` variable is hardcoded as True (-5)
+	- Expression to compute `safe_operation` is incorrect (-5)
+	- `TRAILER_LIMIT` and / or `trailer_weight` values are modified (-1)
+	- Equals comparison was missing (-0.5)
+
+- Q16 deductions 
+	- `safe_operation` variable is hardcoded as True (-5)
+	- Expression to compute `safe_operation` is incorrect (-5)
+	- `UPPER_LIMIT` and / or `LOWER_LIMIT` and / or `truck_weight` values are modified (-1)
+	- Equals comparison was missing (-0.5)
+
+- Q17 deductions
+	- success variable is hardcoded as True (-5)
+	- Expression to compute success is changed (-5)
+	- More than 1 variable's initialization value is changed (-1)
+
+- Q18 deductions
+	- success variable is hardcoded as False (-5)
+	- Expression to compute success was not modified correctly (-5)
+	- Variables `short` and / or `dark` are initialized to different values (-1)
+
+- Q19 deductions
+	- `primary_color` variable is hardcoded as True (-5)
+	- Expression to compute `primary_color` is incorrect (-5)
+	- Variable `color` is initialized to different value (-1)
+
+- Q20 deductions
+	- `average_score` variable is hardcoded as 33.75 (-5)
+	- Expression to compute `average_score` is incorrect (-5)
+	- `alice_score` variables is not defined, initialized, and used in `average_score` computation (-0.5)
+	- `bob_score` variables is not defined, initialized, and used in `average_score` computation (-0.5)
+	- `chang_score` variables is not defined, initialized, and used in `average_score` computation (-0.5)
+	- `divya_score` variables is not defined, initialized, and used in `average_score` computation (-0.5)