diff --git a/s24/AmFam_Ashwin/16_List_Practice/Lecture Code/Lec_16_List_Practice_Solution.ipynb b/s24/AmFam_Ashwin/16_List_Practice/Lecture Code/Lec_16_List_Practice_Solution.ipynb
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@@ -0,0 +1,839 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Lists Practice"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Warmup 0: Hotkeys\n",
+ "\n",
+ "We move quickly, it's good to know some hotkeys!\n",
+ "\n",
+ "#### All-around good-to-knows...\n",
+ "* `Ctrl`+`A`: Select all the text in a cell.\n",
+ "* `Ctrl`+`C`: Copy selected text.\n",
+ "* `Ctrl`+`X`: Cut selected text.\n",
+ "* `Ctrl`+`V`: Paste text from clipboard.\n",
+ "* `Ctrl`+`S`: Save.\n",
+ "\n",
+ "#### Jupyter-specific good-to-knows...\n",
+ "* `Ctrl`+`Enter` or `Shift`+`Enter`: Run Cell\n",
+ "* `Ctrl`+`/`: Comment/uncomment sections of code.\n",
+ "* `Esc` -> `Shift`+`L`: Toggle line numbers.\n",
+ "* Select text -> `Tab`: Add extra level of indent.\n",
+ "* Select text -> `Shift`+`Tab`: Remove level of indent."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Warmup 1: Create an empty list and add elements to it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[]\n",
+ "['study', 'hike', 'golf']\n"
+ ]
+ }
+ ],
+ "source": [
+ "weekend_plans = [] # I have no weekend plans :(\n",
+ "print(weekend_plans)\n",
+ "\n",
+ "# TODO add three things to your weekend plans\n",
+ "weekend_plans.append(\"study\")\n",
+ "weekend_plans.append(\"hike\")\n",
+ "weekend_plans.append(\"golf\")\n",
+ "print(weekend_plans)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Warmup 2: Sets\n",
+ "\n",
+ "Like a **list**, a **set** is another collection. However, it is **unordered** and **unique**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'study', 'hike', 'golf'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "my_set_of_weekend_plans = set()\n",
+ "\n",
+ "# TODO: add 4 weekend plans, 1 of which is a duplicate.\n",
+ "my_set_of_weekend_plans.add(\"study\")\n",
+ "my_set_of_weekend_plans.add(\"study\")\n",
+ "my_set_of_weekend_plans.add(\"hike\")\n",
+ "my_set_of_weekend_plans.add(\"golf\")\n",
+ "\n",
+ "print(my_set_of_weekend_plans)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### We can `pop` to remove elements, but it will remove a random item.\n",
+ "See: https://www.w3schools.com/python/trypython.asp?filename=demo_ref_set_pop2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "study\n",
+ "{'hike', 'golf'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "what_removed = my_set_of_weekend_plans.pop()\n",
+ "\n",
+ "print(what_removed)\n",
+ "print(my_set_of_weekend_plans)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### We can `discard` a specific item!\n",
+ "\n",
+ "Unlike a list's `remove`, this will **not** throw an error if the element does not exist."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'golf'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "my_set_of_weekend_plans.discard('hike')\n",
+ "print(my_set_of_weekend_plans)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Copy/paste the `cell` function from the last lecture"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import csv\n",
+ "\n",
+ "# source: Automate the Boring Stuff with Python Ch 12\n",
+ "def process_csv(filename):\n",
+ " exampleFile = open(filename, encoding=\"utf-8\") \n",
+ " exampleReader = csv.reader(exampleFile) \n",
+ " exampleData = list(exampleReader) \n",
+ " exampleFile.close() \n",
+ " return exampleData"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cs220_csv = process_csv('cs220_survey_data.csv')\n",
+ "cs220_header = cs220_csv[0]\n",
+ "cs220_data = cs220_csv[1:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def cell(row_idx, col_name):\n",
+ " col_idx = cs220_header.index(col_name)\n",
+ " val = cs220_data[row_idx][col_idx]\n",
+ " if val == \"\":\n",
+ " return None\n",
+ " elif col_name == \"Age\" or col_name == \"Zip Code\":\n",
+ " return int(val)\n",
+ " elif col_name == \"Latitude\" or col_name == \"Longitude\":\n",
+ " return float(val)\n",
+ " else:\n",
+ " return val"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Warmup 4: Does the oldest basil/spinach-loving Business major prefer cats, dogs, or neither?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The oldest basil/spinach loving Business major prefers cat\n"
+ ]
+ }
+ ],
+ "source": [
+ "oldest_idx = None\n",
+ "oldest_age = None\n",
+ "\n",
+ "for i in range(len(cs220_data)):\n",
+ " current_age = cell(i, \"Age\")\n",
+ " current_pizza = cell(i, \"Pizza topping\")\n",
+ " current_major = cell(i, \"Primary major\")\n",
+ " if current_age == None:\n",
+ " continue\n",
+ " \n",
+ " if current_pizza == \"basil/spinach\" and current_major.startswith(\"Business\"):\n",
+ " if oldest_idx == None or current_age > oldest_age:\n",
+ " oldest_age = current_age\n",
+ " oldest_idx = i\n",
+ " \n",
+ "print(\"The oldest basil/spinach loving Business major prefers\", cell(oldest_idx, \"Cats or dogs\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Warmup 5: Is their city in the United States?\n",
+ "\n",
+ "The United States has **latitudes** approximately spanning from `23.101` to `49.632`, and **longitudes** approximately spanning from `-129.306` to `-65.017`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The oldest basil/spinach loving Business major is NOT in the USA\n"
+ ]
+ }
+ ],
+ "source": [
+ "spinach_lat = cell(oldest_idx, \"Latitude\")\n",
+ "spinach_lon = cell(oldest_idx, \"Longitude\")\n",
+ "\n",
+ "if 23.101 <= spinach_lat <= 49.632 and -129.306 <= spinach_lon <= -65.017:\n",
+ " print('The oldest basil/spinach loving Business major is in the USA')\n",
+ "else:\n",
+ " print('The oldest basil/spinach loving Business major is NOT in the USA')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Restaurants"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[['restaurant_id', 'name', 'x_coord', 'y_coord'],\n",
+ " ['MCD_1', 'McDonalds', '-3', '-3'],\n",
+ " ['EIN_1', 'Einsteins Bagels', '1', '3'],\n",
+ " ['STA_1', 'Starbucks', '0', '1'],\n",
+ " ['MCD_2', 'McDonalds', '2', '0'],\n",
+ " ['GRE_1', 'Greenbush Donuts', '0', '-3'],\n",
+ " ['STA_2', 'Starbucks', '-2', '1'],\n",
+ " ['PAN_1', 'Panda Express', '', '']]"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "restaurant_csv = process_csv('restaurants.csv')\n",
+ "\n",
+ "# TODO: Display restaurant_csv. What do we call this data structure? -> list of lists\n",
+ "restaurant_csv"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Separate the data into 2 parts: a header row, and a list of data rows"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "restaurant_header = restaurant_csv[0]\n",
+ "restaurant_data = restaurant_csv[1:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['restaurant_id', 'name', 'x_coord', 'y_coord']"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "restaurant_header"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[['MCD_1', 'McDonalds', '-3', '-3'],\n",
+ " ['EIN_1', 'Einsteins Bagels', '1', '3'],\n",
+ " ['STA_1', 'Starbucks', '0', '1'],\n",
+ " ['MCD_2', 'McDonalds', '2', '0'],\n",
+ " ['GRE_1', 'Greenbush Donuts', '0', '-3'],\n",
+ " ['STA_2', 'Starbucks', '-2', '1'],\n",
+ " ['PAN_1', 'Panda Express', '', '']]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "restaurant_data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 1: Make a list of just the names from restaurant_data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['McDonalds',\n",
+ " 'Einsteins Bagels',\n",
+ " 'Starbucks',\n",
+ " 'McDonalds',\n",
+ " 'Greenbush Donuts',\n",
+ " 'Starbucks',\n",
+ " 'Panda Express']"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "names = [] # names starts out empty, we will append to it\n",
+ "for row in restaurant_data:\n",
+ " names.append(row[restaurant_header.index(\"name\")])\n",
+ "names"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 2: Extract the list of unique restaurant names "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['Starbucks', 'Greenbush Donuts', 'Panda Express', 'McDonalds', 'Einsteins Bagels']\n"
+ ]
+ }
+ ],
+ "source": [
+ "names = list(set(names))\n",
+ "print(names)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 3: Sort the list of unique restaurant names"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Sorting Option 1: Print the sorted list without changing it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['Einsteins Bagels', 'Greenbush Donuts', 'McDonalds', 'Panda Express', 'Starbucks']\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(sorted(names))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['Starbucks', 'Greenbush Donuts', 'Panda Express', 'McDonalds', 'Einsteins Bagels']\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(names) # note that this list is still the same as earlier (i.e., before sorting)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Sorting Option 2: Sort the list and then print it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['Einsteins Bagels', 'Greenbush Donuts', 'McDonalds', 'Panda Express', 'Starbucks']\n"
+ ]
+ }
+ ],
+ "source": [
+ "names.sort()\n",
+ "print(names) # note that this list itself has been modified here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 4: Define a `cell` function to extract the data in `restaurant_data`\n",
+ "\n",
+ "Make sure it returns `None` if there is **missing** data. Make sure it **typecasts** appropriately depending on the **column name**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def cell(row_idx, col_name):\n",
+ " col_idx = restaurant_header.index(col_name)\n",
+ " val = restaurant_data[row_idx][col_idx]\n",
+ " if val == \"\":\n",
+ " return None\n",
+ " elif col_name == \"x_coord\" or col_name == \"y_coord\":\n",
+ " return int(val)\n",
+ " else:\n",
+ " return val"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 5: Write a function that is sent x y coordinates and returns back the `restaurant_id` of that restaurant."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_restaurant_at_coordinates(search_x, search_y):\n",
+ " for i in range(len(restaurant_data)):\n",
+ " if cell(i, 'x_coord') == search_x and cell(i, 'y_coord') == search_y:\n",
+ " return cell(i, 'restaurant_id')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "EIN_1\n",
+ "GRE_1\n",
+ "None\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(get_restaurant_at_coordinates(1, 3)) # should be EIN_1\n",
+ "print(get_restaurant_at_coordinates(0, -3)) # should be GRE_1\n",
+ "print(get_restaurant_at_coordinates(2, -3)) # should be None"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 6: Write a function that is sent the restaurant ID of a restaurant and returns the x and y coordinates as a string.\n",
+ "\n",
+ "This should be **case-insensitive**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_coordinates(restaurant_id):\n",
+ " for i in range(len(restaurant_data)):\n",
+ " x_coord = cell(i, \"x_coord\")\n",
+ " y_coord = cell(i, \"y_coord\")\n",
+ " if x_coord == None or y_coord == None:\n",
+ " continue\n",
+ " if cell(i, \"restaurant_id\").lower() == restaurant_id.lower():\n",
+ " return \"(\" + str(x_coord) + \", \" + str(y_coord) + \")\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(0, -3)\n",
+ "(2, 0)\n",
+ "(2, 0)\n",
+ "None\n",
+ "None\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(get_coordinates(\"GRE_1\")) # should be (0, -3)\n",
+ "print(get_coordinates(\"MCD_2\")) # should be (2, 0)\n",
+ "print(get_coordinates(\"mcd_2\")) # should be (2, 0)\n",
+ "print(get_coordinates(\"PAN_1\")) # should be None\n",
+ "print(get_coordinates(\"ZZZ_123\")) # should be None"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 7: Define `get_smallest_index` to get the INDEX of the smallest value in `col_name` (such as `'x_coord'`)\n",
+ "\n",
+ "If there are ties, use the last value in the dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_smallest_index(col_name):\n",
+ " smallest = None\n",
+ " smallest_idx = None\n",
+ " for i in range(len(restaurant_data)):\n",
+ " curr_val = cell(i, col_name)\n",
+ " if curr_val == None:\n",
+ " continue\n",
+ " \n",
+ " if smallest == None or curr_val <= smallest:\n",
+ " smallest = curr_val\n",
+ " smallest_idx = i\n",
+ " return smallest_idx"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### What is the name of the restaurant farthest to the west?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'McDonalds'"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# use `get_smallest_index` to find the answer here\n",
+ "\n",
+ "far_west_idx = get_smallest_index(\"x_coord\")\n",
+ "far_west_restauraunt = cell(far_west_idx, 'name')\n",
+ "far_west_restauraunt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### What is the restaurant ID of the restaurant farthest to the south?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'GRE_1'"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# use `get_smallest_index` to find the answer here\n",
+ "\n",
+ "far_south_idx = get_smallest_index(\"y_coord\")\n",
+ "far_south_restaurant = cell(far_south_idx, 'restaurant_id')\n",
+ "far_south_restaurant"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 8: Complete this function that computes the distance between `(x1,y1)` and `(x2,y2)`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import math\n",
+ "def distance(x1, y1, x2, y2):\n",
+ " return math.sqrt((x1 - x2)**2 + (y1 - y2)**2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "5.0\n",
+ "1.4142135623730951\n",
+ "13.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(distance(0, 0, 3, 4)) # should be 5.0\n",
+ "print(distance(1, 2, 2, 3)) # should be square root of 2\n",
+ "print(distance(-3, 3, 2, -9)) # should be 13.0"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 9: Write a function that is sent x and y coordinates and returns the name of the closest restaurant to those coordinates.\n",
+ "\n",
+ "Use the `distance` function to calculate the distance."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def closest_restaurant(source_x, source_y):\n",
+ " '''return the name of the closest restaurant to the parameters given'''\n",
+ " closest_index = None # start with no value, to be clear if no result found\n",
+ " min_dist = None # why does this have to be None, not just 0? -> If closest restaurant is at (source_x, source_y), we do NOT want to skip\n",
+ " \n",
+ " for i in range(len(restaurant_data)):\n",
+ " current_x = cell(i, \"x_coord\")\n",
+ " current_y = cell(i, \"y_coord\")\n",
+ " if current_x == None or current_y == None: # check for missing data\n",
+ " continue\n",
+ " current_dist = distance(current_x, current_y, source_x, source_y)\n",
+ " \n",
+ " if min_dist == None or min_dist >= current_dist:\n",
+ " closest_index = i\n",
+ " min_dist = current_dist\n",
+ " \n",
+ " return closest_index # Bonus: fix this to make the function more useful -> make the function return the index instead of the name"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Einsteins Bagels\n",
+ "Starbucks\n",
+ "McDonalds\n",
+ "Greenbush Donuts\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(cell(closest_restaurant(3, 3), \"name\")) # should be Einsteins Bagels\n",
+ "print(cell(closest_restaurant(0, 0), \"name\")) # should be Starbucks\n",
+ "print(cell(closest_restaurant(5, -2), \"name\")) # should be McDonalds\n",
+ "print(cell(closest_restaurant(1, -2), \"name\")) # should be Greenbush Donuts"
+ ]
+ }
+ ],
+ "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": "3.11.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}