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+{
+ "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": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "weekend_plans = [] # I have no weekend plans :(\n",
+ "print(weekend_plans)\n",
+ "\n",
+ "# TODO add three things to your weekend plans\n",
+ "\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": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "my_set_of_weekend_plans = set()\n",
+ "\n",
+ "# TODO: add 4 weekend plans, 1 of which is a duplicate.\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": null,
+ "metadata": {},
+ "outputs": [],
+ "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": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "my_set_of_weekend_plans.discard(...)\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": null,
+ "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": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def cell(row_idx, col_name):\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Warmup 4: Does the oldest basil/spinach-loving Business major prefer cats, dogs, or neither?"
+ ]
+ },
+ {
+ "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": "markdown",
+ "metadata": {},
+ "source": [
+ "## Restaurants"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "restaurant_csv = process_csv('restaurants.csv')\n",
+ "\n",
+ "# TODO: Display restaurant_csv. What do we call this data structure?"
+ ]
+ },
+ {
+ "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": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "restaurant_header = ...\n",
+ "restaurant_data = ..."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 1: Make a list of just the names from restaurant_data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "names = [] # names starts out empty, we will append to it\n",
+ "for row in restaurant_data:\n",
+ " pass\n",
+ "names"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 2: Extract the list of unique restaurant names "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(sorted(names))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Sorting Option 2: Sort the list and then print it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "names.sort()\n",
+ "print(names)"
+ ]
+ },
+ {
+ "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": null,
+ "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 ...:\n",
+ " return None\n",
+ " elif ...:\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": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_restaurant_at_coordinates(search_x, search_y):\n",
+ " for i in range(len(restaurant_data)):\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "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": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_coordinates(restaurant_id):\n",
+ " for i in range(len(restaurant_data)):\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "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": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_smallest_index(col_name):\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### What is the name of the restaurant farthest to the west?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use `get_smallest_index` to find the answer here\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### What is the restaurant ID of the restaurant farthest to the south?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use `get_smallest_index` to find the answer here\n"
+ ]
+ },
+ {
+ "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": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import math\n",
+ "def distance(x1, y1, x2, y2):\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "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": null,
+ "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?\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 ...: # check for missing data\n",
+ " continue\n",
+ " current_dist = ...\n",
+ " \n",
+ " if ...:\n",
+ " closest_index = i\n",
+ " min_dist = current_dist\n",
+ " \n",
+ " return cell(closest_index, \"name\") # Bonus: fix this to make the function more useful"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(closest_restaurant(3, 3)) # should be Einsteins Bagels\n",
+ "print(closest_restaurant(0, 0)) # should be Starbucks\n",
+ "print(closest_restaurant(5, -2)) # should be McDonalds\n",
+ "print(closest_restaurant(1, -2)) # 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
+}