{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fc578007",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "# Lab 2: A year with Python\n",
    "\n",
    "In this lab, you're going to create a program that uses several variables and then you'll experiment with some additional assignment statetments.\n",
    "\n",
    "__Challenge Exercise 1:__ Your task is to write a program which computes the fraction of the year that you will spend working on Python for this course. To do that, we'll make the following assumptions:\n",
    "\n",
    "* The \"time spent on Python\" is defined as the amount of time that it is recommended for students to put into a course.\n",
    "* The recommended time to spend on a course is 3 hours outside of class for every hour inside class. \n",
    "* A normal semester includes 14 weeks, with 150 minutes of in-class time each week.\n",
    "* You can assume the year is a non-leap year, with 365 days.\n",
    "\n",
    "Your program might start like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2ad2063",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "num_course_weeks = 14\n",
    "in_class_minutes_per_week = 150\n",
    "total_in_class_minutes = num_course_weeks * in_class_minutes_per_week"
   ]
  },
  {
   "attachments": {
    "ayearwithpythonoutput.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "7f63e48b",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "You should include an output statetment which prints the answer along with text that explains what the number is. For example, something like this:\n",
    "\n",
    "<p>\n",
    "<div>\n",
    "<img src=\"attachment:ayearwithpythonoutput.png\" width=\"800\"/>\n",
    "</div>\n",
    "</p>\n",
    "\n",
    "And, that's actually the answer you should come up with if you make all the assumptions listed above and you do all the calculations correctly.\n",
    "\n",
    "Here's what I'll be looking for in your solution:\n",
    "* A correct calculation (i.e., the number above or something very close to it should be output with some text)\n",
    "* Create at least two new variables with descriptive names\n",
    "* Use variables on both the left and right side of `=`. That is, use the values of your variables in computing values for other variables\n",
    "\n",
    "When you have your solution ready, submit it to codePost for the __Lab 2: A year with Python__ assignment."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eda8177c",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "__Exercise 2:__ Run the following code at the interactive shell, observe the output, and reflect on the questions below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a14c6263",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "my_variable = 5\n",
    "my_variable = my_variable + 1\n",
    "print(my_variable)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28ab6d90",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "* Can you put a variable on both the left and right side of an assignment statement?\n",
    "* Why might you want to do that?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ccf9b4d",
   "metadata": {},
   "source": [
    "__Exercise 3:__ Run the following code at the interactive shell, observe the output, and reflect on the questions below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f7660fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_variable = 5\n",
    "my_variable += 1\n",
    "print(my_variable)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3417b27",
   "metadata": {},
   "source": [
    "* Is there a difference between `my_variable = my_variable + 1` and `my_variable += 1`?\n",
    "* Can you do this with other operators? Is there a `-=`, `*=`, or `/=`?"
   ]
  }
 ],
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   "codemirror_mode": {
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   "file_extension": ".py",
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