Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MSDS 430 Module 1 Python Assignment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <div class=\"alert alert-block alert-warning\"> Read through this notebook and follow the instructions provided. In this assignment you will complete the exercises that follow and submit your notebook (ipynb file) and html file to Canvas. Your files should include all output, i.e. run each cell and save before submitting for a grade.  In this course we will use only Python 3.x</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\"><i>Each week we will be asking you to submit your completed notebook along with an html file. Some assignments will require additional types of files, so watch for that later on. Once you've run every cell in the notebook and you're satisfied with the results, submit the notebook and the html file to Canvas for grading. To download the html file to your computer, go to <u>File --> Download as --> HTML.</u></i></div>"
   ]
  },
  {
   "attachments": {
    "save-as-html-screenshot%20%28Custom%29.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![save-as-html-screenshot%20%28Custom%29.png](attachment:save-as-html-screenshot%20%28Custom%29.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\"><b>Each week you will need to run each code cell in the assignment as you work through it, even if it is already showing output. Many times certain results will be incorrect or variables used later in the notebook will not be recognized and will throw an error. In each week's assignment it's best if you run the code cells individually as you encounter them.</b></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring `Help`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a moment to explore the resources provided above via `Help` in the menubar. Some are provided as a quick reference and are often sufficient to get basic guidance. For example, the `User Interface Tour` is a quick tour of the user interface in Jupyter notebook. There is also a link to documentation on some of the basics of Jupyter notebook in `Notebook Help`. Other resources in `Help` provide much lengthier guidance that you may want to reference later in the course."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Keyboard Shortcuts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jupyter notebook has many keyboard shortcuts that can be useful. A list of these shortcuts can be accessed from the toolbar above the `keyboard` icon or `Help --> Keyboard Shortcuts` from the menu bar. Another reference with keyboard shortcuts is provided in Canvas in the `Additional Resources` module. You can refer to either resource to answer the next question."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Markdown Cells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you insert a cell in your notebook it will be a code cell by default. To convert the cell to a Markdown cell, place your cursor in the cell then select `Markdown` from the drop-down menu in the toolbar. With a `Markdown` cell we can type as we normally would, then run the cell to display the formatted version. \n",
    "\n",
    "1. Single click on this cell to see that it is a markdown cell. This is indicated in the drop-down menu in the toolbar. \n",
    "\n",
    "2. Now double click on this cell and notice that you can see the behind the scenes HTML code.\n",
    "\n",
    "You can create bullet points by using a dash (-).\n",
    "- You can make words **bold**.\n",
    "- You can make them <span style='color:red'>red</span>.\n",
    "- You can <u>underline</u> them.\n",
    "- You can <i>italicize</i> them.\n",
    "\n",
    "3. Run the cell to return it to the formatted version."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\"><b>Problem 1 (4 pts.)</b>: First, convert the cell provided below to a <b><u>Markdown</u></b> cell then use the markdown cell to type your answers to the following questions: \n",
    "    \n",
    "1. What is the keyboard command to enter Command mode?\n",
    "2. What is the keyboard command to return to Edit mode?\n",
    "2. What is the keyboard command for inserting a cell below the current cell?\n",
    "3. What is the keyboard command to run a cell then move to the next cell? </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. What is the keyboard command to enter Command mode? To enter Command mode, press the ESC key. \n",
    "2. What is the keyboard command to return to Edit mode? To enter Edit mode, press the Enter key. \n",
    "3. What is the keyboard command for inserting a cell below the current cell? To insert a cell below the current cell, press the B key.\n",
    "4. What is the keyboard command to run a cell then move to the next cell? To run a cell then move to the next cell, press Shift + Return.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another type of cell we will work with is a `code` cell, which is where we will do all of our programming. As mentioned above, when you insert a cell in a Jupyter notebook, the default is to create a code cell. Unlike a Markdown cell, to add comments to a code cell we need to use an alternative approach. A common method is to use the `hash` symbol, `#`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This is one way to add comments to your code.\n",
    "#For each line of comments, a 'hash' symbol must be used while working in a code cell."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another method of adding (**multiline**) comments within a `code` cell is to use triple quotations like the cell below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'When triple quotations are used, the comments will be enclosed by one set of quotations.\\nThere is no need to start new quotations on each line. Just make sure all of your comments are wrapped\\nwith quotations.'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''When triple quotations are used, the comments will be enclosed by one set of quotations.\n",
    "There is no need to start new quotations on each line. Just make sure all of your comments are wrapped\n",
    "with quotations.'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\"><b>Problem 2 (2 pts.)</b>: Use either method shown above for commenting while working in a code cell. In the cell provided below, explain the advantages of including comments within a program.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#A function of the hash is its use to denote comments in a code cell.\n",
    "#A comment can be used to communicate information or describe the code.\n",
    "#A comment can be used to prevent code from excecuting when a code cell is ran. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\"><b>Problem 3 (3 pts.)</b>: Run the cell immediately below this one and carefully read the error message it generates. Using the cell provided below that:\n",
    "    \n",
    "1. indicate what type of error is generated,\n",
    "2. explain what is causing the error, and\n",
    "3. correct the print statement.\n",
    "\n",
    "Be sure to follow along with the instructions provided in the <font color=b><b># TODO</b></font> statements.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-10-c9b6596d0c69>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-10-c9b6596d0c69>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print(This is my first Python print execution.)\u001b[0m\n\u001b[0m                     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "print(This is my first Python print execution.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Respond to the following prompts:\n",
    "\n",
    "# TODO - What type of error is generated?\n",
    "# Answer: The error is generated from invalid syntax within the attempted command. \n",
    "\n",
    "# TODO - What is causing the error?\n",
    "# Answer: The error results from not having placed quotes around the print statement.\n",
    "\n",
    "# TODO - Write the correct code to print `This is my first Python print execution.` then run this cell.\n",
    "# Answer: print (\"This is my first Python print execution.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\"><b>Problem 4 (2 pts.)</b>: Using the cell provided, name the two other types of errors (<i>different from the error in Problem 1</i>) that are mentioned in Chapter 1 from your readings. </div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO - Name the two other types of errors mentioned in Chapter 1 from your readings.\n",
    "# Answer: Another error is a Runtime error, known as exceptions, and Semantic errors \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell below has assigned values to different types of variables: `string`, `integer`, `float`, `Boolean`, and `null`. We will learn more about data types next week. Run the cell below so these can be used later in `Problem 4`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python defined variables are global and hence can be recalled later on in any other cell\n",
    "name = 'Albert' # this is a string, assigned to name\n",
    "height = 6      # this is an integer value, assigned to 'height'\n",
    "pounds = 181.389321    # this is a numeric value, assigned to 'pounds'\n",
    "tvalue = True   # this is a Boolean value, assigned to 'tValue'\n",
    "flag=None     # this is a null in Python and has nothing defined for 'flag'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could check the type of each variable by using the command `type(variable_name)`. Now that you have run the previous cell, you can run the following cell to see what is displayed. Feel free to change `tvalue` to any of the other variables to see what is displayed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bool"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(tvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Formatting Print Statements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print statements can be formatted nicely using various methods. One possibility is the `String` format method. An example of this method would be: </br>\n",
    "\n",
    "```python\n",
    "print(\"My name is {} and I am {} feet tall.\".format(name, height))\n",
    "```\n",
    "\n",
    "The output will look like this: </br>\n",
    "\n",
    "`My name is Albert and I am 6 feet tall.` </br>\n",
    "\n",
    "As you may have guessed, the brackets `{}` represent format fields and are replaced with objects passed through them in the `str.format` method. In this case we have already defined the variables `name` and `height` above, but we can still use this method without having predefined variables. </br>\n",
    "\n",
    "If we place numbers in the brackets, this indicates the position of where the object should be passed. Remember the 1st position will be represented by `0`, the 2nd by `1`, etc. To see this, run the following cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Camille and Grace are my best friends.\n"
     ]
    }
   ],
   "source": [
    "print(\"{0} and {1} are my best friends.\".format('Camille', 'Grace'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, if we switch the placement of `0` and `1` we get something different. Try running the cell below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grace and Camille are my best friends.\n"
     ]
    }
   ],
   "source": [
    "print(\"{1} and {0} are my best friends.\".format('Camille', 'Grace'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another method of formatting print statements is to use `f-strings`. This is done by typing an `f` or `F` in front of the string and using `{}` to pass our variables through. One of the benefits of the f-string method is that it affords us more formatting options. Now run the following cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Albert weighs 181.39 pounds.\n"
     ]
    }
   ],
   "source": [
    "print(f'{name} weighs {pounds:.2f} pounds.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice this even rounded appropriately. In other words, it rounded to two decimals and rounded up with the last decimal. Another option is to use the right side of the `:` to indicate the minimum number of characters to use. This can be useful to line up columns. Notice how the second column lines up. Run the following cell and feel free to play around with the character lengths to see the effect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Albert     -->     6 feet tall\n",
      "Albert     -->   181 pounds\n"
     ]
    }
   ],
   "source": [
    "print(f'{name:10} --> {height:5d} feet tall')\n",
    "print(f'{name:10} --> {int(pounds):5d} pounds')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are just a few basic examples of how to format print statements nicely. For more details about powerful print options to play with, read through this article on __[f-strings](https://realpython.com/python-f-strings/)__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\"><b>Problem 5 (3 pts.)</b>: In the cell below, create a formatted print statement using an f-string that passes through the variables `name`, `height`, and `weight` defined above and returns the sentence below. Your output should look <i><u>exactly like this</u></i>: </div></br>\n",
    "\n",
    "`Albert was 6 feet tall in high school, but weighed less than 181 pounds.`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO - Write a formatted print statement that passes through the variables 'name', 'height', and 'weight' and\n",
    "# prints the statement exactly as shown above and run the cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = \"Michael Scott\"\n",
    "height = 6\n",
    "pounds = 155\n",
    "tvalue = True\n",
    "flag=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Michael Scott was 6 feet tall in high school, but weighed less than 155 pounds.\n"
     ]
    }
   ],
   "source": [
    "print(f'{name} was {height} feet tall in high school, but weighed less than {pounds} pounds.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">Be sure to <i><u>run all cells in your notebook</u></i> then submit the following two files for a grade:\n",
    "    \n",
    "\n",
    "1. Your completed <b>notebook</b>\n",
    "2. Your <b>html file</b>\n",
    "\n",
    "</div>"
   ]
  }
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