Lect1.ipynb

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    "# Lecture 1 --- April 2023\n",
    "\n",
    "## Outline of topics for this lecture:\n",
    "\n",
    "1. Brief history of python\n",
    "2. Python's place in the family of programming languages\n",
    "3. Basic variables (integer, float, string)\n",
    "4. Conversions between the basic variable types\n",
    "5. Boolean variables\n",
    "6. \"Collection\" data types\n",
    "    a. lists\n",
    "    b. tuples\n",
    "    c. sets\n",
    "    d. dictionaries\n",
    "7. Operators\n",
    "8. Control statements\n",
    "9. Functions\n",
    "\n",
    "## In lab later on the assigned work is:\n",
    "\n",
    "1. Watch tutorials on github, sign up for a github account, and make a copy of (a.k.a., fork) a repo which will allow you to run the neccessary python code in a jupyter notebook.\n",
    "2. Write a script to read a file from disk, rename it, and write it back out with an extra line at the end.\n",
    "3. Write a function to test whether a particular word is present in a disk file or not and return `True` or `False` depending on the case.\n",
    "4. Read a csv file from disk and find the historical corn yield data in preparation for plotting the corn yield trend in the United States.\n",
    "\n",
    "## Some useful background material:\n",
    "\n",
    "The <a href=\"https://the-examples-book.com/book/introduction\" target=\"_blank\">Purdue Data Mine Examples Book</a> contains many useful chapters on data science. While they have not been directly designed for this class, they may be useful. You will not need to use scholar to perform the exercises of this class so don't worry about that part. Here is a direct link to the <a href=\"https://the-examples-book.com/book/python/introduction\" target=\"_blank\">Python chapter.</a>\n",
    "\n",
    "\n",
    "Additional useful links for Python include:\n",
    "\n",
    "<a href=\"https://docs.python.org/3/\" target=\"_blank\">Python 3.9.4 documentation</a>\n",
    "\n",
    "The <a href=\"https://pypi.org/\" target=\"_blank\">Python Package Index</a> (This contains many of the useful Python \"add-on\" packages such as the math package)\n",
    "\n",
    "The <a href=\"https://numpy.org/\" target=\"_blank\">Numpy Package</a> (This contains specialized array (vector and matrix) routines. Numpy stands for \"Numerical Python\")\n",
    "\n",
    "United States Department of Agriculture: <a href=\"https://quickstats.nass.usda.gov/\" target=\"_blank\">Quick Stats</a> (The USDA's National Ag Statistics Service -- go here and familiarize yourself with the available data)\n",
    "\n",
    "You will need to use Git and Github to get the example code for the class. Some useful tutorial links are:\n",
    "\n",
    "<a href=\"https://www.youtube.com/watch?v=USjZcfj8yxE\" target=\"_blank\">Learn Git in 15 Minutes</a> (Colt Steele)\n",
    "\n",
    "<a href=\"https://www.youtube.com/watch?v=USjZcfj8yxE\" target=\"_blank\">Learn Github in 20 Minutes</a> (Colt Steele)\n",
    "\n",
    "It makes sense to create a free github account for yourself: <a href=\"https://github.com/\" target=\"_blank\">Github</a>"
   ]
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   "source": [
    "## History of Python, Etc.\n",
    "\n",
    "- Conceived by Guido van Rossum in December 1989 at the Center Wiskunde and Informatica (Dutch national research institute for mathematics and computer science).\n",
    "- Python version 1.0 in January 1994.\n",
    "- GNU General Public License (open source) since version 1.6.1.\n",
    "- Python version 2.0 in October 2000.\n",
    "- Python Software Foundation formed in 2001 and a new open source license.\n",
    "- Python version 2.7 was the last release in the version 2 series. Support ended January 2020.\n",
    "-Python version 3.0 released December 2008. It broke backward compatibility with much of the verson 2 code.\n",
    "- Latest version is 3.10 (October 2021)."
   ]
  },
  {
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   "metadata": {},
   "source": [
    "## According to stackoverflow survey of profession software developers ...\n",
    "\n",
    "<img align=\"left\" src='Figs/DeveloperSurvey2021.png' width=\"500\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As concerns languages for data science ...\n",
    "\n",
    "The contenders are Python and R. For **Python**\n",
    "\n",
    "* Most popular among data scientists.\n",
    "* Very useful in machine learning and artificial intelligence because of the availabilty of popular libraries such as scikit-learn, matplotlib, and tensorflow, etc.\n",
    "\n",
    "For **R**\n",
    "\n",
    "* A scripting language.\n",
    "* Very good support for statistical computation and visulalization.\n",
    "\n",
    "This course is focussed on Python instead of R because that is a better for the work of my research group. For an independent comparison of the two: <a href=\"https://www.ibm.com/cloud/blog/python-vs-r\" target=\"_blank\">Python vs. R: What's the Difference?</a>"
   ]
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   "source": [
    "## Basic variables\n",
    "\n",
    "Python uses something called **dynamic typing**, which means that a variable is created when a value is assigned to it. The type can be changed after originally set. There are a few rules on variable names:\n",
    "\n",
    "* Must start with a letter or underscore\n",
    "* Names are case-sensitive\n",
    "\n",
    "A python variable is more than just its value. It must also contain information about the type of the value. There is overhead associated with such flexibility. The code below illustrates three of the variable types: **integer, float, and string**."
   ]
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     "text": [
      "The type of x is:\n",
      "<class 'int'>\n",
      "\n",
      "The value of x is:\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "# Integer, i.e., whole numbers both positive and negative. Later on \n",
    "# we will illustrate formatting the print command.\n",
    "x = 4;\n",
    "print('The type of x is:')\n",
    "print(type(x))\n",
    "print() # Just to give a space.\n",
    "print('The value of x is:')\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The type of x is:\n",
      "<class 'float'>\n",
      "\n",
      "The value of x is:\n",
      "4.0\n"
     ]
    }
   ],
   "source": [
    "# Floating point, i.e., computer representation of real numbers.\n",
    "x = 4.0;\n",
    "print('The type of x is:')\n",
    "print(type(x))\n",
    "print() # Just to give a space.\n",
    "print('The value of x is:')\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The type of x is:\n",
      "<class 'str'>\n",
      "\n",
      "The value of x is:\n",
      "four\n"
     ]
    }
   ],
   "source": [
    "# Strings. A string is a sequence of characters. They can be delimited\n",
    "# by single quotes ('blah') or double quotes (\"blah blah\")\n",
    "x = \"four\"\n",
    "print('The type of x is:')\n",
    "print(type(x))\n",
    "print() # Just to give a space.\n",
    "print('The value of x is:')\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conversions\n",
    "Python has a built-in command `float()` that can convert integers and certain strings to floating point numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The type of x is:\n",
      "<class 'int'>\n",
      "\n",
      "The value of x is:\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "# Start with an int.\n",
    "x = 4;\n",
    "\n",
    "print('The type of x is:')\n",
    "print(type(x))\n",
    "print()\n",
    "print('The value of x is:')\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The type of x is:\n",
      "<class 'float'>\n",
      "\n",
      "The value of x is:\n",
      "4.0\n"
     ]
    }
   ],
   "source": [
    "# Convert to float\n",
    "x = float(x)\n",
    "print('The type of x is:')\n",
    "print(type(x))\n",
    "print()\n",
    "print('The value of x is:')\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The type of x is:\n",
      "<class 'str'>\n",
      "\n",
      "The value of x is:\n",
      "1.67\n"
     ]
    }
   ],
   "source": [
    "# Can also convert strings representing floating point numbers to\n",
    "# float.\n",
    "x = '1.67'\n",
    "print('The type of x is:')\n",
    "print(type(x))\n",
    "print()\n",
    "print('The value of x is:')\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The type of x is:\n",
      "<class 'float'>\n",
      "\n",
      "The value of x is:\n",
      "1.67\n"
     ]
    }
   ],
   "source": [
    "# Note that when we print the string version of x it prints it just\n",
    "# as if it were a floating point number, i.e., we can't tell from the\n",
    "# output.\n",
    "x = float(x)\n",
    "print('The type of x is:')\n",
    "print(type(x))\n",
    "print()\n",
    "print('The value of x is:')\n",
    "print(x)"
   ]
  },
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   "metadata": {},
   "source": [
    "There is also a python command `int()`, which can covert floats to integer and certain strings to integer, and a command `str()`, which converts numbers to strings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boolean type\n",
    "A Boolean value has a python type **bool**. The possible values a Boolean variable can take are: **True** and **False**. These are typically used to hold the results of logical tests, which in turn can be used to control the flow of a python program."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "The type of x is:\n",
      "<class 'bool'>\n",
      "\n",
      "The value of x is:\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "x = True;\n",
    "print('The type of x is:')\n",
    "print(type(x))\n",
    "print()\n",
    "print('The value of x is:')\n",
    "print(x)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collection data types\n",
    "There are four **collection** data types: **lists**, **tuples**, **sets**, and **dictionaries**. (Some say that a **string** is a collection data type since it is a ordered set of characters). For now we will only consider lists and sets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <u>Lists</u> are ordered, changeable, and allow duplicate members:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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   "outputs": [
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     "name": "stdout",
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     "text": [
      "The type of Coloradothings is:\n",
      "<class 'list'>\n",
      "\n",
      "The length of Coloradothings is:\n",
      "5\n",
      "\n",
      "The value of Coloradothings is:\n",
      "['wheat', 'corn', 'sugar beets', 'pinto beans', 1959]\n"
     ]
    }
   ],
   "source": [
    "# Create a list with 5 elements.\n",
    "Coloradothings = [\"wheat\", \"corn\", \"sugar beets\", \"pinto beans\", 1959]\n",
    "print('The type of Coloradothings is:')\n",
    "print(type(Coloradothings))\n",
    "print()\n",
    "print('The length of Coloradothings is:')\n",
    "print(len(Coloradothings))\n",
    "print()\n",
    "print('The value of Coloradothings is:')\n",
    "print(Coloradothings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For Coloradothings[3] ...\n",
      "pinto beans\n",
      "<class 'str'>\n",
      "\n",
      "For Coloradothings[4] ...\n",
      "1959\n",
      "<class 'int'>\n"
     ]
    }
   ],
   "source": [
    "# The elements inside of Coloradothings may be of differing\n",
    "# types ...\n",
    "print('For Coloradothings[3] ...')\n",
    "print(Coloradothings[3])\n",
    "print(type(Coloradothings[3]))\n",
    "print()\n",
    "print('For Coloradothings[4] ...')\n",
    "print(Coloradothings[4])\n",
    "print(type(Coloradothings[4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['wheat', 'corn', 'sugar beets', 'pinto beans', 1959, 'Amherst']\n",
      "\n",
      "['wheat', 'corn', 'sunflowers', 'sugar beets', 'pinto beans', 1959, 'Amherst']\n"
     ]
    }
   ],
   "source": [
    "# We can append to a list and insert in a list\n",
    "\n",
    "Coloradothings.append(\"Amherst\")\n",
    "print(Coloradothings)\n",
    "print()\n",
    "Coloradothings.insert(2, \"sunflowers\")\n",
    "print(Coloradothings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <u>Sets</u> are unordered, changeable (in the sense that we can add and remove items from sets). Sets do not allow duplicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'set'>\n",
      "{'professors', 'students', 'study sessions', 'Ag and Bio Engineering', 'Ross-Ade Stadium', 'Gene Keady'}\n",
      "\n",
      "professors\n",
      "students\n",
      "study sessions\n",
      "Ag and Bio Engineering\n",
      "Ross-Ade Stadium\n",
      "Gene Keady\n",
      "\n",
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# Make a set.\n",
    "Purduethings = {\"Ag and Bio Engineering\", \"Ross-Ade Stadium\", \"students\", \"professors\", \"Gene Keady\", \"study sessions\"}\n",
    "print(type(Purduethings))\n",
    "print(Purduethings) # Note the order it prints\n",
    "print()\n",
    "\n",
    "for x in Purduethings: # Note the order with which the for loop executes\n",
    "    print(x)\n",
    "\n",
    "print()\n",
    "print(\"Ag and Bio Engineering\" in Purduethings)\n",
    "print(\"Medical School\" in Purduethings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the code output above we note:\n",
    "1. The order in which we included the set items when defining it is not the order that python used to enumerate the items when printing. Just FYI.\n",
    "2. The statement in the last print command: `\"Ag and Bio Engineering\" in Purduethings` is a Boolean variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can perform classical set operations (**union**, **intersection**, **difference**, **test subset**):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Purduethings union IUthings equals:\n",
      "{'parties', 'professors', 'Hoosiers', 'students', 'study sessions', 'Ag and Bio Engineering', 'Ross-Ade Stadium', 'Bobby Knight', 'Gene Keady'}\n",
      "\n",
      "Purduethings intersection IUthings equals:\n",
      "{'students', 'professors'}\n",
      "\n",
      "IUthings not also in Purduethings equals:\n",
      "{'Hoosiers', 'parties', 'Bobby Knight'}\n",
      "\n",
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# Make another set ...\n",
    "IUthings = {\"Hoosiers\", \"Bobby Knight\", \"students\", \"professors\", \"parties\"}\n",
    "print('Purduethings union IUthings equals:')\n",
    "print(Purduethings.union(IUthings))\n",
    "print()\n",
    "print('Purduethings intersection IUthings equals:')\n",
    "print(Purduethings.intersection(IUthings))\n",
    "print()\n",
    "print('IUthings not also in Purduethings equals:')\n",
    "print(IUthings.difference(Purduethings))\n",
    "print()\n",
    "print({\"Gene Cernan\",}.issubset(Purduethings))\n",
    "Purduethings.add(\"Gene Cernan\")\n",
    "print({\"Gene Cernan\",}.issubset(Purduethings))\n",
    "\n",
    "#print(Purduethings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This is Where I Stopped on Lecture 1\n",
    "# Lab 1 Picked Up Here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Operators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Arithmetic operators: +, -, *, /, %, **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n",
      "2\n",
      "35\n",
      "1.4\n",
      "2\n",
      "16807\n"
     ]
    }
   ],
   "source": [
    "# Arithmetic operators: +, -, *, /, %, **\n",
    "\n",
    "print(7 + 5)  # addition\n",
    "print(7 - 5)  # subtraction\n",
    "print(7 * 5)  # multiplication\n",
    "print(7 / 5)  # division\n",
    "print(7 % 5)  # remainder upon integer division\n",
    "print(7 ** 5) # exponentiation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assignment operators: =, +=, -=, *=, /=, **="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "10\n",
      "5\n",
      "25\n",
      "5.0\n",
      "3125.0\n"
     ]
    }
   ],
   "source": [
    "# Assignment operators: =, +=, -=, *=, /=, **=\n",
    "\n",
    "b = 5\n",
    "a = b\n",
    "print(a)\n",
    "a += b # shorthand for a = a + b\n",
    "print(a)\n",
    "a -= b # shorthand for a = a - b\n",
    "print(a)\n",
    "a *= b # shorthand for a = a*b\n",
    "print(a)\n",
    "a /= b # shorthand for a = a/b\n",
    "print(a)\n",
    "a **= b # shorthand for a = a**b\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison operators: ==, !=, <, <=, >, >="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n",
      "False\n",
      "False\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# Comparison operators: ==, !=, <, <=, >, >=\n",
    "\n",
    "a = 3\n",
    "b = 2\n",
    "\n",
    "print(a == b)\n",
    "print(a != b)\n",
    "print(a < b)\n",
    "print(a <= b)\n",
    "print(a > b)\n",
    "print(a >= b)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logical operators: and, or, not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'bool'>\n",
      "False\n",
      "\n",
      "<class 'bool'>\n",
      "True\n",
      "\n",
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# Logical operators: and, or, not\n",
    "\n",
    "x = (a == b) # The expression a == b it a Boolean value (either True or False).\n",
    "             # The assignment creates a Boolean variable x\n",
    "print(type(x))\n",
    "print(x)\n",
    "\n",
    "print()\n",
    "\n",
    "y = not(x)\n",
    "print(type(y))\n",
    "print(y)\n",
    "\n",
    "print()\n",
    "\n",
    "z = True\n",
    "\n",
    "print(x or z)\n",
    "print(x and z)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 1 Ended Here ...."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Control statements\n",
    "There are three methods of program control that we consider here:\n",
    "1. If/else statement\n",
    "2. For loops\n",
    "3. While loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a is greater than b\n"
     ]
    }
   ],
   "source": [
    "# Example if/else statement\n",
    "\n",
    "a = 5;\n",
    "b = 3;\n",
    "if b > a:\n",
    "    print(\"b is greater than a\")\n",
    "elif a == b:\n",
    "    print(\"a and b are equal\")\n",
    "else:\n",
    "    print(\"a is greater than b\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "# While loop: Execute while condition is true.\n",
    "\n",
    "i = 1\n",
    "while i < 6:\n",
    "    print(i)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b\n",
      "a\n",
      "n\n",
      "a\n",
      "n\n",
      "a\n",
      "\n",
      "\n",
      "Try continue command\n",
      "\n",
      "\n",
      "b\n",
      "a\n",
      "a\n",
      "a\n"
     ]
    }
   ],
   "source": [
    "# For loop: Iterate over a sequence. Also, have break (stop a loop where it is \n",
    "# and exit) and continue (move to the next iteration of loop).\n",
    "\n",
    "for x in \"banana\":\n",
    "    print(x)\n",
    "    \n",
    "print(\"\\n\")    \n",
    "print(\"Try continue command\")\n",
    "print(\"\\n\")\n",
    "\n",
    "for x in \"banana\":\n",
    "    if x == \"n\":\n",
    "        continue\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions\n",
    "   \n",
    "Function are blocks of code that run when called. \n",
    "\n",
    "- Can pass parameters to a function. \n",
    "- A function can return a value.\n",
    "\n",
    "Functions allow code to be more readable by allowing the hiding of details of an operation that may not be central to the understanding of the overall algorithm. Sometimes, this is called encapsulation. For example, perhaps we want to solve some sort of geometric problem, such as finding the height of a tree from the angle of the sun and the length of the shadow cast on the ground. The height calculation will involve intermediate calculations of trigonometric functions of the angle (e.g., sine, cosine, tangent). These sorts of intermediate calculations are naturally left to functions in python and other programming languages.\n",
    "\n",
    "In addition, functions ...\n",
    "\n",
    "- Assist in divide and conquer problem solving.\n",
    "- Allow to reuse the function code in other parts of a larger program."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Useful Function Example\n",
    "\n",
    "According to wikipedia this algorithm goes back to the Babylonians (100 AD) and is widely used for computing square roots by hand. The idea is this. If we want to find the square root of a positive number, say $Z$, we first start with a guess $x$ hoping $x^2 \\approx Z$. Now if the original guess is too large, i.e., $x^2 > Z$ then $x > Z/x$ and so we could move in the correct direction (towards smaller values of $x$) by making a new guess equal to the average of $x$ and $Z/x$, i.e.,\n",
    "\n",
    "New guess = $(x + Z/x)/2$.\n",
    "\n",
    "If, on the other hand, the original quess was too small, i.e., $x^2 < Z$ then $x < Z/x$ and using the above formula for the new guess would move in the correct direction of larger values. The algorithm is implemented in python in the function code below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hand calculation example ...\n",
    "\n",
    "Say Z = 10 and guess x = 3 for the square root. Then the next guess is the average of 3 and 3.3333..., which is approximately 3.16666... The next step in the algorithm gives an estimate of\n",
    "\n",
    "3.1622"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0.9450000000000001\n",
      "0.9433994708994708\n",
      "0.9433981132066374\n",
      "0.9433981132056604\n",
      "0.9433981132056604\n",
      "0.9433981132056604\n",
      "0.9433981132056604\n"
     ]
    }
   ],
   "source": [
    "Z = .89;\n",
    "x = [1, 1, 1, 1, 1, 1, 1, 1];\n",
    "N = len(x);\n",
    "print(x[0])\n",
    "i = 1;\n",
    "while i < N:\n",
    "    x[i] = (x[i-1] + Z/x[i-1])/2\n",
    "    print(x[i])\n",
    "    i = i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Z is the positive number for which we want the square root. epsilon\n",
    "# is the tolerance in the accuracy of the result.\n",
    "\n",
    "def Newtroot(Z,epsilon):\n",
    "    x = 1\n",
    "    xp = (x + Z/x)/2\n",
    "    e = (xp - x)/x\n",
    "    while (e > epsilon) or (-epsilon > e):\n",
    "        x = xp\n",
    "        xp = (x + Z/x)/2\n",
    "        e = (xp - x)/x\n",
    "    return xp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.162277660168379\n"
     ]
    }
   ],
   "source": [
    "# Example of the square root algorithm\n",
    "\n",
    "z = 10\n",
    "epsilon = 1e-12\n",
    "\n",
    "print(Newtroot(z,epsilon))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The math package\n",
    "Python provides many modules designed for specialized programming tasks. See: <a href=\"https://pypi.org\" target=\"_blank\">The Python Package Index</a>.\n",
    "\n",
    "The math package contains trigonometric, exponential, logarithmic, hyperbolic, and special functions. It also contains a number of useful constants such as `pi` and `e`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.141592653589793\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "P = math.pi;\n",
    "print(P)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the math package to create something interesting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.0\n",
      "0.12566370614359174 0.12533323356430426\n",
      "0.25132741228718347 0.2486898871648548\n",
      "0.37699111843077515 0.3681245526846779\n",
      "0.5026548245743669 0.4817536741017153\n",
      "0.6283185307179586 0.5877852522924731\n",
      "0.7539822368615503 0.6845471059286886\n",
      "0.8796459430051421 0.7705132427757891\n",
      "1.0053096491487339 0.8443279255020151\n",
      "1.1309733552923256 0.9048270524660196\n",
      "1.2566370614359172 0.9510565162951535\n",
      "1.382300767579509 0.9822872507286886\n",
      "1.5079644737231006 0.9980267284282716\n",
      "1.6336281798666925 0.9980267284282716\n",
      "1.7592918860102842 0.9822872507286887\n",
      "1.8849555921538756 0.9510565162951536\n",
      "2.0106192982974678 0.9048270524660195\n",
      "2.1362830044410597 0.844327925502015\n",
      "2.261946710584651 0.7705132427757893\n",
      "2.3876104167282426 0.6845471059286888\n",
      "2.5132741228718345 0.5877852522924732\n",
      "2.6389378290154264 0.4817536741017152\n",
      "2.764601535159018 0.36812455268467814\n",
      "2.8902652413026093 0.24868988716485524\n",
      "3.015928947446201 0.12533323356430454\n",
      "3.141592653589793 1.2246467991473532e-16\n",
      "3.267256359733385 -0.1253332335643043\n",
      "3.3929200658769765 -0.24868988716485457\n",
      "3.5185837720205684 -0.3681245526846779\n",
      "3.6442474781641603 -0.4817536741017154\n",
      "3.7699111843077513 -0.5877852522924727\n",
      "3.895574890451343 -0.6845471059286884\n",
      "4.0212385965949355 -0.7705132427757894\n",
      "4.1469023027385274 -0.8443279255020153\n",
      "4.272566008882119 -0.9048270524660198\n",
      "4.39822971502571 -0.9510565162951535\n",
      "4.523893421169302 -0.9822872507286887\n",
      "4.649557127312894 -0.9980267284282716\n",
      "4.775220833456485 -0.9980267284282716\n",
      "4.900884539600077 -0.9822872507286887\n",
      "5.026548245743669 -0.9510565162951536\n",
      "5.15221195188726 -0.9048270524660199\n",
      "5.277875658030853 -0.8443279255020151\n",
      "5.403539364174444 -0.7705132427757896\n",
      "5.529203070318036 -0.684547105928689\n",
      "5.654866776461628 -0.5877852522924732\n",
      "5.780530482605219 -0.4817536741017161\n",
      "5.906194188748811 -0.3681245526846778\n",
      "6.031857894892402 -0.24868988716485535\n",
      "6.157521601035994 -0.12533323356430467\n"
     ]
    }
   ],
   "source": [
    "# This short program will print a table with 50 rows where\n",
    "# each row contains an argument and the sine of the argument.\n",
    "# The sine function will trace on complete period of length\n",
    "# 2*pi.\n",
    "N = 50;\n",
    "for k in range(N):\n",
    "    t = 2*P*k/N;\n",
    "    print(t, math.sin(t))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table above is difficult to read because it is not well formatted. Python has tools to simplify formating with the print command. There is a nice explanation in the Data Mind notes mentioned in the introduction. One prefered method uses `f-strings`, which stands for **format string**. They are indicated by preceeding the string with `f` or `F`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of pi is approximately 3.141592653589793\n",
      "\n",
      "The value of pi is approximately 3.142.\n"
     ]
    }
   ],
   "source": [
    "# Compare two ways of printing pi\n",
    "print('The value of pi is approximately', P)\n",
    "print()\n",
    "print(f'The value of pi is approximately {math.pi:.3f}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a prettier (more readable) sinewave table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 0.00     0.000\n",
      " 0.13     0.125\n",
      " 0.25     0.249\n",
      " 0.38     0.368\n",
      " 0.50     0.482\n",
      " 0.63     0.588\n",
      " 0.75     0.685\n",
      " 0.88     0.771\n",
      " 1.01     0.844\n",
      " 1.13     0.905\n",
      " 1.26     0.951\n",
      " 1.38     0.982\n",
      " 1.51     0.998\n",
      " 1.63     0.998\n",
      " 1.76     0.982\n",
      " 1.88     0.951\n",
      " 2.01     0.905\n",
      " 2.14     0.844\n",
      " 2.26     0.771\n",
      " 2.39     0.685\n",
      " 2.51     0.588\n",
      " 2.64     0.482\n",
      " 2.76     0.368\n",
      " 2.89     0.249\n",
      " 3.02     0.125\n",
      " 3.14     0.000\n",
      " 3.27    -0.125\n",
      " 3.39    -0.249\n",
      " 3.52    -0.368\n",
      " 3.64    -0.482\n",
      " 3.77    -0.588\n",
      " 3.90    -0.685\n",
      " 4.02    -0.771\n",
      " 4.15    -0.844\n",
      " 4.27    -0.905\n",
      " 4.40    -0.951\n",
      " 4.52    -0.982\n",
      " 4.65    -0.998\n",
      " 4.78    -0.998\n",
      " 4.90    -0.982\n",
      " 5.03    -0.951\n",
      " 5.15    -0.905\n",
      " 5.28    -0.844\n",
      " 5.40    -0.771\n",
      " 5.53    -0.685\n",
      " 5.65    -0.588\n",
      " 5.78    -0.482\n",
      " 5.91    -0.368\n",
      " 6.03    -0.249\n",
      " 6.16    -0.125\n"
     ]
    }
   ],
   "source": [
    "# Prettier sine wave table.\n",
    "N = 50;\n",
    "for k in range(N):\n",
    "    t = 2*P*k/N;\n",
    "    print(f'{t: 1.2f}', '  ', f'{math.sin(t): .3f}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The NumPy Package"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All data manipulated by a computer is represented in binary. In otherwords, via one method or another, all data -- temperature sensor readings, hourly barometric pressure from your Davis weather station, an audio file, images from your Bushnell game camera, a yield map -- are represented as arrays of numbers.\n",
    "\n",
    "**NumPy (Numerical Python)** provides an efficient interface to store and compute on dense data buffers. NumPy arrays are much more efficient than Python's built-in list data type.\n",
    "\n",
    "See: <a href=\"http://www.numpy.org\" target=\"_blank\">The Numpy Package</a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the numpy package. This command allows us to refer to numpy\n",
    "# commands using the shorthand \"np\".\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting examples with Matplotlib\n",
    "A better way to plot. Who says there has been no progress in the world since 1970?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import matplotlib and define a shorthand\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  1,  6,  5,  4, 11, -1,  1,  2,  6])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]);\n",
    "y = np.array([3, 1, 6, 5, 4, 11, -1, 1, 2, 6]);\n",
    "\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.style.use('classic')\n",
    "plt.plot(x, y)\n",
    "plt.title(\"Example Plot\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f99702435b0>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 100)\n",
    "fig2 = plt.figure()\n",
    "plt.style.use('seaborn-dark-palette')\n",
    "plt.plot(x, np.sin(x))\n",
    "plt.plot(x, np.cos(x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## United states wheat yield trends as a plotting example ...\n",
    "\n",
    "Data was obtained from the United States Department of Agriculture: <a href=\"https://quickstats.nass.usda.gov/\" target=\"_blank\">Quick Stats</a> (The USDA's National Ag Statistics Service -- we will make more use of this in the lab later)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The years for which we have average wheat yield data.\n",
    "dates = np.array([2021, 2020, 2019, 2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2005, 2004, 2003, 2002, 2001, 2000,1999, 1998, 1997, 1996, 1995, 1994, 1993, 1992, 1991, 1990, 1989, 1988, 1987, 1986, 1985, 1984, 1983, 1982, 1981, 1980, 1979, 1978, 1977, 1976, 1975, 1974, 1973, 1972, 1971, 1970, 1969, 1968, 1967,1966, 1965, 1964, 1963, 1962, 1961, 1960, 1959, 1958, 1957, 1956, 1955, 1954, 1953, 1952, 1951, 1950, 1949, 1948, 1947, 1946, 1945,1944, 1943, 1942, 1941, 1940, 1939, 1938, 1937, 1936, 1935, 1934, 1933, 1932, 1931, 1930, 1929, 1928, 1927, 1926, 1925, 1924, 1923,1922, 1921, 1920, 1919, 1918, 1917, 1916, 1915, 1914, 1913, 1912,1911, 1910, 1909, 1908, 1907, 1906, 1905, 1904, 1903, 1902, 1901,1900, 1899, 1898, 1897, 1896, 1895, 1894, 1893, 1892, 1891, 1890,1889, 1888, 1887, 1886, 1885, 1884, 1883, 1882, 1881, 1880, 1879,1878, 1877, 1876, 1875, 1874, 1873, 1872, 1871, 1870, 1869, 1868, 1867, 1866])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2021, 2020, 2019, 2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011,\n",
       "       2010, 2009, 2008, 2007, 2006, 2005, 2004, 2003, 2002, 2001, 2000,\n",
       "       1999, 1998, 1997, 1996, 1995, 1994, 1993, 1992, 1991, 1990, 1989,\n",
       "       1988, 1987, 1986, 1985, 1984, 1983, 1982, 1981, 1980, 1979, 1978,\n",
       "       1977, 1976, 1975, 1974, 1973, 1972, 1971, 1970, 1969, 1968, 1967,\n",
       "       1966, 1965, 1964, 1963, 1962, 1961, 1960, 1959, 1958, 1957, 1956,\n",
       "       1955, 1954, 1953, 1952, 1951, 1950, 1949, 1948, 1947, 1946, 1945,\n",
       "       1944, 1943, 1942, 1941, 1940, 1939, 1938, 1937, 1936, 1935, 1934,\n",
       "       1933, 1932, 1931, 1930, 1929, 1928, 1927, 1926, 1925, 1924, 1923,\n",
       "       1922, 1921, 1920, 1919, 1918, 1917, 1916, 1915, 1914, 1913, 1912,\n",
       "       1911, 1910, 1909, 1908, 1907, 1906, 1905, 1904, 1903, 1902, 1901,\n",
       "       1900, 1899, 1898, 1897, 1896, 1895, 1894, 1893, 1892, 1891, 1890,\n",
       "       1889, 1888, 1887, 1886, 1885, 1884, 1883, 1882, 1881, 1880, 1879,\n",
       "       1878, 1877, 1876, 1875, 1874, 1873, 1872, 1871, 1870, 1869, 1868,\n",
       "       1867, 1866])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The average wheat yield data in the order to line up with the corresponding years.\n",
    "yields = np.array([44.5, 49.7, 51.7, 47.6, 46.4, 52.7, 43.6, 43.7, 47.1, 46.2, 43.6, 46.1, 44.3, 44.8, 40.2, 38.6, 42. , 43.2, 44.2, 35. , 40.2, 42. , 42.7, 43.2, 39.5, 36.3, 35.8, 37.6, 38.2, 39.3, 34.3, 39.5, 32.7, 34.1, 37.7, 34.4, 37.5, 38.8, 39.4, 35.5, 34.5, 33.5, 34.2, 31.4, 30.7, 30.3, 30.6, 27.3, 31.6, 32.7, 33.9, 31. , 30.6, 28.4, 25.8, 26.3, 26.5, 25.8, 25.2, 25. , 23.9, 26.1, 21.6, 27.5, 21.8, 20.2, 19.8, 18.1, 17.3, 18.4, 16. , 16.5, 14.5, 17.9, 18.2, 17.2, 17. , 17.7, 16.4, 19.5, 16.8, 15.3, 14.1, 13.3, 13.6, 12.8, 12.2, 12.1, 11.2, 13.1, 16.3, 14.2, 13. , 15.4, 14.7, 14.7, 12.8, 16. , 13.3, 13.8, 12.7, 13.5, 12.9, 14.8, 13.2, 11.9, 16.7, 16.1, 14.4, 15.1, 12.4, 13.7, 15.5, 14.3, 14.2, 16. , 15.2, 12.9, 13.7, 14.9, 15. , 12.2, 12.5, 15.2, 14. , 12.8, 13.9, 13.5, 12.4, 14.2, 16.5, 12.2, 14. , 12.1, 13.3, 14.1, 11.4, 14.8, 12.3, 15.1, 11. , 13.2, 13. , 13.5, 14.1, 10.9, 11.1, 13. , 12.9, 11.8, 12.2, 12.1, 13.7, 12.9, 12.6, 11. ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([44.5, 49.7, 51.7, 47.6, 46.4, 52.7, 43.6, 43.7, 47.1, 46.2, 43.6,\n",
       "       46.1, 44.3, 44.8, 40.2, 38.6, 42. , 43.2, 44.2, 35. , 40.2, 42. ,\n",
       "       42.7, 43.2, 39.5, 36.3, 35.8, 37.6, 38.2, 39.3, 34.3, 39.5, 32.7,\n",
       "       34.1, 37.7, 34.4, 37.5, 38.8, 39.4, 35.5, 34.5, 33.5, 34.2, 31.4,\n",
       "       30.7, 30.3, 30.6, 27.3, 31.6, 32.7, 33.9, 31. , 30.6, 28.4, 25.8,\n",
       "       26.3, 26.5, 25.8, 25.2, 25. , 23.9, 26.1, 21.6, 27.5, 21.8, 20.2,\n",
       "       19.8, 18.1, 17.3, 18.4, 16. , 16.5, 14.5, 17.9, 18.2, 17.2, 17. ,\n",
       "       17.7, 16.4, 19.5, 16.8, 15.3, 14.1, 13.3, 13.6, 12.8, 12.2, 12.1,\n",
       "       11.2, 13.1, 16.3, 14.2, 13. , 15.4, 14.7, 14.7, 12.8, 16. , 13.3,\n",
       "       13.8, 12.7, 13.5, 12.9, 14.8, 13.2, 11.9, 16.7, 16.1, 14.4, 15.1,\n",
       "       12.4, 13.7, 15.5, 14.3, 14.2, 16. , 15.2, 12.9, 13.7, 14.9, 15. ,\n",
       "       12.2, 12.5, 15.2, 14. , 12.8, 13.9, 13.5, 12.4, 14.2, 16.5, 12.2,\n",
       "       14. , 12.1, 13.3, 14.1, 11.4, 14.8, 12.3, 15.1, 11. , 13.2, 13. ,\n",
       "       13.5, 14.1, 10.9, 11.1, 13. , 12.9, 11.8, 12.2, 12.1, 13.7, 12.9,\n",
       "       12.6, 11. ])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the wheat yield trend\n",
    "fig = plt.figure()\n",
    "plt.style.use('classic')\n",
    "plt.plot(dates, yields)\n",
    "plt.title(\"United States Average Wheat Yield by Year\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Yield in bushels per acre\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}