practice-sets.ipynb

practice-sets.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Practice Sets\n",
        "**Ryan D. Watts** <br/>\n",
        "**WGU - Introduction to Python `C859`**"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "pd.options.display.html.table_schema = True"
      ],
      "outputs": [],
      "execution_count": 41,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "tags": []
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Lesson 1"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# declare 3 variables with one assignment statement and assign each one an integer value\n",
        "one, two, three = 1, 2, 3\n",
        "print(one, two, three)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 2 3\n"
          ]
        }
      ],
      "execution_count": 45,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# convert each of your previous variables to float objects\n",
        "one = float(one)\n",
        "two = float(two)\n",
        "three = float(three)\n",
        "\n",
        "print(one, type(one))\n",
        "print(two, type(two))\n",
        "print(three, type(three))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0 <class 'float'>\n",
            "2.0 <class 'float'>\n",
            "3.0 <class 'float'>\n"
          ]
        }
      ],
      "execution_count": 55,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# convert each of your previous variable to string objects\n",
        "one = str(one)\n",
        "two = str(two)\n",
        "three = str(three)\n",
        "\n",
        "print(one, type(one))\n",
        "print(two, type(two))\n",
        "print(three, type(three))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0 <class 'str'>\n",
            "2.0 <class 'str'>\n",
            "3.0 <class 'str'>\n"
          ]
        }
      ],
      "execution_count": 61,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# print the result of dividing 783.56 by 123.2 and ensure that the answer results in an integer\n",
        "# expected outcome: 6\n",
        "divisible = int(783.56 / 123.2)\n",
        "\nprint(divisible, type(divisible))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6 <class 'int'>\n"
          ]
        }
      ],
      "execution_count": 65,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# determine if 2019 is a leap year and print the result\n",
        "# expected outcome: 3\n",
        "leapyear = 2019 % 4\n",
        "\nprint(leapyear)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n"
          ]
        }
      ],
      "execution_count": 66,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# print the calculated length of myFirstString\n",
        "# expected outcome: 35\n",
        "myFirstString = 'I love working with Python so much!'\n",
        "print(len(myFirstString))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "35\n"
          ]
        }
      ],
      "execution_count": 68,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create a string value and print it with the first letter of each word capitalized using a Python method\n",
        "# expected outcome: varies based on your string value\n",
        "TitleCase = 'my first title case string.'\n",
        "print(TitleCase.title())"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "My First Title Case String.\n"
          ]
        }
      ],
      "execution_count": 70,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# use the given variable to construct a python statement that counts how many times the word piza is used. Print the final count.\n",
        "# expected outcome: True or False\n",
        "commercial = 'In the Little Ceasars pizza commercial the character says, \"pizza, pizza\"!'\n",
        "print(commercial.count('pizza'))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n"
          ]
        }
      ],
      "execution_count": 72,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the given username and phone to create a message that lets the user know that you will be calling\n",
        "# at a specified number for your appointment. Use the format method to insert data into the printed message.\n",
        "# expected outcome: Hi, Allen. I will call you at 888-555-0011 for our appointment.\n",
        "username = 'Allen'\n",
        "phone = 8885550011\n",
        "parsedPhone = format(int(str(phone)[:-1]), \",\").replace(\",\", \"-\") + str(phone)[-1] # solution from Stack Overflow https://stackoverflow.com/questions/7058120/whats-the-best-way-to-format-a-phone-number-in-python\n",
        "message = 'Hi, {}. I will call you at {} for our appointment.'.format(username, parsedPhone)\n",
        "print(message)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hi, Allen. I will call you at 888-555-0011 for our appointment.\n"
          ]
        }
      ],
      "execution_count": 73,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Lesson 2"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# create a function wich receives two integers as input, adds them and returns the sum\n",
        "def add_ints(a, b):\n",
        "    return a + b"
      ],
      "outputs": [],
      "execution_count": 74,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# run your function and print the result with integers 7 and 9\n",
        "# expected outcome: 16\n",
        "print(add_ints(7, 9))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16\n"
          ]
        }
      ],
      "execution_count": 76,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# run your function and print the result with integers 20 and 49\n",
        "# expected outcome: 69\n",
        "print(add_ints(20, 49))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "69\n"
          ]
        }
      ],
      "execution_count": 77,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Run your function with integers 2 and 8, and save the output to a new variable called myNewSum. Print myNewSum.\n",
        "# expected outcome: 10\n",
        "myNewSum = add_ints(2, 8)\n",
        "print(myNewSum)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10\n"
          ]
        }
      ],
      "execution_count": 80,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# You are provided a student's score on the recent exam.\n",
        "# Create a function that will print a reply based on the score.\n",
        "# Students who score 90 points or more receive an A and pass the course.\n",
        "# Students receiving 80 points or more receive a B and pass the course.\n",
        "# Students receiving 79 points do not pass and need to retake the exam.\n",
        "# Students receiving a score of 0 have not attempted the exam and need instructions to schedule.\n",
        "\n",
        "def student_reply(score = 0):\n",
        "\ta = 'You received an A and have passed the course.'\n",
        "\tb = 'You received a B and have passed the course.'\n",
        "\tc = 'You have not passed the course. Please retake the exam.'\n",
        "\td = 'You have not attempted the exam and need instructions to schedule.'\n",
        "\tif score == 0:\n",
        "\t\treturn d\n",
        "\telif score <= 79:\n",
        "\t\treturn c\n",
        "\telif (score >= 80) and (score <= 89):\n",
        "\t\treturn b\n",
        "\telif score >= 90:\n",
        "\t\treturn a"
      ],
      "outputs": [],
      "execution_count": 91,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Run the function with a score of 90 and print the result\n",
        "# expected outcome: You received an A and have passed the course.\n",
        "print(student_reply(90))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You received an A and have passed the course.\n"
          ]
        }
      ],
      "execution_count": 86,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Run the function with a score of 70 and print the result\n",
        "# expected outcome: You have not passed the course. Please retake the exam.\n",
        "print(student_reply(70))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You have not passed the course. Please retake the exam.\n"
          ]
        }
      ],
      "execution_count": 87,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Bonus Practice A\n",
        "print(student_reply(84))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You received a B and have passed the course.\n"
          ]
        }
      ],
      "execution_count": 88,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Bonus Practice B\n",
        "# This practice ensures that if a score is received of 0 or if the method was called without passing a score the default value of 0 is passed\n",
        "print(student_reply())"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You have not attempted the exam and need instructions to schedule.\n"
          ]
        }
      ],
      "execution_count": 93,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Lesson 3"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# create a list containing the names Dana, Connie, Jessica, Mike, and Dana\n",
        "mylist = ['Dana', 'Connie', 'Jessica', 'Mike', 'Dana']"
      ],
      "outputs": [],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the length of the list.\n",
        "# expected outcome: 5\n",
        "print(len(mylist))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5\n"
          ]
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check to see if Candice is in the list. If not in the list, add her and print the list.\n",
        "# expected outcome: ['Dana', 'Connie', 'Jessica', 'Mike', 'Dana', 'Candice']\n",
        "if mylist.count('Candice') == 0:\n",
        "    mylist.append('Candice')\n",
        "print(mylist)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Dana', 'Connie', 'Jessica', 'Mike', 'Dana', 'Candice']\n"
          ]
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create and print a single string containing all of the names separated by commas\n",
        "# expected outcome: Dana, Connie, Jessica, Mike, Dana, Candice\n",
        "print(', '.join(mylist))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dana, Connie, Jessica, Mike, Dana, Candice\n"
          ]
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ensure that each name is only listed once and print the list of unique values\n",
        "# expected outcome: ['Dana', 'Connie', 'Jessica', 'Mike', 'Candice'] *note: order of items in list may vary\n",
        "mylist_unique = list(set(mylist))\n",
        "print(mylist_unique)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Connie', 'Jessica', 'Dana', 'Candice', 'Mike']\n"
          ]
        }
      ],
      "execution_count": 19,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create an individual message for each unique name and welcome them to WGU\n",
        "# expected outcome: Welcome to WGU, Dana\n",
        "#                   Welcome to WGU, Jessica\n",
        "#                   Welcome to WGU, Mike\n",
        "#                   Welcome to WGU, Candice\n",
        "#                   Welcome to WGU, Connie\n",
        "for person in mylist_unique:\n",
        "    print('Welcome to WGU, {}'.format(person))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Welcome to WGU, Connie\n",
            "Welcome to WGU, Jessica\n",
            "Welcome to WGU, Dana\n",
            "Welcome to WGU, Candice\n",
            "Welcome to WGU, Mike\n"
          ]
        }
      ],
      "execution_count": 21,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# given the following dictionary of employees and salaries, create an personalized salary message letting each employee\n",
        "# know they have been given a 2% raise and the new total of their salary.\n",
        "# expected outcome: John, your current salary is 54000.00. You received a 2% raise. This makes your new salary 55080.0\n",
        "#                   Judy, your current salary is 71000.00. You received a 2% raise. This makes your new salary 72420.0\n",
        "#                   Albert, your current salary is 38000.00. You received a 2% raise. This makes your new salary 38760.0\n",
        "#                   Alfonzo, your current salary is 42000.00. You received a 2% raise. This makes your new salary 42840.0\n",
        "employeeDatabase = {\n",
        "    'John': 54000.00,\n",
        "    'Judy': 71000.00,\n",
        "    'Albert': 38000.00,\n",
        "    'Alfonzo': 42000.00\n",
        "}\n",
        "for emp, sal in employeeDatabase.items():\n",
        "    newSal = (sal * .02) + sal\n",
        "    message = '{}, your current salary is {}. You received a 2% raise. This makes your new salary {}'.format(emp, sal, newSal)\n",
        "    print(message)\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "John, your current salary is 54000.0. You received a 2% raise. This makes your new salary 55080.0\n",
            "Judy, your current salary is 71000.0. You received a 2% raise. This makes your new salary 72420.0\n",
            "Albert, your current salary is 38000.0. You received a 2% raise. This makes your new salary 38760.0\n",
            "Alfonzo, your current salary is 42000.0. You received a 2% raise. This makes your new salary 42840.0\n"
          ]
        }
      ],
      "execution_count": 45,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# starting with year 2000, create a list containing 5 leap years\n",
        "# when the list is complete, print the full list with a message\n",
        "# expected outcome: These are the leap years: [2000, 2004, 2008, 2012, 2016]\n",
        "leap_years = []\n",
        "i = 2000\n",
        "while len(leap_years) < 5:\n",
        "    leap_years.append(i)\n",
        "    i += 4\n",
        "print(leap_years)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2000, 2004, 2008, 2012, 2016]\n"
          ]
        }
      ],
      "execution_count": 52,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# A nurse is monitoring a patient's rising temperature. The temp is rising in increments of .5 degrees continually.\n",
        "# The nurse needs to be shown a message when the temp reaches 104 and the monitoring should end at that time.\n",
        "# expected outcome: The temp has reached 104.0\n",
        "temp = 99.5\n",
        "while temp <= 104.0:\n",
        "    # extra practice\n",
        "    #print('The temp has now reached {}'.format(temp))\n",
        "    if temp == 104.0:\n",
        "        print('The temp has reached 104.0')\n",
        "    temp += .5"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The temp has reached 104.0\n"
          ]
        }
      ],
      "execution_count": 56,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Lesson 4"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# create a tuple to store the WGU phone number 877-435-7948\n",
        "phone = tuple('877-435-7948')\n",
        "print(phone)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('8', '7', '7', '-', '4', '3', '5', '-', '7', '9', '4', '8')\n"
          ]
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# print the last four digits of the phone number\n",
        "# expected outcome: 7948\n",
        "print(str().join(phone[-4:]))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7948\n"
          ]
        }
      ],
      "execution_count": 12,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# print the entire phone number with the message to Call WGU now\n",
        "# expected outcome: Call WGU now at 877-435-7948\n",
        "print(str().join(phone))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "877-435-7948\n"
          ]
        }
      ],
      "execution_count": 13,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# use the math module to determine the factorial of the number 7 and print the result\n",
        "# expected outcome: 5040\n",
        "import math as math\n",
        "print(math.factorial(7))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5040\n"
          ]
        }
      ],
      "execution_count": 16,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# use the math module to determine the square root of the number 27 and print the result\n",
        "# expected outcome: 5.196152422706632\n",
        "import math as math\n",
        "print(math.sqrt(27))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5.196152422706632\n"
          ]
        }
      ],
      "execution_count": 17,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# use the math module to determine the largest integer less than or equal to 15.87 and print the result\n",
        "# expected outcome: 15\n",
        "import math as math\n",
        "print(math.floor(15.87))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15\n"
          ]
        }
      ],
      "execution_count": 20,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# use the math module to determine the smallest integer integer greater than or equal to 15.87 and print the result\n",
        "# expected outcome: 16\n",
        "import math as math\n",
        "print(math.ceil(15.87))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16\n"
          ]
        }
      ],
      "execution_count": 21,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# use the math module to determine e to the power of 4\n",
        "# expected outcome: 54.598150033144236\n",
        "import math as math\n",
        "print(math.exp(4))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "54.598150033144236\n"
          ]
        }
      ],
      "execution_count": 109,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# use the Python standard library to generate a random number between 2 and 20 and print the result\n",
        "import random as random\n",
        "print(random.randrange(2, 20))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5\n"
          ]
        }
      ],
      "execution_count": 44,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Library Code Problems"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Math"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Using the library documentation. Define the following methods and give a code example of its use.\n",
        "# Math\n",
        "import math as math\n",
        "n = 14.37\n",
        "answers = {\n",
        "    'floor' : {\n",
        "        'code' : 'math.floor(n)',\n",
        "        'result' : math.floor(n),\n",
        "        'explanation' : 'This method returns the floor of x, the largest integer less than or equal to x.'\n",
        "    },\n",
        "    'ceil' : {\n",
        "        'code' : 'math.ceil(n)',\n",
        "        'result' : math.ceil(n),\n",
        "        'explanation' : 'This method returns the ceiling of x, the smallest integer great than or equal to x.'\n",
        "    },\n",
        "    'factorial' : {\n",
        "        'code' : 'math.factorial(int(n))',\n",
        "        'result' : math.factorial(int(n)),\n",
        "        'explanation' : 'This method returns the factorial of x. Note x must be an integer and cannot be negative.'\n",
        "    },\n",
        "    'exponential' : {\n",
        "        'code' : 'math.exp(n)',\n",
        "        'result' : math.exp(n),\n",
        "        'explanation' : 'This method returns e raised to the power of x.'\n",
        "    },\n",
        "    'square_root' : {\n",
        "        'code' : 'math.sqrt(n)',\n",
        "        'result' : math.sqrt(n),\n",
        "        'explanation' : 'This method returns the square root of x.'\n",
        "    }\n",
        "}\n",
        "\n",
        "for i, answer in answers.items():\n",
        "    print(i.title() + ' : ' + answer['explanation'] + '\\nExample : ' + 'n = {}; '.format(n) + answer['code'] + ' == '  + str(answer['result']) + '\\n' )\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Floor : This method returns the floor of x, the largest integer less than or equal to x.\n",
            "Example : n = 14.37; math.floor(n) == 14\n",
            "\n",
            "Ceil : This method returns the ceiling of x, the smallest integer great than or equal to x.\n",
            "Example : n = 14.37; math.ceil(n) == 15\n",
            "\n",
            "Factorial : This method returns the factorial of x. Note x must be an integer and cannot be negative.\n",
            "Example : n = 14.37; math.factorial(int(n)) == 87178291200\n",
            "\n",
            "Exponential : This method returns e raised to the power of x.\n",
            "Example : n = 14.37; math.exp(n) == 1741051.8499277548\n",
            "\n",
            "Square_Root : This method returns the square root of x.\n",
            "Example : n = 14.37; math.sqrt(n) == 3.790778284204973\n",
            "\n"
          ]
        }
      ],
      "execution_count": 145,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### OS"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# link()\n",
        "from os import link\n",
        "# here is an example, it's disabled because if the directories do not exist or if you do not have the right permissions it will throw an exception.\n",
        "# my_link = link(src= './foo', dst='./bar')\n",
        "\nprint('The link() method creates a hard link pointing to the source named destination.')    "
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The link() method creates a hard link pointing to the source named destination.\n"
          ]
        }
      ],
      "execution_count": 150,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# remove()\n",
        "from os import remove\n",
        "#remove(path = './foo.txt')\n",
        "\nprint('The remove() method deletes the file passed to the path argument. Note if the path argument is a directory an exception will be thrown. It is suggested to use the rmdir() command.')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The remove() method deletes the file passed to the path argument. Note if the path argument is a directory an exception will be thrown. It is suggested to use the rmdir() command.\n"
          ]
        }
      ],
      "execution_count": 152,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# getcwd()\n",
        "from os import getcwd\n",
        "mycwd = getcwd()\n",
        "\n",
        "print(mycwd)\n",
        "print('The getcwd() method returns a string representing the current working directory.')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "D:\\wgu\\c859\\practice-sets\n",
            "The getcwd() method returns a string representing the current working directory.\n"
          ]
        }
      ],
      "execution_count": 156,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# chroot()\n",
        "# from os import chroot\n",
        "# Note: the chroot() command is only available on Unix systems. I am currently working on a Windows system therefore the command is not available to me during import.\n",
        "\nprint('This method changes the root directory of the current process to the path passed via the arguments')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This method changes the root directory of the current process to the path passed via the arguments\n"
          ]
        }
      ],
      "execution_count": 158,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Random"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# randrange()\n",
        "from random import randrange\n",
        "\n",
        "my_rand = randrange(3,8)\n",
        "\n",
        "print(my_rand)\n",
        "print('This method returns a randomly selected element between two ranges, accepts 3 arguments start, stop, [step]')\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4\n",
            "This method returns a randomly selected element between two ranges, accepts 3 arguments start, stop, [step]\n"
          ]
        }
      ],
      "execution_count": 173,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# randint()\n",
        "from random import randint\n",
        "my_rand_int = randint(4,9)\n",
        "\n",
        "print(my_rand_int)\n",
        "print('This method returns a random integer N which is denoted as a <= N <= b')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7\n",
            "This method returns a random integer N which is denoted as a <= N <= b\n"
          ]
        }
      ],
      "execution_count": 181,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# random()\n",
        "from random import random\n",
        "my_random = random()\n",
        "\n",
        "print(my_random)\n",
        "print('This method returns the next random floating point number in the rane of [0.0, 1.0)')    "
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6499325813676742\n",
            "This method returns the next random floating point number in the rane of [0.0, 1.0)\n"
          ]
        }
      ],
      "execution_count": 189,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# choice()\n",
        "from random import choice\n",
        "my_choices = ['one', 'two', 'three', 'four', 'five']\n",
        "print(choice(my_choices))\n",
        "print('This method returns a random element from the non-empty sequence passed via an argument.')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "three\n",
            "This method returns a random element from the non-empty sequence passed via an argument.\n"
          ]
        }
      ],
      "execution_count": 194,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## PIL\n",
        "\n",
        "`PIL` is the Python Imaging Library.<br/>\n",
        "It is used to open display and manipulate images in Python.<br/>\n",
        "In newer versions of Python this library is best known as **Pillow**.<br/>\n",
        "The methods mentioned below specifically deal with the **Image** class in the module."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# open()\n",
        "# Using the open method will open a new file, from then you can show, manipulate , save, and even delete the image.\n",
        "from PIL import Image\n",
        "file = './image.jpg'\n",
        "image = Image.open(file)\n",
        "\n",
        "# to show we've pened the file, let's print some details about the file.\n",
        "image"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 28,
          "data": {
            "text/plain": [
              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=299x168 at 0x25CAB700DD8>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 28,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# new()\n",
        "# creates a new image and accepts 3 arguments mode, size, and color. \n",
        "new_image = Image.new(mode = 'RGBA', size = (512,512), color = 'lightblue')\n",
        "new_image"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 38,
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGBA size=512x512 at 0x25CAB761DA0>"
            ],
            "image/png": [
              "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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 38,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# show()\n",
        "# In windows the show method provided after creating or importing a new image displays the image in Paint for Windows operating systems.\n",
        "image.show()"
      ],
      "outputs": [],
      "execution_count": 41,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# save()\n",
        "# The save method saves the image under the given filename\n",
        "my_image = image\n",
        "\n",
        "my_image.resize(size = (512, 512))\n",
        "\n",
        "my_image.save(fp='my_image.png')\n",
        "\n",
        "# In this example we resize the image to 512x512 then save the image with the file name my_image as a png file. \n",
        "# Remember the original image was a .jpeg image.\n",
        "my_image"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 56,
          "data": {
            "text/plain": [
              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=299x168 at 0x25CAB700DD8>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 56,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pandas"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Why do you use describe()? What is the result of the following code? \n",
        "# The purpose of describe is to generate descriptive statistics that summarize the central tendency, dispersion and shape of a dataset's distribution. \n",
        "# describe() in python is similar to the summary() method found in the R programming language.\n",
        "\n",
        "import pandas as pd\n",
        "s = pd.Series([1,2,3])\n",
        "s.describe()\n",
        "\n",
        "print(s.describe(), '\\n')\n",
        "print('As shown above the describe method for the series s shows the count, mean, standard deviation, min, 25% quartile, 50% quartile, and 75% quartile along with the max and the data type')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    3.0\n",
            "mean     2.0\n",
            "std      1.0\n",
            "min      1.0\n",
            "25%      1.5\n",
            "50%      2.0\n",
            "75%      2.5\n",
            "max      3.0\n",
            "dtype: float64 \n",
            "\n",
            "As shown above the describe method for the series s shows the count, mean, standard deviation, min, 25% quartile, 50% quartile, and 75% quartile along with the max and the data type\n"
          ]
        }
      ],
      "execution_count": 62,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# What is the result of the following code(hint: the code is not finished):\n",
        "import pandas as pd\n",
        "s = pd.Series(['a', 'a', 'b', 'c'])\n",
        "s.describe()\n",
        "\n",
        "print('The result of the following code is: ')\n",
        "print(s.describe())\n",
        "print('\\nThe results show that there is a count of 4 elements in the series')\n",
        "print('There are 3 unique elements in the series')\n",
        "print('The top most occuring element is \"a\"')\n",
        "print('The frequency is 2 meaning the most occuring element happens 2 times which is \"a\"')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The result of the following code is: \n",
            "count     4\n",
            "unique    3\n",
            "top       a\n",
            "freq      2\n",
            "dtype: object\n",
            "\n",
            "The results show that there is a count of 4 elements in the series\n",
            "There are 3 unique elements in the series\n",
            "The top most occuring element is \"a\"\n",
            "The frequency is 2 meaning the most occuring element happens 2 times which is \"a\"\n"
          ]
        }
      ],
      "execution_count": 72,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Numpy"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "- What is Numpy library used for in Python?\n",
        "    - According to the docs **Numpy** is the fundamental package for scientific computing in Python. Numpy at it's core is essentially the **ndarray object**\n",
        "- What is this line of code trying to accomplish?\n",
        "    ```py\n",
        "    import matplotlib.pyplot as plt\n",
        "    ```\n",
        "    - This line of code is importing the pyplot module from the matplotlib library and assigning it to the name space of **plt**\n",
        "    - It can then be accessed by calling `plt.methodName` where methodName would be a method or function accessible via the imported module."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Datetime"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Using datetime, how do I print the year?\n",
        "# First we must create an instance of the current point in time. \n",
        "# We then call the year property of that new time variable.\n",
        "import datetime\n",
        "dt = datetime.datetime.now()\n",
        "\nprint(dt.year)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2018\n"
          ]
        }
      ],
      "execution_count": 89,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Using datetime, how do i print the month? \n",
        "# Given the date time variable set above \"dt\" I will show the month\n",
        "print(dt.month)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7\n"
          ]
        }
      ],
      "execution_count": 91,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Using datetime,  how do I print the day? \n",
        "# Using the same date time variable above \"dt\" I will show the day\n",
        "print(dt.day)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15\n"
          ]
        }
      ],
      "execution_count": 92,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# timedelta.max\n",
        "# The most positive timedelta object\n",
        "# This example shows the max timedelta in 90 days.\n",
        "ninety_days = datetime.timedelta(days = 90)\n",
        "ninety_days.max"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 148,
          "data": {
            "text/plain": [
              "datetime.timedelta(999999999, 86399, 999999)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 148,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# timedelta.total_seconds()\n",
        "# Return the total number of seconds contained in the duration\n",
        "# This example shows the total seconds possible in 30 days.\n",
        "thirty_days = datetime.timedelta(days = 30)\n",
        "thirty_days.total_seconds()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 146,
          "data": {
            "text/plain": [
              "2592000.0"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 146,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {}
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.5",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "nteract": {
      "version": "0.9.1"
    },
    "gist_id": "372857b6f17e9305315bef3da49c462a"
  },
  "nbformat": 4,
  "nbformat_minor": 4
}