Mini_Projects_2_Solution.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Mini_Projects_2_Solution.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m3EhnurEOcyr"
      },
      "source": [
        "# Repetion Structures"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MXJEHLW4_c-z"
      },
      "source": [
        "**1. Calories Burned** <br>\n",
        "Running on a particular treadmill you burn 4.2 calories per minute. Write a program that\n",
        "uses a loop to display the number of calories burned after 10, 15, 20, 25, and 30 minutes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JkhyBliV5o_W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "610f7d88-11e8-4cb1-b6f8-421719cc35b0"
      },
      "source": [
        "for minute in range(30):\n",
        "  if minute > 5 and minute % 5 == 0:\n",
        "    calories = minute * 4.2\n",
        "    print(str(calories) + ' Calories burned,' + ' Running time: ' + str(minute) + ' Minutes' )"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "42.0 Calories burned,  Running time: 10 Minutes\n",
            "63.0 Calories burned,  Running time: 15 Minutes\n",
            "84.0 Calories burned,  Running time: 20 Minutes\n",
            "105.0 Calories burned,  Running time: 25 Minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mNpgbwxmC7-V"
      },
      "source": [
        "**2. Distance Traveled** <br>\n",
        "The distance a vehicle travels can be calculated as follows:<br>\n",
        "<center>\n",
        "$distance = speed * time$<br>\n",
        "For example, if a train travels 40 miles per hour for three hours, the distance traveled is\n",
        "120 miles. Write a program that asks the user for the speed of a vehicle (in miles per hour)\n",
        "and the number of hours it has traveled. It should then use a loop to display the distance\n",
        "the vehicle has traveled for each hour of that time period. Here is an example of the desired\n",
        "output:\n",
        "\n",
        "![10.PNG](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rEwAkAKZDvCu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "039b965e-f3a4-489a-9d5d-d8a23ddefc7e"
      },
      "source": [
        "# Get vehicle speed and time\n",
        "speed = int(input('What is the speed of the vehicle in mph? '))\n",
        "time =  int(input('How many hours has it traveled? '))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "What is the speed of the vehicle in mph? 40\n",
            "How many hours has it traveled? 5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KhCKlF64O0a9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc939e6a-7135-4cc9-d77c-ec8c9a367a55"
      },
      "source": [
        "# Print label for output table \n",
        "print('\\nHour\\t\\tDistance Traveled')\n",
        "print('_________________________________')\n",
        "\n",
        "# output Distance for each hour\n",
        "for hour in range(time):\n",
        "  hour +=1\n",
        "  distance = speed * hour\n",
        "  print(str(hour) + \"\\t\\t\" + str(distance))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Hour\t\tDistance Traveled\n",
            "_________________________________\n",
            "1\t\t40\n",
            "2\t\t80\n",
            "3\t\t120\n",
            "4\t\t160\n",
            "5\t\t200\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gD_C29KRDxIb"
      },
      "source": [
        "**3. Celsius to Fahrenheit Table** <br>\n",
        "Write a program that displays a table of the Celsius temperatures 0 through 20 and\n",
        "their Fahrenheit equivalents. The formula for converting a temperature from Celsius to\n",
        "Fahrenheit is <br>\n",
        "\n",
        "<center>$F=\\frac{9}{5} C + 32$ <br>\n",
        "\n",
        "\n",
        "\n",
        "where F is the Fahrenheit temperature and C is the Celsius temperature. Your program must\n",
        "use a loop to display the table.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D_Ri_C_1FSpK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9503fa2a-6670-41c1-8d9b-0f403c0c5583"
      },
      "source": [
        "# Print label for output table \n",
        "print('Celsius    =    Fahrenheit')\n",
        "\n",
        "# Print converting table\n",
        "for cel in range(21):\n",
        "  fah = (9/5) * cel + 32\n",
        "  print(str(cel) + '  ْC' + '\\t=\\t' + str(fah) + '  ْF')"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Celsius    =    Fahrenheit\n",
            "0  ْC\t=\t32.0  ْF\n",
            "1  ْC\t=\t33.8  ْF\n",
            "2  ْC\t=\t35.6  ْF\n",
            "3  ْC\t=\t37.4  ْF\n",
            "4  ْC\t=\t39.2  ْF\n",
            "5  ْC\t=\t41.0  ْF\n",
            "6  ْC\t=\t42.8  ْF\n",
            "7  ْC\t=\t44.6  ْF\n",
            "8  ْC\t=\t46.4  ْF\n",
            "9  ْC\t=\t48.2  ْF\n",
            "10  ْC\t=\t50.0  ْF\n",
            "11  ْC\t=\t51.8  ْF\n",
            "12  ْC\t=\t53.6  ْF\n",
            "13  ْC\t=\t55.400000000000006  ْF\n",
            "14  ْC\t=\t57.2  ْF\n",
            "15  ْC\t=\t59.0  ْF\n",
            "16  ْC\t=\t60.8  ْF\n",
            "17  ْC\t=\t62.6  ْF\n",
            "18  ْC\t=\t64.4  ْF\n",
            "19  ْC\t=\t66.2  ْF\n",
            "20  ْC\t=\t68.0  ْF\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "akh-6CAUFUVj"
      },
      "source": [
        "**4. Calculating the Factorial of a Number** <br>\n",
        "In mathematics, the notation n! represents the factorial of the nonnegative integer n . The\n",
        "factorial of n is the product of all the nonnegative integers from 1 to n. For example,<br>\n",
        "<center>$ 7!=1 *2*3*4 *5* 6*7 = 5040 $ </center><br>\n",
        "and <br>\n",
        "<center>$ 4!=1 *2*3*4  = 24 $ </center><br>\n",
        "Write a program that lets the user enter a nonnegative integer and then uses a loop to calculate the factorial of that number. Display the factorial."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AUTAqQKlFU_Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "29fa9dc7-828a-43f0-b6ba-9b781d2c5166"
      },
      "source": [
        "# Get user input\n",
        "while True:\n",
        "  num = int(input('Enter a nonnegative integer: '))\n",
        "  if num >= 0:\n",
        "    break"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter a nonnegative integer: 7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7BLQxptaOy4H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dbdf8c8c-1a4e-493b-cadc-ccb018214f21"
      },
      "source": [
        "# Calculate the factorial \n",
        "fac = 1\n",
        "for n in range(num):\n",
        "  fac *= (n+1)\n",
        "\n",
        "# Print the result\n",
        "print(str(num) + '! = ' + str(fac))"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7! = 5040\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0_Y1_X5pGSwI"
      },
      "source": [
        "**5. Write a program that uses nested loops to draw this pattern:** <br>\n",
        "\n",
        "<center><img src=\"data:image/png;base64,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\" width=\"150\">\n",
        "<img src=\"data:image/png;base64,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\" width=\"188\">\n",
        "</center>\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xbF5fwWXOxwR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8cf900a4-084c-4fa5-89ad-cad1c2e9dcde"
      },
      "source": [
        "# Left pattern\n",
        "for i in range(7):\n",
        "  for j in range(7 - i):\n",
        "    print('*', end=\" \")\n",
        "  print('\\n')"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "* * * * * * * \n",
            "\n",
            "* * * * * * \n",
            "\n",
            "* * * * * \n",
            "\n",
            "* * * * \n",
            "\n",
            "* * * \n",
            "\n",
            "* * \n",
            "\n",
            "* \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qKGcnKadOxzI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "69e74e42-74d6-4760-de3d-e061f08544cb"
      },
      "source": [
        "# Right Pattern\n",
        "for i in range(6):\n",
        "  print('#', end=\" \")\n",
        "  for j in range(i):\n",
        "    print(' ', end=\" \")\n",
        "  print('#')"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "# #\n",
            "#   #\n",
            "#     #\n",
            "#       #\n",
            "#         #\n",
            "#           #\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rAJBTLOzOpLS"
      },
      "source": [
        "# Functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e1dAWy70RVRE"
      },
      "source": [
        "**6. Math Quiz** <br>\n",
        "Write a program that gives simple math quizzes. The program should display two random numbers that are to be added, such as: 247 + 129\n",
        "\n",
        "The program should allow the student to enter the answer. If the answer is correct, a message of congratulations should be displayed. If the answer is incorrect, a message showing the correct answer should be displayed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lidYoHbhRUZ5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8dff19e-b572-40a8-ccc9-b257ac33af42"
      },
      "source": [
        "# Useful library\n",
        "import random\n",
        "\n",
        "# Change to modify generated number range\n",
        "min = 0\n",
        "max = 1000\n",
        "\n",
        "# Generate random numbers\n",
        "n1 = random.randint(min, max) \n",
        "n2 = random.randint(min, max)\n",
        "result = n1 + n2\n",
        "\n",
        "# Print the quizze\n",
        "print(str(n1) + ' + ' + str(n2) + ' = ?')"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "150 + 797 = ?\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TvSUqzaURUz3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17236d43-aaac-4cf5-83c0-617e76c44731"
      },
      "source": [
        "# Get user answer\n",
        "answer = int(input('Enter your answer: '))\n",
        "\n",
        "# Check \n",
        "if result == answer:\n",
        "  print('Congratulations ;)')\n",
        "else:\n",
        "  print('Sorry, wrong answer it is ' + str(result))"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter your answer: 947\n",
            "Congratulations ;)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L7ISMnk_O6rp"
      },
      "source": [
        "**7. Kinetic Energy** <br>\n",
        "In physics, an object that is in motion is said to have kinetic energy. The following formula can be used to determine a moving object’s kinetic energy:<br>\n",
        "\n",
        "\n",
        "<center>$KE=\\frac{1}{2}m v^2 $ <br> </center>\n",
        " \n",
        "\n",
        "The variables in the formula are as follows: KE is the kinetic energy, m is the object’s mass in kilograms, and v is the object’s velocity in meters per second.\n",
        "Write a function named *kinetic_energy* that accepts an object’s mass (in kilograms) and velocity (in meters per second) as arguments. The function should return the amount of kinetic energy that the object has.\n",
        "Write a program that asks the user to enter values for mass and velocity, and then calls the kinetic_energy function to get the object’s kinetic energy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YUuP_mvpIhBx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94134a32-eba0-4da5-df2b-0af4d1bc214e"
      },
      "source": [
        "# function to calculate KE\n",
        "def kinetic_energy(mass, velocity):\n",
        "  return 0.5 * mass * velocity**2\n",
        "\n",
        "# Get user input\n",
        "m = int(input('Enter object\\'s mass: '))\n",
        "v = int(input('Enter object\\'s velocity: '))\n",
        "\n",
        "\n",
        "KE = kinetic_energy(m, v)\n",
        "\n",
        "print('KE = ' + str(KE) + ' J')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter object's mass: 5\n",
            "Enter object's velocity: 1\n",
            "KE = 2.5 J\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c2sSEq3uSurn"
      },
      "source": [
        "# Files and Exceptions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qw2XyRs-TWIF"
      },
      "source": [
        "**8. Sum of Numbers** <br>\n",
        "Assume that a file containing a series of integers is named numbers.txt and exists on the computer’s disk. Write a program that reads all of the numbers stored in the file and calculates their total.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "92EStR9KSu8N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6487f5d9-78a7-4167-8346-03a45adfb6d6"
      },
      "source": [
        "# Open the file to read\n",
        "num = open('numbers.txt', 'r')\n",
        "\n",
        "# initialize sum variable\n",
        "sum = 0\n",
        "\n",
        "# Read file content\n",
        "for n in num:\n",
        "  sum += int(n)\n",
        "\n",
        "# Close the file after process\n",
        "num.close()\n",
        "\n",
        "print(sum)\n"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "55\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gW0mWcdDVgK4"
      },
      "source": [
        "**9. Average of Numbers** <br>\n",
        "Assume that a file containing a series of integers is named numbers.txt and exists on the computer’s disk. Write a program that calculates the average of all the numbers stored in\n",
        "the file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-DZx-vnSvCe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2724d7c8-5a80-4775-d9a4-a3e6ccfb74a2"
      },
      "source": [
        "# Open the file to read\n",
        "num = open('numbers.txt', 'r')\n",
        "\n",
        "# initialize sum, counter variables\n",
        "sum = 0\n",
        "cnt = 0\n",
        "\n",
        "# Read file content\n",
        "for n in num:\n",
        "  sum += int(n)\n",
        "  cnt += 1\n",
        "\n",
        "# Close the file after process\n",
        "num.close()\n",
        "\n",
        "# Calculate Average\n",
        "average = sum / cnt\n",
        "\n",
        "print(average)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vjchCYETVwPx"
      },
      "source": [
        "**10. Random Number File Writer**<br>\n",
        "Write a program that writes a series of random numbers to a file. Each random number should be in the range of 1 \n",
        "through 500. The application should let the user specify how\n",
        "many random numbers the file will hold.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DCyba_BZVxLF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0577c7be-903d-4e32-aaba-62814e23c94c"
      },
      "source": [
        "# Useful library\n",
        "import random\n",
        "\n",
        "# Get how many numbers will be written\n",
        "cnt = int(input('Enter how many random numbers you want the file to hold: '))\n",
        "\n",
        "# Open file to write\n",
        "num = open('random_numbers.txt', 'w')\n",
        "\n",
        "# Write random number\n",
        "for i in range(cnt):\n",
        "  num.write(str(random.randint(1, 500)) + '\\n')\n",
        "\n",
        "# print Success masseage\n",
        "print('*****Process Succeed*****')\n",
        "# Close the file\n",
        "num.close()"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter how many random numbers you want the file to hold: 1\n",
            "*****Process Successed*****\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p75ZKqbXWMdY"
      },
      "source": [
        "**11.  Exception Handing** <br>\n",
        "Modify the program that you wrote for Exercise 9 so it handles the following exceptions: <br>\n",
        "* It should handle any IOError exceptions that are raised when the file is opened and data\n",
        "is read from it. <br>\n",
        "* It should handle any ValueError exceptions that are raised when the items that are read from the file are converted to a number."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "22SOHQbrVxRE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f3c1dbc-fd8a-4413-e2de-2053741cee09"
      },
      "source": [
        "try:\n",
        "  # Open the file to read\n",
        "  num = open('numbers.txt', 'r')\n",
        "\n",
        "  # initialize sum, counter variables\n",
        "  sum = 0\n",
        "  cnt = 0\n",
        "\n",
        "  # Read file content\n",
        "  for n in num:\n",
        "    try:\n",
        "      sum += int(n)\n",
        "      cnt += 1\n",
        "    except ValueError:\n",
        "      print('There is a string in the file it will be ignored.')\n",
        "  \n",
        "  # Close the file after process\n",
        "  num.close()\n",
        "\n",
        "  # Calculate Average\n",
        "  average = sum / cnt\n",
        "\n",
        "  print(average)\n",
        "  \n",
        "except IOError:\n",
        "  print('There is a problem with the file it may be lost or corrupted, please try again...')\n",
        "\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3p0MENR_WrBV"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}