Week_3.ipynb

{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "# Week 3 handin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 01 Assignment\n",
    "\n",
    "## Ex 1 Classes\n",
    "1. Create 3 classes: Student, DataSheet and Course\n",
    "2. A student has a data_sheet and a data_sheet has multiple courses in particular order\n",
    "3. Each course has name, classroom, teacher, ETCS and optional grade if course is taken.\n",
    "4. In Student create __init__() so that a Student can be initiated with name, gender, data_sheet and image_url\n",
    "5. In DataSheet create a method to get_grades_as_list()\n",
    "6. In student create a method: get_avg_grade()\n",
    "7. Create a function that can generate n number of students with random: name, gender, courses (from a fixed list of course names), grades, img_url\n",
    "   1. Let the function write the result to a csv file with format stud_name, course_name, teacher, ects, classroom, grade, img_url\n",
    "7. Read student data into a list of Students from a csv file: \n",
    "   1. loop through the list and print each student with name, img_url and avg_grade.\n",
    "   2. sort the list by avg_grade\n",
    "   3. create a bar chart with student_name on x and avg_grade on y-axis\n",
    "8. Make a method on Student class that can show progression of the study in % (add up ECTS from all passed courses divided by total of 150 total points (equivalent to 5 semesters))\n",
    "9. Show a bar chart of distribution of study progression on x-axis and number of students in each category on y-axis. (e.g. make 10 categories from 0-100%)\n",
    "\n",
    "Extra: Make the Datasheet class iterable so that next(data_sheet) will return the next course in the list\n",
    "\n",
    "## Ex 2 Exceptions (extra)\n",
    "1. Create a function that can take a list of students and return the 3 students closest to completing their study.\n",
    "2. If list is shorter than 3 raise your own custom exception (NotEnoughStudentsException)\n",
    "3. Create another function that can create a csv file with 3 students closest to completion\n",
    "   1. If an exception is raised write an appropriate message to the file\n",
    "\n",
    "## Ex 3 Plotting (extra)\n",
    "1. Create a function that can take a list of students and show a pie chart of how students are distributed in ECTS percentage categories (10%, 20%, ...)\n",
    "2. create a function that can take a list of students and show how many students have taken each course (bar chart)\n",
    "   1. create a method on student that can return a list of courses\n",
    "3. make the figure show males and females in different colors for each course (display 2 datasets in same figure)\n"
   ]
  },
  {
   "source": [
    "## 02 Status\n",
    "Completed all EX1 tasks"
   ],
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   "source": [
    "## Exercise 1"
   ]
  },
  {
   "source": [
    "### Excersie 1-6\n",
    "Create 3 classes: Student, DataSheet and Course\n",
    "A student has a data_sheet and a data_sheet has multiple courses in particular order\n",
    "Each course has name, classroom, teacher, ETCS and optional grade if course is taken.\n",
    "In Student create init() so that a Student can be initiated with name, gender, data_sheet and image_url\n",
    "In DataSheet create a method to get_grades_as_list()\n",
    "In student create a method: get_avg_grade()"
   ],
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    "class Student():\n",
    "    \"\"\"A student representation, students have name, a student ID , gender, a datesheet and a image_url\"\"\"\n",
    "\n",
    "    def __init__(self, name, studentid, gender, datasheet, image_url=None):\n",
    "        \"\"\"Initialize name, student ID, gender, datasheet and image_url.\"\"\"\n",
    "        self.name = name \n",
    "        self.studentid = studentid\n",
    "        self.gender = gender\n",
    "        self.datasheet = datasheet\n",
    "        self.image_url = image_url        \n",
    "    \n",
    "    def __repr__(self):\n",
    "        return 'Student(%r, %r, %r, %r, %r)' % (self.name, self.gender, self.studentid, \n",
    "                                     self.datasheet, self.image_url)\n",
    "    \n",
    "    def __str__(self):\n",
    "        return 'Student with name {name} identifies as {gender} witd ID: {id}, studentpicture: {pic} his/her/their datasheet looks like this: {datasheet}.'.format(\n",
    "            name=self.name, gender=self.gender, id=self.studentid, pic=self.image_url, datasheet=self.datasheet)\n",
    "    \n",
    "    def get_avg_grade(self):\n",
    "        grades = self.datasheet.get_grades_as_list()\n",
    "        return sum(grades)/len(grades)\n",
    "    \n",
    "    def get_education_progress(self):\n",
    "        return (self.datasheet.get_completed_etcs()/150)*100\n",
    "\n",
    "class Datasheet():\n",
    "\n",
    "    def __init__(self, courses=[]):\n",
    "        \"\"\"A datasheet consists of multiple courses.\"\"\"\n",
    "        self.courses = courses\n",
    "        \n",
    "\n",
    "    def __repr__(self):\n",
    "        return 'Datasheet(%r' % (self.courses)\n",
    "\n",
    "    def get_grades_as_list(self):\n",
    "        returnlist = []\n",
    "        for course in self.courses:\n",
    "            returnlist.append(int(course.getGrade()))\n",
    "        return returnlist\n",
    "    \n",
    "    def get_completed_etcs(self):\n",
    "        sum = 0\n",
    "        for x in self.courses:\n",
    "            sum += x.etcs\n",
    "        return sum\n",
    "                \n",
    "class Course():\n",
    "    \"\"\"A course consist of name, classroom, teacher, ETCS and optional grade\"\"\"\n",
    "\n",
    "    def __init__(self, name, classroom, teacher, etcs, grade):\n",
    "        \"\"\"Initialize the course with name, classroom, teacher, ETCS and optional grade\"\"\"\n",
    "        self.name = name\n",
    "        self.classroom = classroom\n",
    "        self.teacher = teacher\n",
    "        self.etcs = etcs\n",
    "        self._grade = grade\n",
    "\n",
    "        \n",
    "    def __repr__(self):\n",
    "        return 'Course(%r, %r, %r, %r, %r)' % (self.name, self.classroom, self.teacher, self.etcs, self._grade)\n",
    "    \n",
    "    def __str__(self):\n",
    "        return 'Course name is {name} taking place in {classroom}, with the {teacher} as the teacher, it gives {etcs} etcs points and the student has the current grade of {grade}.'.format(\n",
    "            name=self.name, classroom=self.classroom, teacher=self.teacher, etcs=self.etcs, grade=self._grade)\n",
    "\n",
    "    def getGrade(self):\n",
    "        return self._grade\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Student with name Allan Simonsen identifies as Male witd ID: cph-as484, studentpicture: handsomepicture.jpg his/her/their datasheet looks like this: Datasheet([Course('Python', '1.05', 'Thomas', 10, '12')].\n"
     ]
    }
   ],
   "source": [
    "testStudent = Student(\"Allan Simonsen\", \"cph-as484\", \"Male\", Datasheet([Course(\"Python\", \"1.05\", \"Thomas\", 10, \"12\")]), \"handsomepicture.jpg\")\n",
    "print(testStudent)"
   ]
  },
  {
   "source": [
    "### Excersise 7\n",
    "7. Create a function that can generate n number of students with random: name, gender, courses (from a fixed list of course names), grades, img_url\n",
    "   1. Let the function write the result to a csv file with format stud_name, course_name, teacher, ects, classroom, grade, img_url"
   ],
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   "source": [
    "import random\n",
    "import csv\n",
    "\n",
    "fnames = [\"Allan\", \"Jacob\", \"Tobias\", \"Pia\", \"Axel\", \"Hans\", \"Thomas\", \"Lars\", \"Ulrik\", \"Caroline\", \"Louise\", \"Simonse\", \"Sylvia\", \"Kristian\", \"Andreas\", \"Anders\", \"Nina\", \"sofie\"]\n",
    "lnames = [\"Simonsen\", \"Holm\", \"Jensen\", \"Hansen\", \"Olsen\", \"Jakobsen\", \"Bjørnsen\", \"Iversen\", \"Elleman\", \"Klenum\", \"Lyft\"]\n",
    "genderlist = [\"Male\", \"Female\", \"Other\"]\n",
    "grades = [\"-3\", \"00\", \"02\", \"4\", \"7\", \"10\", \"12\"]\n",
    "coursenames = [\"Python\", \"Javascript\", \"Security\", \"IoT\", \"Advanced Programming\", \"Funktionel Programming\", \"Programming First Year\", \"Programming Second Year\"]\n",
    "\n",
    "def generateStudents(n):\n",
    "    \"\"\"a function that generates N amount of student with 3 courses each and returns a list\"\"\"\n",
    "    studentlist=[]\n",
    "    for i in range(n):\n",
    "        studentname = random.choice(fnames) + \" \" + random.choice(lnames)\n",
    "        studentid = \"cph-\" + str(random.randrange(111,999))\n",
    "        gender = random.choice(genderlist)\n",
    "        courses = []\n",
    "        for coursename in random.sample(coursenames, random.randrange(3,6)):\n",
    "            courses.append(Course(coursename, \"1.05\", random.choice(fnames), 10, random.choice(grades)))\n",
    "        image_url = \"picture\" + str(random.randrange(11111,99999)) + \".jpg\"\n",
    "\n",
    "        studentlist.append(Student(studentname, studentid, gender, Datasheet(courses),image_url))\n",
    "    return studentlist\n",
    "\n",
    "\n",
    "def write_list_to_csv(output_file, students):\n",
    "    with open(output_file, \"w\") as file_object:\n",
    "        csv_writer = csv.writer(file_object)\n",
    "        csv_writer.writerow([\"Student_name\", \"Studentid\" \"Gender\", \"Course_name\",\n",
    "                                \"Teacher\", \"ECTS\", \"Classroom\", \"Grade\", \"Img_url\"])\n",
    "        for x in students:\n",
    "            for course in x.datasheet.courses:\n",
    "                csv_writer.writerow([x.name, x.studentid, x.gender, course.name, course.teacher, course.etcs, course.classroom, course._grade, x.image_url])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Student with name sofie Jakobsen identifies as Other witd ID: cph-468, studentpicture: picture42068.jpg his/her/their datasheet looks like this: Datasheet([Course('Programming Second Year', '1.05', 'Pia', 10, '4'), Course('Security', '1.05', 'Lars', 10, '7'), Course('IoT', '1.05', 'Lars', 10, '00')].\nStudent with name Kristian Hansen identifies as Female witd ID: cph-807, studentpicture: picture87763.jpg his/her/their datasheet looks like this: Datasheet([Course('Programming Second Year', '1.05', 'Hans', 10, '00'), Course('Python', '1.05', 'Sylvia', 10, '7'), Course('IoT', '1.05', 'sofie', 10, '7'), Course('Programming First Year', '1.05', 'Allan', 10, '02'), Course('Advanced Programming', '1.05', 'Kristian', 10, '-3')].\nStudent with name Simonse Jakobsen identifies as Other witd ID: cph-728, studentpicture: picture16788.jpg his/her/their datasheet looks like this: Datasheet([Course('Programming Second Year', '1.05', 'Andreas', 10, '7'), Course('Advanced Programming', '1.05', 'Caroline', 10, '-3'), Course('Programming First Year', '1.05', 'Axel', 10, '10')].\nStudent with name Andreas Bjørnsen identifies as Other witd ID: cph-467, studentpicture: picture95573.jpg his/her/their datasheet looks like this: Datasheet([Course('Advanced Programming', '1.05', 'Hans', 10, '7'), Course('Security', '1.05', 'Tobias', 10, '10'), Course('IoT', '1.05', 'sofie', 10, '-3'), Course('Python', '1.05', 'Simonse', 10, '10')].\nStudent with name Andreas Hansen identifies as Male witd ID: cph-665, studentpicture: picture95389.jpg his/her/their datasheet looks like this: Datasheet([Course('Advanced Programming', '1.05', 'Andreas', 10, '02'), Course('IoT', '1.05', 'Louise', 10, '7'), Course('Funktionel Programming', '1.05', 'Tobias', 10, '10'), Course('Javascript', '1.05', 'Caroline', 10, '4'), Course('Python', '1.05', 'Sylvia', 10, '00')].\n"
     ]
    }
   ],
   "source": [
    "studentslisttest = generateStudents(5)\n",
    "output_file = \"week3modules/students.csv\"\n",
    "\n",
    "print(*studentslisttest, sep = \"\\n\")\n",
    "write_list_to_csv(output_file, studentslisttest)"
   ]
  },
  {
   "source": [
    "### Exercise 8\n",
    "8. Read student data into a list of Students from a csv file: \n",
    "   1. loop through the list and print each student with name, img_url and avg_grade.\n",
    "   2. sort the list by avg_grade\n",
    "   3. create a bar chart with student_name on x and avg_grade on y-axis"
   ],
   "cell_type": "markdown",
   "metadata": {}
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  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "def read_from_csvfile(inputfile):\n",
    "    student_list = []\n",
    "    with open(inputfile, \"r\") as file_object:\n",
    "        csvreader = csv.reader(file_object)\n",
    "        print(next(csvreader))\n",
    "        formerstudent = Student(\"\", \"\", \"\",[] , \"\")\n",
    "        for line in csvreader:\n",
    "            name, studentid, gender, course, teacher, etcs, classroom, grade, image_url = line\n",
    "            newstudent = Student(name, studentid, gender, Datasheet([Course(course, classroom, teacher, etcs, grade)]), image_url)\n",
    "            if newstudent.name == formerstudent.name:\n",
    "                formerstudent.datasheet.courses.append(Course(course, classroom, teacher, etcs, grade))\n",
    "            else:\n",
    "                student_list.append(newstudent)\n",
    "                formerstudent = newstudent\n",
    "    return student_list\n",
    "\n",
    "def taskA(inputlist):\n",
    "    printlist = []\n",
    "    for x in inputlist:\n",
    "        printlist.append([x.name, x.image_url, x.get_avg_grade()])\n",
    "    return printlist\n",
    "\n",
    "def taskB(inputlist):\n",
    "    printlist = []\n",
    "    for x in inputlist:\n",
    "        printlist.append([x.name, x.image_url, x.get_avg_grade()])\n",
    "    printlist.sort(key=lambda x: x[2], reverse=True)\n",
    "    return printlist\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Testing CSV reader\n['Student_name', 'StudentidGender', 'Course_name', 'Teacher', 'ECTS', 'Classroom', 'Grade', 'Img_url']\nStudent with name Sylvia Klenum identifies as Female witd ID: cph-258, studentpicture: picture97812.jpg his/her/their datasheet looks like this: Datasheet([Course('Javascript', '1.05', 'Lars', '10', '4'), Course('Advanced Programming', '1.05', 'Caroline', '10', '4'), Course('Programming Second Year', '1.05', 'Louise', '10', '12'), Course('Python', '1.05', 'Caroline', '10', '02')].\nStudent with name Simonse Bjørnsen identifies as Male witd ID: cph-197, studentpicture: picture26837.jpg his/her/their datasheet looks like this: Datasheet([Course('IoT', '1.05', 'Lars', '10', '7'), Course('Advanced Programming', '1.05', 'sofie', '10', '12'), Course('Programming Second Year', '1.05', 'Allan', '10', '00'), Course('Funktionel Programming', '1.05', 'Lars', '10', '12'), Course('Javascript', '1.05', 'Simonse', '10', '12')].\nStudent with name Allan Simonsen identifies as Female witd ID: cph-191, studentpicture: picture23849.jpg his/her/their datasheet looks like this: Datasheet([Course('Programming First Year', '1.05', 'Hans', '10', '12'), Course('Security', '1.05', 'Kristian', '10', '7'), Course('Javascript', '1.05', 'Andreas', '10', '4'), Course('Funktionel Programming', '1.05', 'Kristian', '10', '7'), Course('IoT', '1.05', 'Hans', '10', '12')].\nStudent with name Ulrik Holm identifies as Female witd ID: cph-142, studentpicture: picture13800.jpg his/her/their datasheet looks like this: Datasheet([Course('Javascript', '1.05', 'Louise', '10', '02'), Course('Advanced Programming', '1.05', 'Hans', '10', '12'), Course('Security', '1.05', 'Axel', '10', '4')].\nStudent with name Pia Elleman identifies as Other witd ID: cph-576, studentpicture: picture75310.jpg his/her/their datasheet looks like this: Datasheet([Course('Programming First Year', '1.05', 'Thomas', '10', '12'), Course('Advanced Programming', '1.05', 'Nina', '10', '12'), Course('Javascript', '1.05', 'Allan', '10', '7'), Course('Security', '1.05', 'sofie', '10', '12')].\nTask A\n['Sylvia Klenum', 'picture97812.jpg', 5.5]\n['Simonse Bjørnsen', 'picture26837.jpg', 8.6]\n['Allan Simonsen', 'picture23849.jpg', 8.4]\n['Ulrik Holm', 'picture13800.jpg', 6.0]\n['Pia Elleman', 'picture75310.jpg', 10.75]\nTask B\n['Pia Elleman', 'picture75310.jpg', 10.75]\n['Simonse Bjørnsen', 'picture26837.jpg', 8.6]\n['Allan Simonsen', 'picture23849.jpg', 8.4]\n['Ulrik Holm', 'picture13800.jpg', 6.0]\n['Sylvia Klenum', 'picture97812.jpg', 5.5]\nTask C\n"
     ]
    },
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(\"Testing CSV reader\")\n",
    "input_file = \"week3modules/students.csv\"\n",
    "input_from_csvfile = read_from_csvfile(input_file)\n",
    "print(*input_from_csvfile, sep = \"\\n\")\n",
    "\n",
    "print(\"Task A\")\n",
    "print(*taskA(input_from_csvfile), sep = \"\\n\")\n",
    "\n",
    "print(\"Task B\")\n",
    "print(*taskB(input_from_csvfile), sep = \"\\n\")\n",
    "\n",
    "print(\"Task C\")\n",
    "\n",
    "name = []\n",
    "grade = []\n",
    "for x in taskA(input_from_csvfile):\n",
    "    name.append(x[0])\n",
    "    grade.append(x[2])\n",
    "plt.figure()\n",
    "plt.bar(name, grade)\n",
    "plt.title(\"Students grades\")\n",
    "plt.xlabel(\"student\")\n",
    "plt.ylabel(\"Avg grade\")\n",
    "plt.show(block=True)\n"
   ]
  },
  {
   "source": [
    "### Exercise 9\n",
    "9. Make a method on Student class that can show progression of the study in % (add up ECTS from all passed courses divided by total of 150 total points (equivalent to 5 semesters))\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Name: Tobias Klenum Has completed: 26.666666666666668%\nName: Thomas Jensen Has completed: 20.0%\nName: Hans Simonsen Has completed: 26.666666666666668%\nName: Caroline Jensen Has completed: 33.33333333333333%\nName: Andreas Klenum Has completed: 20.0%\n"
     ]
    }
   ],
   "source": [
    "studentslistex9 = generateStudents(5)\n",
    "\n",
    "for x in studentslistex9:\n",
    "    print (\"Name: \" + x.name + \" Has completed: \" + str(x.get_education_progress()) + \"%\")"
   ]
  },
  {
   "source": [
    "### Exercise 10\n",
    "10. Show a bar chart of distribution of study progression on x-axis and number of students in each category on y-axis. (e.g. make 10 categories from 0-100%)\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "studentslistex10 = generateStudents(10)\n",
    "names = []\n",
    "etcsprog = []\n",
    "for x in studentslistex10:\n",
    "    names.append(x.name)\n",
    "    etcsprog.append(x.get_education_progress())\n",
    "plt.figure()\n",
    "plt.bar(names, etcsprog)\n",
    "plt.title(\"Students Etcs Progression\")\n",
    "plt.xlabel(\"student\")\n",
    "plt.ylabel(\"ETCS progress in %\")\n",
    "plt.show(block=True)\n"
   ]
  },
  {
   "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": "3.7.6-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}