Time Table Reader with Clash Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e2eab404",
   "metadata": {},
   "source": [
    "### Important Note"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6df96f9b",
   "metadata": {},
   "source": [
    "You have to enter the subject name in the exact same format as it appears in the TimeTable. \n",
    "For Example, if you are studying computer networks, Enter : 'Comp Networks'. Make sure to keep the latest version of the Timetable in the project Folder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17b8238d",
   "metadata": {},
   "source": [
    "## Library Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9d17b80a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13436a69",
   "metadata": {},
   "source": [
    "## Remove Unnecessary Top Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bc69fe36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this function will be called in a loop and unnecessary rows of all days dataframes will be dropped\n",
    "def drop_top_rows(df):\n",
    "    df.drop([0, 1, 2], axis=0, inplace=True)\n",
    "    df.columns = df.iloc[0] # make time schedule the column name/header\n",
    "    try:\n",
    "        df = df[['Room', '08:30-09:50','10:00-11:20','11:30-12:50','01:00-02:20','02:30-03:50','03:55-05:15','05:20-06:40 ', '06:45-08:05']]\n",
    "    except: # friday exceptional case\n",
    "        df = df[['Room', '08:30-09:50','10:00-11:20','11:30-12:50','01:00-02:20','02:00-03:20','03:30-04:50','05:20-06:40 ','06:45-08:05']]\n",
    "    df.drop([3], axis=0, inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7eaf7304",
   "metadata": {},
   "outputs": [],
   "source": [
    "def separate_labs_and_classes(day, classes, labs):\n",
    "    # finding the splitting point of labs and classes\n",
    "    lab_df = day[day.eq('Lab').any(1)]\n",
    "    ind = list(lab_df.index)\n",
    "    ind = ind[0]\n",
    "    \n",
    "    classes.append(day[:ind-4])\n",
    "    \n",
    "    lab_df = day[ind-4:]\n",
    "    lab_df.columns = lab_df.iloc[0]\n",
    "    try:\n",
    "        lab_df = lab_df[['Lab','08:30-11:15','11:25-02:10','02:25-05:10','05:20 - 08:05 (inc. 10 min. break)  ']]\n",
    "    except: \n",
    "        # friday exceptional case\n",
    "        lab_df = lab_df[['Lab', '08:30-11:15', '02:15-05:00', '05:20 - 08:05 (inc. 10 min. break)  ']]\n",
    "    lab_df.drop([ind], axis=0, inplace=True)\n",
    "    labs.append(lab_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "358a37a8",
   "metadata": {},
   "source": [
    "## Reading TimeTable Excel File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "100b2883",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\2656327064.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df.drop([3], axis=0, inplace=True)\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\2135482496.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  lab_df = day[day.eq('Lab').any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\2656327064.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df.drop([3], axis=0, inplace=True)\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\2135482496.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  lab_df = day[day.eq('Lab').any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\2656327064.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df.drop([3], axis=0, inplace=True)\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\2135482496.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  lab_df = day[day.eq('Lab').any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\2656327064.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df.drop([3], axis=0, inplace=True)\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\2135482496.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  lab_df = day[day.eq('Lab').any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\2656327064.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df.drop([3], axis=0, inplace=True)\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\2135482496.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  lab_df = day[day.eq('Lab').any(1)]\n"
     ]
    }
   ],
   "source": [
    "TimeTable = pd.ExcelFile(r\"C:\\Users\\Aziz Rasool\\Desktop\\Personal Projects\\TimeTable Reader\\TimeTable, FSC, Spring-2023.xlsx\")\n",
    "\n",
    "day_names = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']\n",
    "week=[] # list to store dataframe of timetables of all days of the week\n",
    "classes=[] # list to store dataframe of all classes of all days of the week\n",
    "labs=[] # list to store dataframe of all labs of all days of the week\n",
    "\n",
    "for day in day_names:\n",
    "    temp = pd.read_excel(TimeTable, day)\n",
    "    week.append(temp)\n",
    "    \n",
    "for day in week:\n",
    "    day = drop_top_rows(day) # drop unncessary top rows\n",
    "    separate_labs_and_classes(day, classes, labs) # separate lab data and class data\n",
    "    # database is ready at this point"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63c19c91",
   "metadata": {},
   "source": [
    "## Return Time, Day, Room Number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ed3a96f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_lab(find, subject, day_names, i, fhandle): # parameters: dataframe, subject name\n",
    "    lab = find['Lab']\n",
    "    lab = list(lab)\n",
    "    lab = lab.pop()    \n",
    "    \n",
    "    cond = find==subject\n",
    "    find = find.transpose()\n",
    "    find.columns = [i for i in range(len(find.columns))]\n",
    "    cond=find[0]==subject\n",
    "    find = find[cond]\n",
    "    \n",
    "    time = find.index\n",
    "    time = list(time)\n",
    "    time = time.pop()\n",
    "    \n",
    "    fhandle.write('Subject : ')\n",
    "    fhandle.write(subject)\n",
    "    fhandle.write('\\n')\n",
    "    fhandle.write('Lab : ')\n",
    "    fhandle.write(lab)\n",
    "    fhandle.write('\\n')\n",
    "    fhandle.write('Time : ')\n",
    "    fhandle.write(time)\n",
    "    fhandle.write('\\n')\n",
    "    fhandle.write('Day : ')\n",
    "    fhandle.write(day_names[i])\n",
    "    fhandle.write('\\n')\n",
    "#     print('Subject : ', subject)\n",
    "#     print('Lab : ', lab)\n",
    "#     print('Time : ', time)\n",
    "#     print('Day : ', day_names[i])    \n",
    "    \n",
    "def find_class(find, subject, day_names, i, fhandle): # parameters: dataframe, subject name\n",
    "    room = find['Room']\n",
    "    room = list(room)\n",
    "    room = room.pop()    \n",
    "    \n",
    "    cond = find==subject\n",
    "    find = find.transpose()\n",
    "    find.columns = [i for i in range(len(find.columns))]\n",
    "    cond=find[0]==subject\n",
    "    find = find[cond]\n",
    "    \n",
    "    time = find.index\n",
    "    time = list(time)\n",
    "    time = time.pop()\n",
    "    \n",
    "    fhandle.write('Subject : ')\n",
    "    fhandle.write(subject)\n",
    "    fhandle.write('\\n')\n",
    "    fhandle.write('Room : ')\n",
    "    fhandle.write(room)\n",
    "    fhandle.write('\\n')\n",
    "    fhandle.write('Time : ')\n",
    "    fhandle.write(time)\n",
    "    fhandle.write('\\n')\n",
    "    fhandle.write('Day : ')\n",
    "    fhandle.write(day_names[i])\n",
    "    fhandle.write('\\n')\n",
    "#     print('Subject : ', subject)\n",
    "#     print('Room : ', room)\n",
    "#     print('Time : ', time)\n",
    "#     print('Day : ', day_names[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f03d46bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_details(subject, fhandle):\n",
    "    # one df in list of dfs\n",
    "    not_found_count=0\n",
    "    for i in range(len((classes))):\n",
    "        find = classes[i][classes[i].eq(subject).any(1)]\n",
    "\n",
    "        if len(find)==0: # important checks\n",
    "            # user might have entered the name of a lab\n",
    "            find=labs[i][labs[i].eq(subject).any(1)]\n",
    "            if len(find)==0:\n",
    "                not_found_count+=1\n",
    "                continue\n",
    "                # search in next day\n",
    "            else:\n",
    "                find_lab(find, subject, day_names, i, fhandle)\n",
    "        else:\n",
    "            find_class(find, subject, day_names, i, fhandle)\n",
    "    if not_found_count==5:\n",
    "        print('No class or lab with name {} found.'.format(subject))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "40e81df4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_days(day):\n",
    "    days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']\n",
    "    return days.index(day.strip())\n",
    "    \n",
    "def sort_timetable(timetable: str) -> pd.DataFrame():\n",
    "    timetable = timetable.split('Subject')[1:]\n",
    "    subject_title = []\n",
    "    room = []\n",
    "    time = []\n",
    "    day = []\n",
    "    for subject in timetable:\n",
    "        details = subject.split('\\n')\n",
    "        subject_title.append(details[0].split(':')[1])\n",
    "        room.append(details[1].split(':')[1])\n",
    "        time.append(details[2].split(' : ')[1])\n",
    "        day.append(details[3].split(':')[1])\n",
    "\n",
    "    df = pd.DataFrame(list(zip(room, time, day)), index = subject_title)\n",
    "    df.columns = ['Room', 'Time', 'Day']\n",
    "    df['Day Number'] = df['Day'].apply(map_days)\n",
    "    df = df.sort_values(by='Day Number')\n",
    "    df.drop('Day Number', axis=1, inplace=True)\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e4924635",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_timetable(subjects: list) -> pd.DataFrame():\n",
    "    fhandle = open('unsorted_timetable.txt', 'w')\n",
    "    for subject in subjects:\n",
    "        find_details(subject, fhandle)\n",
    "        fhandle.write('\\n')\n",
    "    #     print()    \n",
    "    \n",
    "    timetable = ''\n",
    "\n",
    "    fhandle = open('unsorted_timetable.txt', 'r')\n",
    "\n",
    "    for line in fhandle:\n",
    "        timetable += line\n",
    "\n",
    "    df = sort_timetable(timetable)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a991570c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:5: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find = classes[i][classes[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:9: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find=labs[i][labs[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:5: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find = classes[i][classes[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:9: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find=labs[i][labs[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:5: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find = classes[i][classes[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:9: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find=labs[i][labs[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:5: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find = classes[i][classes[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:9: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find=labs[i][labs[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:5: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find = classes[i][classes[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:9: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find=labs[i][labs[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:5: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find = classes[i][classes[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:9: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find=labs[i][labs[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:5: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find = classes[i][classes[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:9: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find=labs[i][labs[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:5: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find = classes[i][classes[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:9: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find=labs[i][labs[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:5: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find = classes[i][classes[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:9: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find=labs[i][labs[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:5: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find = classes[i][classes[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:9: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find=labs[i][labs[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:5: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find = classes[i][classes[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:9: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find=labs[i][labs[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:5: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find = classes[i][classes[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:9: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find=labs[i][labs[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:5: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find = classes[i][classes[i].eq(subject).any(1)]\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3072477601.py:9: FutureWarning: In a future version of pandas all arguments of DataFrame.any and Series.any will be keyword-only.\n",
      "  find=labs[i][labs[i].eq(subject).any(1)]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Room</th>\n",
       "      <th>Time</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Comp Networks (AI-J)</th>\n",
       "      <td>A-315</td>\n",
       "      <td>08:30-09:50</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PDC (AI-J)</th>\n",
       "      <td>A-315</td>\n",
       "      <td>10:00-11:20</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DB (CS-G)</th>\n",
       "      <td>B-229</td>\n",
       "      <td>10:00-11:20</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Art Neural Net (AI-J)</th>\n",
       "      <td>B-229</td>\n",
       "      <td>08:30-09:50</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIP (AI-J)</th>\n",
       "      <td>B-129</td>\n",
       "      <td>10:00-11:20</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DB Lab (CS-G)</th>\n",
       "      <td>C-Rawal 1</td>\n",
       "      <td>11:25-02:10</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comp Networks (AI-J)</th>\n",
       "      <td>A-314</td>\n",
       "      <td>08:30-09:50</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PDC (AI-J)</th>\n",
       "      <td>A-314</td>\n",
       "      <td>10:00-11:20</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comp Networks Lab (AI-J)</th>\n",
       "      <td>A-Mehran 1</td>\n",
       "      <td>11:25-02:10</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DB (CS-G)</th>\n",
       "      <td>B-229</td>\n",
       "      <td>10:00-11:20</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Art Neural Net (AI-J)</th>\n",
       "      <td>C-404</td>\n",
       "      <td>08:30-09:50</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIP (AI-J)</th>\n",
       "      <td>C-404</td>\n",
       "      <td>10:00-11:20</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Room         Time         Day\n",
       " Comp Networks (AI-J)            A-315  08:30-09:50      Monday\n",
       " PDC (AI-J)                      A-315  10:00-11:20      Monday\n",
       " DB (CS-G)                       B-229  10:00-11:20      Monday\n",
       " Art Neural Net (AI-J)           B-229  08:30-09:50     Tuesday\n",
       " DIP (AI-J)                      B-129  10:00-11:20     Tuesday\n",
       " DB Lab (CS-G)               C-Rawal 1  11:25-02:10     Tuesday\n",
       " Comp Networks (AI-J)            A-314  08:30-09:50   Wednesday\n",
       " PDC (AI-J)                      A-314  10:00-11:20   Wednesday\n",
       " Comp Networks Lab (AI-J)   A-Mehran 1  11:25-02:10   Wednesday\n",
       " DB (CS-G)                       B-229  10:00-11:20   Wednesday\n",
       " Art Neural Net (AI-J)           C-404  08:30-09:50    Thursday\n",
       " DIP (AI-J)                      C-404  10:00-11:20    Thursday"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subjects = [\n",
    "    'Art Neural Net (AI-J)',\n",
    "    'DIP (AI-J)',\n",
    "    'Comp Networks (AI-J)',\n",
    "    'PDC (AI-J)',\n",
    "    'Comp Networks Lab (AI-J)',\n",
    "    'DB (CS-G)',\n",
    "    'DB Lab (CS-G)'\n",
    "]\n",
    "\n",
    "df = generate_timetable(subjects)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "272f14ca",
   "metadata": {},
   "source": [
    "### Detect Clashes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5fc4be38",
   "metadata": {},
   "outputs": [],
   "source": [
    "def start_time(time: str) -> str:\n",
    "    time = time.split('-')[0]\n",
    "    return time\n",
    "\n",
    "def end_time(time: str) -> str:\n",
    "    time = time.split('-')[1]\n",
    "    return time\n",
    "\n",
    "def standarize(time: str) -> int: # to bring the time to a (kind of) 24 hour format\n",
    "    time = time.replace(':', '')\n",
    "    time = int(time)\n",
    "\n",
    "    if time < 800:\n",
    "        time += 1200\n",
    "        \n",
    "    return time\n",
    "\n",
    "def label(difference: int) -> str:\n",
    "    if difference == 120:\n",
    "        return 'Class'\n",
    "    return 'Lab'\n",
    "\n",
    "def std(time: int) -> str: # standarize time to a small scale of 1s and 3s\n",
    "    return time//100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1063d5d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def detect_clashes(df: pd.DataFrame()) -> pd.DataFrame():\n",
    "    df['Start Time'] = df['Time'].apply(start_time)\n",
    "    df['End Time'] = df['Time'].apply(end_time)\n",
    "\n",
    "    days = [' Monday', ' Tuesday', ' Wednesday', ' Thursday', ' Friday']\n",
    "\n",
    "    df['Start Time'] = df['Start Time'].apply(standarize)\n",
    "    df['End Time'] = df['End Time'].apply(standarize)\n",
    "    df['Difference'] = df['End Time'] - df['Start Time']\n",
    "    df['Label'] = df['Difference'].apply(label)\n",
    "    # at this point I have just separeted the start time and end time of the classes\n",
    "\n",
    "    days = [' Monday', ' Tuesday', ' Wednesday', ' Thursday', ' Friday']\n",
    "    clash = pd.DataFrame()\n",
    "\n",
    "    for day in days:\n",
    "        test_df = df[df['Day'] == day] # case study\n",
    "\n",
    "        # test_df['Start Time'] = test_df['Start Time'].apply(std)\n",
    "        # test_df['End Time'] = test_df['End Time'].apply(std)\n",
    "        # test_df['Difference'] = test_df['End Time'] - test_df['Start Time']\n",
    "\n",
    "        # now i want to increment the start time by a factor of 'Difference'\n",
    "        # then check in the dataframe if a clash exists\n",
    "\n",
    "        test_df2 = test_df\n",
    "        for i in range(len(test_df2)):\n",
    "            test_df2 = df[df['Day'] == day]\n",
    "            counts_series = (test_df2['Start Time'].value_counts())\n",
    "            ind = list(counts_series[counts_series > 1].index)\n",
    "            for x in ind:\n",
    "                clash1 = test_df2.loc[test_df2['Start Time'] == x]\n",
    "                clash = clash.append(clash1)\n",
    "            if test_df2['Difference'].iloc[i] > 1: # indicating a lab, clash in course can be detected simply\n",
    "                while test_df2['Difference'].iloc[i] > 0:\n",
    "                    test_df2['Start Time'].iloc[i] += 1\n",
    "                    test_df2['Difference'].iloc[i] -= 1\n",
    "                    counts_series = (test_df2['Start Time'].value_counts())\n",
    "                    ind = list(counts_series[counts_series > 1].index)\n",
    "                    for x in ind:\n",
    "                        clash1 = test_df2.loc[test_df2['Start Time'] == x]\n",
    "                        clash = clash.append(clash1)\n",
    "    clash = clash.drop_duplicates() # because it returns multiple rows of same clashes\n",
    "    return clash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e2ff273b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df['Start Time'] = test_df['Start Time'].apply(std)\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df['End Time'] = test_df['End Time'].apply(std)\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df['Difference'] = test_df['End Time'] - test_df['Start Time']\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:32: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  clash = clash.append(clash1)\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:32: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  clash = clash.append(clash1)\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:32: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  clash = clash.append(clash1)\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df['Start Time'] = test_df['Start Time'].apply(std)\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df['End Time'] = test_df['End Time'].apply(std)\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df['Difference'] = test_df['End Time'] - test_df['Start Time']\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:35: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df2['Start Time'].iloc[i] += 1\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:36: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df2['Difference'].iloc[i] -= 1\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:35: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df2['Start Time'].iloc[i] += 1\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:36: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df2['Difference'].iloc[i] -= 1\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:35: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df2['Start Time'].iloc[i] += 1\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:36: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df2['Difference'].iloc[i] -= 1\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df['Start Time'] = test_df['Start Time'].apply(std)\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df['End Time'] = test_df['End Time'].apply(std)\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df['Difference'] = test_df['End Time'] - test_df['Start Time']\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:32: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  clash = clash.append(clash1)\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:32: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  clash = clash.append(clash1)\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:32: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  clash = clash.append(clash1)\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:35: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df2['Start Time'].iloc[i] += 1\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:36: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df2['Difference'].iloc[i] -= 1\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  clash = clash.append(clash1)\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:35: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df2['Start Time'].iloc[i] += 1\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:36: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df2['Difference'].iloc[i] -= 1\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  clash = clash.append(clash1)\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:35: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df2['Start Time'].iloc[i] += 1\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:36: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df2['Difference'].iloc[i] -= 1\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:41: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  clash = clash.append(clash1)\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:32: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  clash = clash.append(clash1)\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df['Start Time'] = test_df['Start Time'].apply(std)\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df['End Time'] = test_df['End Time'].apply(std)\n",
      "C:\\Users\\Aziz Rasool\\AppData\\Local\\Temp\\ipykernel_10160\\3621762527.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df['Difference'] = test_df['End Time'] - test_df['Start Time']\n"
     ]
    }
   ],
   "source": [
    "clash = detect_clashes(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "22e3f58c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You have a clash in your timetable\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Room</th>\n",
       "      <th>Time</th>\n",
       "      <th>Day</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PDC (AI-J)</th>\n",
       "      <td>A-315</td>\n",
       "      <td>10:00-11:20</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DB (CS-G)</th>\n",
       "      <td>B-229</td>\n",
       "      <td>10:00-11:20</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PDC (AI-J)</th>\n",
       "      <td>A-314</td>\n",
       "      <td>10:00-11:20</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DB (CS-G)</th>\n",
       "      <td>B-229</td>\n",
       "      <td>10:00-11:20</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Class</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Room         Time         Day  Label\n",
       " PDC (AI-J)   A-315  10:00-11:20      Monday  Class\n",
       " DB (CS-G)    B-229  10:00-11:20      Monday  Class\n",
       " PDC (AI-J)   A-314  10:00-11:20   Wednesday  Class\n",
       " DB (CS-G)    B-229  10:00-11:20   Wednesday  Class"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if len(clash) > 1:\n",
    "    print('You have a clash in your timetable')\n",
    "    clash.drop(['Start Time', 'End Time', 'Difference'], axis=1, inplace=True)\n",
    "else:\n",
    "    print('No Clash detected in timetable')\n",
    "\n",
    "clash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37fa7235",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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