cao.ipynb

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   "source": [
    "***\n",
    "# Fundamentals of Data Analytics Assessment - CAO Points Analysis\n",
    "\n",
    "***"
   ]
  },
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   "source": [
    "![img](images/cao_logo.png)"
   ]
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   "source": [
    "Students who wish to study at third level in the Republic of Ireland apply to colleges and universities through the Central Applications Office (CAO) [1]. <i>\"The purpose of the Central Applications Office (CAO) is to process centrally applications for undergraduate courses in Irish Higher Education Institutions\"</i> [2].\n",
    " \n",
    "<i>“The CAO awards points to students based on their achievements in the Leaving Certificate examination. A student's points are calculated according to these tables (see below), counting their best six subjects only”</i> [3]. A student can study a subject at higher or ordinary level - more points are awarded for higher level papers. Since 2012, the maximum number of points a student can receive is 625 points [3]."
   ]
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   "source": [
    "![img](images/points.jpg)"
   ]
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   "source": [
    "The entry points needed for a course is dependent on demand. This means the points requirements for a course varies year on year.  "
   ]
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   "source": [
    "This notebook will focus on comparing the CAO points for 2019, 2020 and 2021 and provide an overview of how to load the points from the CAO website into a `pandas` dataframe."
   ]
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   "source": [
    "***\n",
    "***"
   ]
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   "source": [
    "### Importing Libraries "
   ]
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    "# Convenient HTTP requests.\n",
    "import requests as rq\n",
    "\n",
    "# Regular expressions.\n",
    "import re\n",
    "\n",
    "# Dates and times.\n",
    "import datetime as dt\n",
    "\n",
    "# Data frames.\n",
    "import pandas as pd\n",
    "\n",
    "# Working with data\n",
    "import numpy as np\n",
    "\n",
    "# For downloading.\n",
    "import urllib.request as urlrq\n",
    "\n",
    "# For scraping data from pdf\n",
    "import camelot\n",
    "\n",
    "# Unzipping folders \n",
    "from zipfile import ZipFile\n",
    "\n",
    "# To merge csv files into one\n",
    "import os, glob\n",
    "\n",
    "# Common string operations\n",
    "import string as str"
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   "source": [
    "# Visualisation\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Visualisation\n",
    "import seaborn as sns\n",
    "\n",
    "# Standard plot size.\n",
    "plt.rcParams['figure.figsize'] = (15, 10)\n",
    "\n",
    "# Selecting a colour scheme.\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "# Configures matplotlib to show figures embedded in the notebook. \n",
    "%matplotlib inline"
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    "# Get the current date and time.\n",
    "now = dt.datetime.now()\n",
    "\n",
    "# Format as a string.\n",
    "nowstr = now.strftime('%Y%m%d_%H%M%S')"
   ]
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    "***\n",
    "****"
   ]
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   "source": [
    "## 2021 CAO Points\n"
   ]
  },
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   "source": [
    "***"
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   "source": [
    "### Level 8  \n",
    "[Link to 2021 CAO points (Level 8)]('http://www2.cao.ie/points/l8.php')"
   ]
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   "source": [
    "As the 2021 points are stored as a html file on the CAO website, we must make a request to the server they are stored on. We can easily do this by using the python requests package which makes HTTP requests easily and simple and \"human friendly\". <i>\"The HTTP request returns a Response Object with all the response data (content, encoding, status, etc)\" </i>[4]."
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   "source": [
    "#### Server Request"
   ]
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the CAO points URL\n",
    "resp = rq.get('http://www2.cao.ie/points/l8.php')\n",
    "\n",
    "# 200 = ok. 404 = error: not found\n",
    "resp"
   ]
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   "source": [
    "#### Save Orignal Dataset\n"
   ]
  },
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   "source": [
    "To save the original `HTML` file from the request we need to create a path in our repository. Using the datetime module we can convert the response request date to the day the request was made."
   ]
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   "source": [
    "# Create a file path for the original data.\n",
    "pathhtml = 'datasets/cao2021_' + nowstr + '.html'"
   ]
  },
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   "id": "cf6ebf8d-6a79-4307-a7f7-911dfa56863f",
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   "source": [
    "### Webserver Error\n"
   ]
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   "source": [
    "The CAO website throws an error - it tells us to decode in `iso-8859-1` but, this isn't possible as some of the lines includes` \\x96 `- a fada - which isn't recognised in `iso-8859-1`. To combat this, we use the decoding standard `cp1252` which includes `\\x96` which allows the fada to be understood. "
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   "source": [
    "Webserver error - server says decode as:\n",
    "\n",
    "    Content-Type: text/html; charset=iso-8859-1\n",
    "    \n",
    "However, one line uses `\\x96` which isn't defined in `iso-8859-1`. \n",
    "\n",
    "Therefore, a similar decoding standard `cp1252` was used. It is similar but includes  `\\x96`"
   ]
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   "source": [
    "# Fixing the webserves wrong encoding\n",
    "original_encoding = resp.encoding\n",
    "\n",
    "# Changing to cp1252\n",
    "resp.encoding = 'cp1252'"
   ]
  },
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   "source": [
    "# Save the original html file.\n",
    "with open(pathhtml, 'w') as f: # Opening path in write mode\n",
    "    f.write(resp.text)"
   ]
  },
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   "source": [
    "### Getting The Data\n"
   ]
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   "source": [
    "[Link To Regular Expression Documentation]('https://docs.python.org/3/library/re.html')"
   ]
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   "source": [
    "To get the relevant lines from the response request, we employ a regular expression. A regular expression <i>\"...is a sequence of characters that specifies a search pattern\"</i> [5]. You can think of them as a search and find function. We use a regular expression as it more efficient than constantly recalling an expression over and over."
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   "source": [
    "# Compile regular expression for matching lines.\n",
    "re_course = re.compile(r'([A-Z]{2}[0-9]{3})(.*)') # r for raw data"
   ]
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   "source": [
    "We will then use this expression to loop through the response from earlier for any matches of the regular expression and save these matches in a CSV file. "
   ]
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   "source": [
    "# Path to csv file\n",
    "path2021 = 'datasets/cao2021_csv_' + nowstr + '.csv'"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of lines is 949.\n"
     ]
    }
   ],
   "source": [
    "# Tracking number of courses matched\n",
    "no_lines = 0\n",
    "\n",
    "\n",
    "\n",
    "# Open the csv file for writing.\n",
    "with open(path2021, 'w') as f:\n",
    "    # Write a header row.\n",
    "    f.write(','.join(['code', 'title', 'pointsR1', 'pointsR2']) + '\\n')\n",
    "    # Loop through lines of the response.\n",
    "    for line in resp.iter_lines():\n",
    "        # Decode the line, using the wrong encoding!\n",
    "        dline = line.decode('cp1252')\n",
    "        # Match only the lines representing courses.\n",
    "        if re_course.fullmatch(dline):\n",
    "            # Add one to the lines counter.\n",
    "            no_lines = no_lines + 1\n",
    "            # The course code.\n",
    "            course_code = dline[:5] # i.e first 5 characters\n",
    "            # The course title.\n",
    "            course_title = dline[7:57].strip() # Strip gets rid of whitespace\n",
    "            # Round one points.\n",
    "            course_points = re.split(' +', dline[60:]) # split into substringd from index 60 \n",
    "            if len(course_points) != 2: # This is because last course has an extra substring\n",
    "                course_points = course_points[:2]\n",
    "            # Join the fields using a comma.\n",
    "            linesplit = [course_code, course_title, course_points[0], course_points[1]]\n",
    "            # Rejoin the substrings with commas in between.\n",
    "            f.write(','.join(linesplit) + '\\n')\n",
    "\n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "# Number of courses matched\n",
    "print(f\"Total number of lines is {no_lines}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c39f3281-2ae8-4ac1-b0dd-636e184f7897",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1216a7ae-d58e-442c-8bee-c3c9d65d69b0",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e07b846-3906-4e6d-80d8-b7f8c3762160",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83cad955-40cf-4be2-bfae-368faa0eea59",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bdf89fe4-174a-459a-b218-a56e63a72ff5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading dataframe\n",
    "df2021 = pd.read_csv(path2021, encoding='cp1252')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "faeba46f-1781-4df2-8e88-4bdf0fb75ae5",
   "metadata": {},
   "outputs": [
    {
     "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>code</th>\n",
       "      <th>title</th>\n",
       "      <th>pointsR1</th>\n",
       "      <th>pointsR2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL801</td>\n",
       "      <td>Software Design for Virtual Reality and Gaming</td>\n",
       "      <td>300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL802</td>\n",
       "      <td>Software Design in Artificial Intelligence for...</td>\n",
       "      <td>313</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL803</td>\n",
       "      <td>Software Design for Mobile Apps and Connected ...</td>\n",
       "      <td>350</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL805</td>\n",
       "      <td>Computer Engineering for Network Infrastructure</td>\n",
       "      <td>321</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL810</td>\n",
       "      <td>Quantity Surveying</td>\n",
       "      <td>328</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>944</th>\n",
       "      <td>WD211</td>\n",
       "      <td>Creative Computing</td>\n",
       "      <td>270</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>945</th>\n",
       "      <td>WD212</td>\n",
       "      <td>Recreation and Sport Management</td>\n",
       "      <td>262</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>WD230</td>\n",
       "      <td>Mechanical and Manufacturing Engineering</td>\n",
       "      <td>230</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>WD231</td>\n",
       "      <td>Early Childhood Care and Education</td>\n",
       "      <td>266</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>WD232</td>\n",
       "      <td>Business Information Systems</td>\n",
       "      <td>261</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>949 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      code                                              title pointsR1  \\\n",
       "0    AL801     Software Design for Virtual Reality and Gaming      300   \n",
       "1    AL802  Software Design in Artificial Intelligence for...      313   \n",
       "2    AL803  Software Design for Mobile Apps and Connected ...      350   \n",
       "3    AL805    Computer Engineering for Network Infrastructure      321   \n",
       "4    AL810                                 Quantity Surveying      328   \n",
       "..     ...                                                ...      ...   \n",
       "944  WD211                                 Creative Computing      270   \n",
       "945  WD212                    Recreation and Sport Management      262   \n",
       "946  WD230           Mechanical and Manufacturing Engineering      230   \n",
       "947  WD231                 Early Childhood Care and Education      266   \n",
       "948  WD232                       Business Information Systems      261   \n",
       "\n",
       "    pointsR2  \n",
       "0        NaN  \n",
       "1        NaN  \n",
       "2        NaN  \n",
       "3        NaN  \n",
       "4        NaN  \n",
       "..       ...  \n",
       "944      NaN  \n",
       "945      NaN  \n",
       "946      230  \n",
       "947      NaN  \n",
       "948      261  \n",
       "\n",
       "[949 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2021"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "383616b7-5390-4800-b65c-1aae713107fc",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff526da2-87b2-4a81-8e7e-2990d905f4b7",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0bc925c-9fa2-46f9-a1c5-072d04a99d78",
   "metadata": {},
   "source": [
    "As we know these are all level 8 courses, we will also insert a new column into the dataframe to show this."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59277e35-d59e-4232-9e70-fe7166ace82f",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a343c01-e070-4274-b7f1-01ff4c7f9a33",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d77614a8-ea9a-4529-87b2-f873eb2cd78d",
   "metadata": {},
   "outputs": [
    {
     "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>code</th>\n",
       "      <th>title</th>\n",
       "      <th>level</th>\n",
       "      <th>pointsR1</th>\n",
       "      <th>pointsR2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL801</td>\n",
       "      <td>Software Design for Virtual Reality and Gaming</td>\n",
       "      <td>8</td>\n",
       "      <td>300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL802</td>\n",
       "      <td>Software Design in Artificial Intelligence for...</td>\n",
       "      <td>8</td>\n",
       "      <td>313</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL803</td>\n",
       "      <td>Software Design for Mobile Apps and Connected ...</td>\n",
       "      <td>8</td>\n",
       "      <td>350</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL805</td>\n",
       "      <td>Computer Engineering for Network Infrastructure</td>\n",
       "      <td>8</td>\n",
       "      <td>321</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL810</td>\n",
       "      <td>Quantity Surveying</td>\n",
       "      <td>8</td>\n",
       "      <td>328</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>944</th>\n",
       "      <td>WD211</td>\n",
       "      <td>Creative Computing</td>\n",
       "      <td>8</td>\n",
       "      <td>270</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>945</th>\n",
       "      <td>WD212</td>\n",
       "      <td>Recreation and Sport Management</td>\n",
       "      <td>8</td>\n",
       "      <td>262</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>WD230</td>\n",
       "      <td>Mechanical and Manufacturing Engineering</td>\n",
       "      <td>8</td>\n",
       "      <td>230</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>WD231</td>\n",
       "      <td>Early Childhood Care and Education</td>\n",
       "      <td>8</td>\n",
       "      <td>266</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>WD232</td>\n",
       "      <td>Business Information Systems</td>\n",
       "      <td>8</td>\n",
       "      <td>261</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>949 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      code                                              title level pointsR1  \\\n",
       "0    AL801     Software Design for Virtual Reality and Gaming     8      300   \n",
       "1    AL802  Software Design in Artificial Intelligence for...     8      313   \n",
       "2    AL803  Software Design for Mobile Apps and Connected ...     8      350   \n",
       "3    AL805    Computer Engineering for Network Infrastructure     8      321   \n",
       "4    AL810                                 Quantity Surveying     8      328   \n",
       "..     ...                                                ...   ...      ...   \n",
       "944  WD211                                 Creative Computing     8      270   \n",
       "945  WD212                    Recreation and Sport Management     8      262   \n",
       "946  WD230           Mechanical and Manufacturing Engineering     8      230   \n",
       "947  WD231                 Early Childhood Care and Education     8      266   \n",
       "948  WD232                       Business Information Systems     8      261   \n",
       "\n",
       "    pointsR2  \n",
       "0        NaN  \n",
       "1        NaN  \n",
       "2        NaN  \n",
       "3        NaN  \n",
       "4        NaN  \n",
       "..       ...  \n",
       "944      NaN  \n",
       "945      NaN  \n",
       "946      230  \n",
       "947      NaN  \n",
       "948      261  \n",
       "\n",
       "[949 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a new column at the second index with the value 8\n",
    "df2021.insert(2, 'level', '8')\n",
    "df2021"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8ac8746-8313-414b-b859-bc45eeb5e9f9",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f98a4e99-d6a4-412a-8ec3-25f575a79046",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc5b8e3f-e03e-4853-8956-ee011fa7963d",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c3da752-a60c-44d6-a240-708bc1be83b6",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b409506-5c4c-493b-aa4c-7d03a7f9b1a9",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aab34a7-c8c5-4cba-9eee-7ef7aa6b077c",
   "metadata": {},
   "source": [
    "## Level 6/7 Courses\n",
    "\n",
    "[Link to 2021 level 6 & 7 Courses](http://www2.cao.ie/points/l76.php)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3146e6c-25c5-434c-89d9-d1d97ecde758",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b5dc59e-95ca-4614-9d49-9c1766fabf33",
   "metadata": {},
   "source": [
    "To retrieve the 2021 level 6 and 7 courses we repeat this process."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9319905-d530-4899-b2fa-077a27af82df",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cff6020f-a909-41e7-ae84-4744f652e541",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Server Request"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b0d7712-0fc6-4cc2-bb0c-a06dfa611a6a",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "efbc09b7-338f-43a6-9d26-bb52a6c2c58b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fetch the CAO points URL.\n",
    "resp = rq.get('http://www2.cao.ie/points/l76.php')\n",
    "\n",
    "# 200 = ok. 404 = error: not found\n",
    "resp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79929c73-415a-45c5-9478-60ca639834bd",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96b0df1b-2d22-4243-9013-1405e3a0ec1b",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a0b8e9e-d117-4a4c-85c0-892cd35f2bb2",
   "metadata": {},
   "source": [
    "### Save Orignal Dataset\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef24a6bb-3d6a-4695-9220-744a1aee7337",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "db4494f6-0970-44af-9474-653558bae464",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a file path for the original data.\n",
    "pathhtml_2 = 'datasets/cao2021_2' + nowstr + '.html'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94c44744-0a54-4a73-9514-50158373a7c1",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42bd2da8-4946-45a3-9b0d-e2afea32c96e",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a95eaa4-52f8-4ea4-a854-8cab1ccf369e",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Webserver Error\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9391e500-df62-4fe9-8d14-404e01c3a90a",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b89901fa-e71a-4305-b15e-6cd698ca68fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fixing the webserves wrong encoding\n",
    "original_encoding = resp.encoding\n",
    "\n",
    "# Changing to cp1252\n",
    "resp.encoding = 'cp1252'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9623d244-f191-415a-a687-c4eeffd5f663",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "401249fd-d330-432a-ab36-8f934040b51a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the original html file.\n",
    "with open(pathhtml_2, 'w') as f: # Opening path in write mode\n",
    "    f.write(resp.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd1f429b-f17a-46b1-9163-dc50bc8c4763",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8cbd674-72ce-4c52-9471-89deb07c38e6",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cccb32a-f400-4d8d-804e-539453b5cd7a",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Getting The Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74d89d2f-f04b-4f25-a21a-502e1c6378f1",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "784e211e-2996-4828-a826-1a3ce50ecdec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile regular expression for matching lines.\n",
    "re_course = re.compile(r'([A-Z]{2}[0-9]{3})(.*)') # r for raw data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "389c7dd2-1eff-481c-8b27-c390b2237a45",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1595586f-b75e-43cd-a8c6-24df405bd597",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0d5b8009-8b24-4b9d-acc2-9e847a2e8157",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path to csv file\n",
    "path2021_2 = 'datasets/cao2021_2_csv_' + nowstr + '.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6174aaa-11ac-48a4-be0d-07bec4b2e1c6",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3da4a8b5-dae3-44e5-a20d-c69584022cab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of lines is 416.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Tracking number of courses matched\n",
    "no_lines = 0\n",
    "\n",
    "\n",
    "\n",
    "# Open the csv file for writing.\n",
    "with open(path2021_2, 'w') as f:\n",
    "    # Write a header row.\n",
    "    f.write(','.join(['code', 'title', 'pointsR1', 'pointsR2']) + '\\n')\n",
    "    # Loop through lines of the response.\n",
    "    for line in resp.iter_lines():\n",
    "        # Decode the line, using the wrong encoding!\n",
    "        dline = line.decode('cp1252')\n",
    "        # Match only the lines representing courses.\n",
    "        if re_course.fullmatch(dline):\n",
    "            # Add one to the lines counter.\n",
    "            no_lines = no_lines + 1\n",
    "            # The course code.\n",
    "            course_code = dline[:5]\n",
    "            # The course title.\n",
    "            course_title = dline[7:57].strip()\n",
    "            # Round one points.\n",
    "            course_points = re.split(' +', dline[60:])\n",
    "            if len(course_points) != 2:\n",
    "                course_points = course_points[:2]\n",
    "            # Join the fields using a comma.\n",
    "            linesplit = [course_code, course_title, course_points[0], course_points[1]]\n",
    "            # Rejoin the substrings with commas in between.\n",
    "            f.write(','.join(linesplit) + '\\n')\n",
    "\n",
    "\n",
    "            \n",
    "# Number of courses matched\n",
    "print(f\"Total number of lines is {no_lines}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b4791c0-8c90-416e-bd6b-a31c4e4bb6f2",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a376bae-8e4c-4704-8834-34c004563fbc",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "817fe374-d355-4132-8350-e5d851d1ee79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading dataframe\n",
    "df2021_2 = pd.read_csv(path2021_2, encoding='cp1252')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f7bed73-a292-4fd5-b568-1109cb49cb98",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f6da4f-586f-4e9b-badb-c16163f45bcb",
   "metadata": {},
   "source": [
    "Unfortunately, the 2021 level 6 and 7 courses aren't distinguished from each other. So, I've decided to insert them as '6/7' under a new column heading 'level'. This will distinguish them from the courses we know for certain are level 8."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f0227af-71cc-4e66-93fa-da9766893feb",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dee67ba5-7f38-4e8a-8090-c3d078a0e1d4",
   "metadata": {},
   "outputs": [
    {
     "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>code</th>\n",
       "      <th>title</th>\n",
       "      <th>level</th>\n",
       "      <th>pointsR1</th>\n",
       "      <th>pointsR2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL605</td>\n",
       "      <td>Music and Instrument Technology</td>\n",
       "      <td>6/7</td>\n",
       "      <td>211</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL630</td>\n",
       "      <td>Pharmacy Technician</td>\n",
       "      <td>6/7</td>\n",
       "      <td>308</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL631</td>\n",
       "      <td>Dental Nursing</td>\n",
       "      <td>6/7</td>\n",
       "      <td>311</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL632</td>\n",
       "      <td>Applied Science</td>\n",
       "      <td>6/7</td>\n",
       "      <td>297</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL650</td>\n",
       "      <td>Business</td>\n",
       "      <td>6/7</td>\n",
       "      <td>AQA</td>\n",
       "      <td>AQA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>WD188</td>\n",
       "      <td>Applied Health Care</td>\n",
       "      <td>6/7</td>\n",
       "      <td>220</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>WD205</td>\n",
       "      <td>Molecular Biology with Biopharmaceutical Science</td>\n",
       "      <td>6/7</td>\n",
       "      <td>AQA</td>\n",
       "      <td>262v</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>WD206</td>\n",
       "      <td>Electronic Engineering</td>\n",
       "      <td>6/7</td>\n",
       "      <td>180</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>WD207</td>\n",
       "      <td>Mechanical Engineering</td>\n",
       "      <td>6/7</td>\n",
       "      <td>172</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>WD208</td>\n",
       "      <td>Manufacturing Engineering</td>\n",
       "      <td>6/7</td>\n",
       "      <td>317</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>416 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      code                                             title level pointsR1  \\\n",
       "0    AL605                   Music and Instrument Technology   6/7      211   \n",
       "1    AL630                               Pharmacy Technician   6/7      308   \n",
       "2    AL631                                    Dental Nursing   6/7      311   \n",
       "3    AL632                                   Applied Science   6/7      297   \n",
       "4    AL650                                          Business   6/7      AQA   \n",
       "..     ...                                               ...   ...      ...   \n",
       "411  WD188                               Applied Health Care   6/7      220   \n",
       "412  WD205  Molecular Biology with Biopharmaceutical Science   6/7      AQA   \n",
       "413  WD206                            Electronic Engineering   6/7      180   \n",
       "414  WD207                            Mechanical Engineering   6/7      172   \n",
       "415  WD208                         Manufacturing Engineering   6/7      317   \n",
       "\n",
       "    pointsR2  \n",
       "0        NaN  \n",
       "1        NaN  \n",
       "2        NaN  \n",
       "3        NaN  \n",
       "4        AQA  \n",
       "..       ...  \n",
       "411      NaN  \n",
       "412     262v  \n",
       "413      NaN  \n",
       "414      NaN  \n",
       "415      NaN  \n",
       "\n",
       "[416 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a new column at the second index\n",
    "df2021_2.insert(2, 'level', '6/7')\n",
    "df2021_2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95ce2a5e-76a5-4bdc-9668-3691939523d3",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe9dc1f9-bfb8-42df-88a0-953861e7581d",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9486ad30-3df9-40ee-85c9-d79f74a2398e",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85e7fdcf-31e4-4350-8898-ba84f842839f",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13fbf9c3-a159-4fcb-8d8b-548f3be117c4",
   "metadata": {
    "tags": []
   },
   "source": [
    "***\n",
    "\n",
    "## 2020 Points\n",
    "***\n",
    "\n",
    "https://www.cao.ie/index.php?page=points&p=2020"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d5c7f40-f09c-4678-a180-2a2bd07447de",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb5c93f6-ffe2-44b7-b92c-8173cf56ee2d",
   "metadata": {},
   "source": [
    "Luckily the 2020 points are already available in a excel file. This equates to a simpler workload! Additionally, level 6, 7 and 8 are all included and are clearly marked."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17e62e68-f5c0-4424-a311-5b2e051393cf",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba69bb01-2fd0-467d-951e-3f89a2859cb7",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "63ae4676-e6e7-475b-bab0-c7646301c80e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the URL as a variable\n",
    "url2020 = 'http://www2.cao.ie/points/CAOPointsCharts2020.xlsx'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb29f22d-7345-4c71-860a-e80332e936f5",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ae06735-c174-45bc-b6f0-6f50355101c5",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9db25757-97c2-47ad-9ac5-804d9243c43f",
   "metadata": {},
   "source": [
    "### Saving Orginal Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bf1250d-d135-42d5-8413-81139ff61559",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a5c2f1a-9574-4f90-9c4b-8a9efb06bef4",
   "metadata": {
    "tags": []
   },
   "source": [
    "We must save a copy of the original file; we do this with the `datetime` module which keeps tabs on when the request was made."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24d1f79a-7353-405e-9840-a5d5c9434607",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2ac30704-1a02-44a0-afb2-97b63bd6416d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a file path for original data\n",
    "pathxlsx = 'datasets/cao2020_' + nowstr + '.xlsx'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04152d20-4be0-4911-8ab3-b4777e103bd9",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2b97824-4fea-4f60-a579-284178b43c28",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "013f22b3-09e2-400a-85ca-23772816124e",
   "metadata": {},
   "source": [
    "### Retrieve Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6155312a-4a0f-40e8-8d1e-bff2ec40cd85",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78b4caf6-cae1-4873-b3f2-7081ccb03f51",
   "metadata": {
    "tags": []
   },
   "source": [
    "To get the data we can use the `Urllib` package. `Urlib` is a <i>\"...URL handling module for python. It is used to fetch URLs\"</i> [6]. The retrieve method downloads <i>\"...the remote data directly to the local [disk]\"</i> [7]. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72c506b7-328d-4447-93dd-34c9d2c19437",
   "metadata": {},
   "source": [
    "  <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6351f1ef-6d9c-42e3-8552-5c434839e8e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('datasets/cao2020_20211229_194054.xlsx',\n",
       " <http.client.HTTPMessage at 0x1f0cd1611f0>)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting data\n",
    "urlrq.urlretrieve(url2020, pathxlsx)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa120ab7-e85a-468e-94b0-ad985abc823e",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c77ddd6a-b666-4b0e-b6aa-ef53438e364e",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "640690d4-6218-401e-b491-82f72bde68dd",
   "metadata": {},
   "source": [
    "### Loading The Spreadsheet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3709b83-b426-4e7d-a7e7-ab2d66a4d6e4",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba9c1d91-6e7b-4fff-a8c4-084b4c56fac4",
   "metadata": {},
   "source": [
    "We can now load the data into the notebook by using `pandas`. The first 10 lines of the file include a blurb about the CAO so we will skip the first 10 rows to avoid pulling them into the newly created dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9bbfd36-ac8a-4176-88b3-a9a8d74c10d7",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7d1b9d88-2296-44e2-83bc-55021640d157",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download and parse the excel spreadsheet. First few rows where a blurb\n",
    "df2020 = pd.read_excel(url2020, skiprows=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "61e8dc44-4dc4-4472-ad7f-0bdea4a04e38",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CATEGORY (i.e.ISCED description)</th>\n",
       "      <th>COURSE TITLE</th>\n",
       "      <th>COURSE CODE2</th>\n",
       "      <th>R1 POINTS</th>\n",
       "      <th>R1 Random *</th>\n",
       "      <th>R2 POINTS</th>\n",
       "      <th>R2 Random*</th>\n",
       "      <th>EOS</th>\n",
       "      <th>EOS Random *</th>\n",
       "      <th>EOS Mid-point</th>\n",
       "      <th>...</th>\n",
       "      <th>avp</th>\n",
       "      <th>v</th>\n",
       "      <th>Column1</th>\n",
       "      <th>Column2</th>\n",
       "      <th>Column3</th>\n",
       "      <th>Column4</th>\n",
       "      <th>Column5</th>\n",
       "      <th>Column6</th>\n",
       "      <th>Column7</th>\n",
       "      <th>Column8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Business and administration</td>\n",
       "      <td>International Business</td>\n",
       "      <td>AC120</td>\n",
       "      <td>209</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>209</td>\n",
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       "      <td>280</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Humanities (except languages)</td>\n",
       "      <td>Liberal Arts</td>\n",
       "      <td>AC137</td>\n",
       "      <td>252</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>252</td>\n",
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       "      <td>270</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>Arts</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>#+matric</td>\n",
       "      <td>NaN</td>\n",
       "      <td>#+matric</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arts</td>\n",
       "      <td>Graphic Design and Moving Image Design (portfo...</td>\n",
       "      <td>AD102</td>\n",
       "      <td>#+matric</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>#+matric</td>\n",
       "      <td>NaN</td>\n",
       "      <td>#+matric</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arts</td>\n",
       "      <td>Textile &amp; Surface Design and Jewellery &amp; Objec...</td>\n",
       "      <td>AD103</td>\n",
       "      <td>#+matric</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>#+matric</td>\n",
       "      <td>NaN</td>\n",
       "      <td>#+matric</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>Manufacturing and processing</td>\n",
       "      <td>Manufacturing Engineering</td>\n",
       "      <td>WD208</td>\n",
       "      <td>188</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>188</td>\n",
       "      <td>NaN</td>\n",
       "      <td>339</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>Information and Communication Technologies (ICTs)</td>\n",
       "      <td>Software Systems Development</td>\n",
       "      <td>WD210</td>\n",
       "      <td>279</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>279</td>\n",
       "      <td>NaN</td>\n",
       "      <td>337</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>Information and Communication Technologies (ICTs)</td>\n",
       "      <td>Creative Computing</td>\n",
       "      <td>WD211</td>\n",
       "      <td>271</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>271</td>\n",
       "      <td>NaN</td>\n",
       "      <td>318</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1462</th>\n",
       "      <td>Personal services</td>\n",
       "      <td>Recreation and Sport Management</td>\n",
       "      <td>WD212</td>\n",
       "      <td>270</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>270</td>\n",
       "      <td>NaN</td>\n",
       "      <td>349</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1463</th>\n",
       "      <td>Engineering and engineering trades</td>\n",
       "      <td>Mechanical and Manufacturing Engineering</td>\n",
       "      <td>WD230</td>\n",
       "      <td>253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>369</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1464 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       CATEGORY (i.e.ISCED description)  \\\n",
       "0                           Business and administration   \n",
       "1                         Humanities (except languages)   \n",
       "2                                                  Arts   \n",
       "3                                                  Arts   \n",
       "4                                                  Arts   \n",
       "...                                                 ...   \n",
       "1459                       Manufacturing and processing   \n",
       "1460  Information and Communication Technologies (ICTs)   \n",
       "1461  Information and Communication Technologies (ICTs)   \n",
       "1462                                  Personal services   \n",
       "1463                 Engineering and engineering trades   \n",
       "\n",
       "                                           COURSE TITLE COURSE CODE2  \\\n",
       "0                                International Business        AC120   \n",
       "1                                          Liberal Arts        AC137   \n",
       "2      First Year Art & Design (Common Entry,portfolio)        AD101   \n",
       "3     Graphic Design and Moving Image Design (portfo...        AD102   \n",
       "4     Textile & Surface Design and Jewellery & Objec...        AD103   \n",
       "...                                                 ...          ...   \n",
       "1459                          Manufacturing Engineering        WD208   \n",
       "1460                       Software Systems Development        WD210   \n",
       "1461                                 Creative Computing        WD211   \n",
       "1462                    Recreation and Sport Management        WD212   \n",
       "1463           Mechanical and Manufacturing Engineering        WD230   \n",
       "\n",
       "     R1 POINTS R1 Random * R2 POINTS R2 Random*       EOS EOS Random *  \\\n",
       "0          209         NaN       NaN        NaN       209          NaN   \n",
       "1          252         NaN       NaN        NaN       252          NaN   \n",
       "2     #+matric         NaN       NaN        NaN  #+matric          NaN   \n",
       "3     #+matric         NaN       NaN        NaN  #+matric          NaN   \n",
       "4     #+matric         NaN       NaN        NaN  #+matric          NaN   \n",
       "...        ...         ...       ...        ...       ...          ...   \n",
       "1459       188         NaN       NaN        NaN       188          NaN   \n",
       "1460       279         NaN       NaN        NaN       279          NaN   \n",
       "1461       271         NaN       NaN        NaN       271          NaN   \n",
       "1462       270         NaN       NaN        NaN       270          NaN   \n",
       "1463       253         NaN       NaN        NaN       253          NaN   \n",
       "\n",
       "     EOS Mid-point  ...  avp    v Column1 Column2 Column3  Column4  Column5  \\\n",
       "0              280  ...  NaN  NaN     NaN     NaN     NaN      NaN      NaN   \n",
       "1              270  ...  NaN  NaN     NaN     NaN     NaN      NaN      NaN   \n",
       "2         #+matric  ...  NaN  NaN     NaN     NaN     NaN      NaN      NaN   \n",
       "3         #+matric  ...  NaN  NaN     NaN     NaN     NaN      NaN      NaN   \n",
       "4         #+matric  ...  NaN  NaN     NaN     NaN     NaN      NaN      NaN   \n",
       "...            ...  ...  ...  ...     ...     ...     ...      ...      ...   \n",
       "1459           339  ...  NaN  NaN     NaN     NaN     NaN      NaN      NaN   \n",
       "1460           337  ...  NaN  NaN     NaN     NaN     NaN      NaN      NaN   \n",
       "1461           318  ...  NaN  NaN     NaN     NaN     NaN      NaN      NaN   \n",
       "1462           349  ...  NaN  NaN     NaN     NaN     NaN      NaN      NaN   \n",
       "1463           369  ...  NaN  NaN     NaN     NaN     NaN      NaN      NaN   \n",
       "\n",
       "      Column6  Column7  Column8  \n",
       "0         NaN      NaN      NaN  \n",
       "1         NaN      NaN      NaN  \n",
       "2         NaN      NaN      NaN  \n",
       "3         NaN      NaN      NaN  \n",
       "4         NaN      NaN      NaN  \n",
       "...       ...      ...      ...  \n",
       "1459      NaN      NaN      NaN  \n",
       "1460      NaN      NaN      NaN  \n",
       "1461      NaN      NaN      NaN  \n",
       "1462      NaN      NaN      NaN  \n",
       "1463      NaN      NaN      NaN  \n",
       "\n",
       "[1464 rows x 23 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the dataframe\n",
    "df2020"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a071385-e973-455e-ac45-d29dd3f97274",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2166e32c-7f98-406e-84e0-34ea7eb83861",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edaa7e56-e319-4576-b607-933305980e5d",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7704e4f8-9c33-42b6-9f76-52a00a4b8030",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CATEGORY (i.e.ISCED description)    Engineering and engineering trades\n",
       "COURSE TITLE                                    Mechanical Engineering\n",
       "COURSE CODE2                                                     SG333\n",
       "R1 POINTS                                                          216\n",
       "R1 Random *                                                        NaN\n",
       "R2 POINTS                                                          NaN\n",
       "R2 Random*                                                         NaN\n",
       "EOS                                                                216\n",
       "EOS Random *                                                       NaN\n",
       "EOS Mid-point                                                      347\n",
       "LEVEL                                                                7\n",
       "HEI                                     Institute of Technology, Sligo\n",
       "Test/Interview #                                                   NaN\n",
       "avp                                                                NaN\n",
       "v                                                                  NaN\n",
       "Column1                                                            NaN\n",
       "Column2                                                            NaN\n",
       "Column3                                                            NaN\n",
       "Column4                                                            NaN\n",
       "Column5                                                            NaN\n",
       "Column6                                                            NaN\n",
       "Column7                                                            NaN\n",
       "Column8                                                            NaN\n",
       "Name: 1000, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking random row\n",
    "df2020.iloc[1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0fabd96-74da-4158-bb60-1e9b82d16eff",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4106260e-b052-4dcb-b206-936ecb080d6d",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7397853c-33af-4365-ba08-5540cd693e26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CATEGORY (i.e.ISCED description)          Engineering and engineering trades\n",
       "COURSE TITLE                        Mechanical and Manufacturing Engineering\n",
       "COURSE CODE2                                                           WD230\n",
       "R1 POINTS                                                                253\n",
       "R1 Random *                                                              NaN\n",
       "R2 POINTS                                                                NaN\n",
       "R2 Random*                                                               NaN\n",
       "EOS                                                                      253\n",
       "EOS Random *                                                             NaN\n",
       "EOS Mid-point                                                            369\n",
       "LEVEL                                                                      8\n",
       "HEI                                        Waterford Institute of Technology\n",
       "Test/Interview #                                                         NaN\n",
       "avp                                                                      NaN\n",
       "v                                                                        NaN\n",
       "Column1                                                                  NaN\n",
       "Column2                                                                  NaN\n",
       "Column3                                                                  NaN\n",
       "Column4                                                                  NaN\n",
       "Column5                                                                  NaN\n",
       "Column6                                                                  NaN\n",
       "Column7                                                                  NaN\n",
       "Column8                                                                  NaN\n",
       "Name: 1463, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -1 is always last row/element\n",
    "df2020.iloc[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8821b05-a3b4-4bf1-bdb8-24b385122e02",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a7978dd-0786-4d43-bf98-6499274ce4ea",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b194c63-82f2-4b61-b286-0aaa6d59fe52",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88d13c49-9ae3-48f8-921a-e7779615c867",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac577818-2200-4763-b339-a1eb27670aa8",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "896349eb-c73b-4276-8cac-bf5728266009",
   "metadata": {},
   "source": [
    "### Storing The Dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf4ce6c8-7b1c-48a5-946f-de8c8f9476b8",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5df3a1d8-5210-4d22-8bf5-b1646ce15ee7",
   "metadata": {
    "tags": []
   },
   "source": [
    "Storing the dataframe we created with the other files in the project directory. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68e766f5-fbaa-488c-a24c-f227711d05f4",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0568deaf-0c6d-44ce-ae88-4b3b13c9b7ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a path for dataframe\n",
    "path2020 = 'datasets/cao2020_' + nowstr + '.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65266843-e9ac-4d5b-8194-d11f28d8c31b",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b6635892-5211-4c84-b7c8-c1d1793ca56a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving dataframe to disk\n",
    "df2020.to_csv(path2020)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbc6ddd1-c2df-4662-9927-bcfdb8db8025",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6c4e18d-3f92-41b0-bde7-ac8b9291e75c",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "154eb60c-5114-408b-86b7-a3853172b2ab",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14b77010-9bd5-4ea8-aeb5-b374d90ecde9",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaad733a-a9c6-4ac6-9181-edcb3fa83e67",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## 2019 Points\n",
    "***\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9190e7c4-7703-491c-a946-6d2a576f8ec2",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28f0f3c9-86cc-49ad-875c-3c8d2dbcdbca",
   "metadata": {},
   "source": [
    "### Level 8 Courses\n",
    "https://www.cao.ie/index.php?page=points&p=2019"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e69f8c48-b27c-47c7-8e2e-85473a8c3483",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6697ee42-9880-4bea-9650-63ec4a0f1418",
   "metadata": {},
   "source": [
    "The 2019 CAO points are stored in PDF files. Only round 1 points are available for 2019."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f00ea9e4-95ff-453d-b068-1fc3f6fad36c",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf4fe6ce-4c58-4c4a-9dda-b02e438dc8c4",
   "metadata": {},
   "source": [
    "I decided to use the package `Camelot` to extract the data from the files. More information can be found in the `README` file of this projects repository and [the official documentation for Camelot can be found here](https://camelot-py.readthedocs.io/en/master/user/quickstart.html).\n",
    "\n",
    "`Camelot` has a number of [dependencies](https://camelot-py.readthedocs.io/en/master/user/install-deps.html) which must be installed before the package can be used. I found [this](https://www.youtube.com/watch?v=LoiHI-IB3lY&t=308s) video extremely useful in getting Camelot up and running."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e78193c-9efe-457c-a2f0-d59cbcffa36e",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5505736-2bdc-42c4-bd0a-e454569903c6",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4410b807-ca1d-4e66-ad6b-8d6d4edc74c1",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Getting The Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13f6240b-1cdf-4418-bd57-5ccd5dcabfac",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b6290947-5ec8-4221-b219-6ee477b386f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checking all pages of pdf for data\n",
    "pdf = camelot.read_pdf('datasets/2019_points.pdf', pages='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc9bdf69-e3c6-4ffe-8b84-43bdd9fa276a",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b6688b33-fd18-4e9a-bfb7-618278b3c71d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'camelot.core.TableList'>\n"
     ]
    }
   ],
   "source": [
    "# Checking the type\n",
    "print(type(pdf))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76bf14b0-d7d2-47db-8af3-a39641bcbe88",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c903d9b-1808-44ea-8dea-134b42a92eaf",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24510c92-34c7-4290-958f-32afccf0ea76",
   "metadata": {
    "tags": []
   },
   "source": [
    "Note: The number of pages in the 2019 level 8 courses PDF is 18."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fd995a3-fc9b-475d-be10-1b3cc20c2d23",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9349d243-3ede-47be-9f31-24b6a32e64c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TableList n=18>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of tables found\n",
    "pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cec9936-378f-4391-b84f-83c15a7cbdd0",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6406c41f-4804-4579-aa82-c655977e13e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 100.0, 'whitespace': 2.73, 'order': 1, 'page': 2}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking tothe accuracy\n",
    "pdf[1].parsing_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74d1b3e8-6e4b-4724-b701-b6d2a6aa0ce0",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29de53f0-10fe-4213-87f1-8fd9918a463a",
   "metadata": {
    "tags": []
   },
   "source": [
    "From the parsing report we can see the table was extracted correctly. You can check this with all pages."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea17836d-3eff-477d-9ce0-85ff2cfef465",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67be9f3e-d646-4b65-8781-9ffb5a9e90c8",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d4bb406-ff93-4950-bd7c-50d25afafc00",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Exporting Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "658aef01-bf3d-4bb9-a8fa-b05169429bc6",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5a8546c-5151-4382-a37d-a679588e2f69",
   "metadata": {
    "tags": []
   },
   "source": [
    "The next step is to export the data found into a CSV file so we can convert it into a dataframe. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c1c6c48-5341-49ba-9b2f-fd84d83a112e",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b2ad6ee8-336b-4e35-9afd-b57460b202ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exporting tables into a csv file\n",
    "pdf.export('datasets/2019_points.csv', f='csv', compress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35d84728-66ae-41ad-b97d-62b0f3c1f85a",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58484861-945a-4f5d-a369-9757f2c6a530",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2872bbb-4bd8-4b63-bc57-18ad97282f06",
   "metadata": {
    "tags": []
   },
   "source": [
    "To unzip the CSV files with use the `Zipfile` package [8]. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6e03c2b-6002-4fbf-813a-e73bdf8c6e5e",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "80a4bf4c-ef2d-41d4-a6aa-6da29c9f0794",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loop through zipped folder for files\n",
    "with ZipFile('datasets/2019_points.zip', 'r') as df:\n",
    "   # Extract all the contents of zip file in current directory\n",
    "   df.extractall('datasets/2019_points')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "249ddd8c-9d12-45a3-a516-b8b3377d262a",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f27194a1-2ebf-49ac-9d31-838d0394df23",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58e03b82-c878-4be4-bad2-6e1743def21c",
   "metadata": {},
   "source": [
    "### Merging CSV Files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ecfd8a3-ca50-4ee4-b513-9317924249fc",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29e95987-a88d-4386-b046-eb5d5e3a118b",
   "metadata": {
    "tags": []
   },
   "source": [
    "As `camelot` exports each table as a single file, we need to merge them all into one dataframe. We can do this by using the `Glob` module. `Glob`, which stands for global, is part of the standard Python library. \n",
    "\n",
    "It is used to <i>\"...search for a specific file pattern, or perhaps more usefully, search for files where the filename matches a certain pattern by using wildcard characters\"</i> [9]."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47928ba3-c025-4a54-8871-c72c688c5c9c",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3323cf2-4c9f-47db-818b-6a215cc9fcf1",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "afc9e0bb-fdee-4f39-be49-3915fad5cfa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The path to use\n",
    "path = 'datasets/2019_points/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eff422b-6969-4fd7-80e4-054380f2d66f",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "84be02b6-a453-4882-893a-87e0e181a276",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find csvs which match this pattern\n",
    "filelist = glob.glob(path + '2019_*.csv')\n",
    "\n",
    "# Merge them together with these column headings\n",
    "df2019 = pd.concat([pd.read_csv(file, names=['code','title','points','median' ]) for file in filelist])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33f91e8a-5be5-4274-a135-85e6131b0c58",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9cdee5a-177c-4165-8e8c-f6f84d3f3be9",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2209205-1898-4cfb-8206-59583804f2ea",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "005784e7-8557-470b-b731-650308610d37",
   "metadata": {
    "tags": []
   },
   "source": [
    "As we know these are all level 8 courses, we will also insert a new column into the dataframe to show this. We will also clean up the dataframe a bit by dropping the old heading and by getting rid of the rows which only contain the name of the third level institutes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "327cdb87-844e-449c-832a-3ae1fccac36f",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a828dc68-33b0-488d-83de-87e04b67acba",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "244978ec-2258-414a-b753-29ba7b2ad0a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a new column at the second index\n",
    "df2019.insert(2, 'level', '8')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18978a65-f797-4bf0-a78c-1d3a20f17555",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2619d2ae-1acd-4ec8-81f1-0b09889e25a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>code</th>\n",
       "      <th>title</th>\n",
       "      <th>level</th>\n",
       "      <th>points</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Athlone Institute of Technology</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL801</td>\n",
       "      <td>Software Design with Virtual Reality and Gaming</td>\n",
       "      <td>8</td>\n",
       "      <td>304</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL802</td>\n",
       "      <td>Software Design with Cloud Computing</td>\n",
       "      <td>8</td>\n",
       "      <td>301</td>\n",
       "      <td>306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL803</td>\n",
       "      <td>Software Design with Mobile Apps and Connected...</td>\n",
       "      <td>8</td>\n",
       "      <td>309</td>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AL805</td>\n",
       "      <td>Network Management and Cloud Infrastructure</td>\n",
       "      <td>8</td>\n",
       "      <td>329</td>\n",
       "      <td>442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>TR032</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>8</td>\n",
       "      <td>487*</td>\n",
       "      <td>520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>TR033</td>\n",
       "      <td>Computer Science</td>\n",
       "      <td>8</td>\n",
       "      <td>465*</td>\n",
       "      <td>488.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>TR034</td>\n",
       "      <td>Management Science and Information Systems Stu...</td>\n",
       "      <td>8</td>\n",
       "      <td>589*</td>\n",
       "      <td>602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>TR035</td>\n",
       "      <td>Theoretical Physics</td>\n",
       "      <td>8</td>\n",
       "      <td>554</td>\n",
       "      <td>601.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>TR038</td>\n",
       "      <td>Engineering with Management</td>\n",
       "      <td>8</td>\n",
       "      <td>499</td>\n",
       "      <td>543.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>948 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     code                                              title level points  \\\n",
       "1     NaN                    Athlone Institute of Technology     8    NaN   \n",
       "2   AL801    Software Design with Virtual Reality and Gaming     8    304   \n",
       "3   AL802               Software Design with Cloud Computing     8    301   \n",
       "4   AL803  Software Design with Mobile Apps and Connected...     8    309   \n",
       "5   AL805        Network Management and Cloud Infrastructure     8    329   \n",
       "..    ...                                                ...   ...    ...   \n",
       "50  TR032                                        Engineering     8   487*   \n",
       "51  TR033                                   Computer Science     8   465*   \n",
       "52  TR034  Management Science and Information Systems Stu...     8   589*   \n",
       "53  TR035                                Theoretical Physics     8    554   \n",
       "54  TR038                        Engineering with Management     8    499   \n",
       "\n",
       "   median  \n",
       "1     NaN  \n",
       "2     328  \n",
       "3     306  \n",
       "4     337  \n",
       "5     442  \n",
       "..    ...  \n",
       "50  520.0  \n",
       "51  488.0  \n",
       "52  602.0  \n",
       "53  601.0  \n",
       "54  543.0  \n",
       "\n",
       "[948 rows x 5 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping old heading\n",
    "df2019.drop(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b51b7525-e0bb-466c-b86c-bcf2728af00f",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3cb4c584-ac09-4a1b-bff5-a1ce2dece574",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting rid of rows containing institute names only\n",
    "df2019 = df2019[df2019['code'].notnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16249be5-d1ce-4b51-a155-6f05ca8bf4d7",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ba70f573-6af3-4f58-b04a-4aecd7343448",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>50</th>\n",
       "      <td>TR032</td>\n",
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       "      <td>8</td>\n",
       "      <td>487*</td>\n",
       "      <td>520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>TR033</td>\n",
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       "      <td>8</td>\n",
       "      <td>465*</td>\n",
       "      <td>488.0</td>\n",
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       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>TR034</td>\n",
       "      <td>Management Science and Information Systems Stu...</td>\n",
       "      <td>8</td>\n",
       "      <td>589*</td>\n",
       "      <td>602.0</td>\n",
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       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>TR035</td>\n",
       "      <td>Theoretical Physics</td>\n",
       "      <td>8</td>\n",
       "      <td>554</td>\n",
       "      <td>601.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>TR038</td>\n",
       "      <td>Engineering with Management</td>\n",
       "      <td>8</td>\n",
       "      <td>499</td>\n",
       "      <td>543.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     code                                              title level points  \\\n",
       "50  TR032                                        Engineering     8   487*   \n",
       "51  TR033                                   Computer Science     8   465*   \n",
       "52  TR034  Management Science and Information Systems Stu...     8   589*   \n",
       "53  TR035                                Theoretical Physics     8    554   \n",
       "54  TR038                        Engineering with Management     8    499   \n",
       "\n",
       "   median  \n",
       "50  520.0  \n",
       "51  488.0  \n",
       "52  602.0  \n",
       "53  601.0  \n",
       "54  543.0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking last 5 rows\n",
    "df2019.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8260ae18-9577-49b1-abcd-3038d2d11829",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd42ad6f-600e-450a-9fce-882325e9ca51",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4bd875b-76e6-4b1a-a260-81fba6e48e4a",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed0812d2-50c2-47c3-b1d6-f80a086e4bb4",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d75975ea-bf69-4cb0-9f2a-216655885581",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "959c3229-da80-4b0a-9fcb-f9c464654de0",
   "metadata": {
    "tags": []
   },
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae9cae1d-97a9-4991-958f-a35d8dbbcab0",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a382a48d-7eae-4d8b-b464-f6b0bd3a423e",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Level 6 & 7 Courses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc54a4b0-1e40-4cfc-8768-3778d8388ece",
   "metadata": {
    "tags": []
   },
   "source": [
    "http://www2.cao.ie/points/lvl76_19.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ee90755-980d-43a6-b8cf-d161b88aa540",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a325a13-625a-4a21-95d1-2e7b7dc253bb",
   "metadata": {
    "tags": []
   },
   "source": [
    "To retrieve the 2019 level 6 and 7 courses we repeat this process."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68be944e-a976-49c1-ad65-6570103cbf65",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "055059e0-5559-4b26-ae15-2e52ee255ace",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Getting The Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ea1d38c-8331-428b-84fa-7c17c76cbd3c",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53fd8c61-ecb6-4ff0-8b9a-07c30df4c98c",
   "metadata": {
    "tags": []
   },
   "source": [
    "There are 10 pages in the level 6 and 7 courses PDF."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fb794ed-87a1-4be2-98b4-de0b87705cfa",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "30b0352a-9300-4784-a1f5-7734d65efb50",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checking all pages of pdf for data\n",
    "pdf_2 = camelot.read_pdf('datasets/2019_points_2.pdf', pages='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dc2e623-380c-4d7f-ac04-b04c6fd5e55c",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2b96527d-dfb4-46d4-85ed-c8c571e3082f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TableList n=10>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of tables, should be 10\n",
    "pdf_2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d62af47-be9a-4689-a510-9ed4c1adeac2",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b90975f2-f2c5-4bf3-b3aa-d4feb2561566",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5ea24a78-755f-4705-96fc-fd4a595adeee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 100.0, 'whitespace': 9.26, 'order': 1, 'page': 4}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking to make sure it worked\n",
    "pdf_2[3].parsing_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f7db0fb-e1a6-4bbb-b023-89ee903761a1",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbf49701-77b5-4a26-bdea-846b391fe5d1",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cac03a30-b85b-4942-b17e-3403af15f606",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deb900e6-996b-405a-80f8-538b9164e4eb",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Exporting The Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d1cccaa-99a7-4918-a1a9-6ca76e089767",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1c88dc7-0c9b-4696-b99f-dce406b0fd25",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ae34aae1-fef4-4b97-8b22-81a5d8e70597",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exporting tables into a csv file\n",
    "pdf_2.export('datasets/2019_points_2.csv', f='csv', compress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "515c6ba6-ccf7-48d6-a85d-9956e615497c",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fada7eb3-3f2e-4135-9dfa-0d9337d970f7",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "00ae28cc-8991-44ae-b025-c2d634e3d6f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unzipping the folder - multiple tables are exported as a zip\n",
    "# Loop through zipped folder for files\n",
    "with ZipFile('datasets/2019_points_2.zip', 'r') as df:\n",
    "   # Extract all the contents of zip file in current directory\n",
    "   df.extractall('datasets/2019_points_2')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f41928e-f3d2-4461-ab42-9dde3092fa45",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5c79474-4efe-4c53-9f1c-d5e01e0b2899",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09c5b4c6-8072-456e-a474-b7af76ec3524",
   "metadata": {},
   "source": [
    "### Merging CSV Files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74a55e87-56c6-416a-94ac-a6e2cc718504",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1d349b61-b086-4b9f-97ac-51950d7d44bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a path\n",
    "path = 'datasets/2019_points_2/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c29cf108-674e-4599-be78-865488462994",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f776d555-a480-4d5f-a558-83f9cb3e0d21",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "144859d4-5442-4c50-8038-bf44d2b5cc8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find csvs which match this pattern\n",
    "filelist = glob.glob(path + '2019_*.csv')\n",
    "\n",
    "# Merge them together with these column headings. Leaving blurb from pdf without skiprows\n",
    "df2019_2 = pd.concat([pd.read_csv(file, names=['code','title','points','median'], skiprows=10) for file in filelist])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "340beab7-1284-45ae-9711-3f8031f51c7b",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ae4ae4f-2bb4-4b62-93dc-ac95a32c1fd0",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b3abf8b-7209-42ea-8ffa-6d7d76097a00",
   "metadata": {
    "tags": []
   },
   "source": [
    "Unfortunately, the 2019 level 6 and 7 courses aren't distinguished from each other. In tandem with 2021, I decided to insert them as '6/7' under a new column heading 'level'. This will distinguish them from the courses we know for certain are level 8. Additionally, we will get rid of the rows which only contain the name of the third level institutes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38c4bf16-a076-4be6-b560-056d5de98787",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46d5274f-6dc8-48b0-b9b9-fddf79f2cfec",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7dba18ef-17ce-4b06-9dc3-de0cb69d8e6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a new column at the second index\n",
    "df2019_2.insert(2, 'level', '6/7')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eed9c85-00ed-44a7-9c91-5a39eadea9d0",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9847cb4e-3fcd-439d-a852-215b91e09835",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting rid of rows containing institute names only\n",
    "df2019_2 = df2019_2[df2019_2['code'].notnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c90131b7-0cb8-4a3f-a3b4-d649315568f1",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "aa6e2b8d-4a92-49ee-b8de-9b37510315cd",
   "metadata": {},
   "outputs": [
    {
     "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>code</th>\n",
       "      <th>title</th>\n",
       "      <th>level</th>\n",
       "      <th>points</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL600</td>\n",
       "      <td>Software Design</td>\n",
       "      <td>6/7</td>\n",
       "      <td>205</td>\n",
       "      <td>306.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL601</td>\n",
       "      <td>Computer Engineering</td>\n",
       "      <td>6/7</td>\n",
       "      <td>196</td>\n",
       "      <td>272.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL602</td>\n",
       "      <td>Mechanical Engineering</td>\n",
       "      <td>6/7</td>\n",
       "      <td>258</td>\n",
       "      <td>424.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL604</td>\n",
       "      <td>Civil Engineering</td>\n",
       "      <td>6/7</td>\n",
       "      <td>252</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL630</td>\n",
       "      <td>Pharmacy Technician</td>\n",
       "      <td>6/7</td>\n",
       "      <td>306</td>\n",
       "      <td>366.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    code                   title level points  median\n",
       "0  AL600         Software Design   6/7    205   306.0\n",
       "1  AL601    Computer Engineering   6/7    196   272.0\n",
       "2  AL602  Mechanical Engineering   6/7    258   424.0\n",
       "3  AL604       Civil Engineering   6/7    252   360.0\n",
       "4  AL630     Pharmacy Technician   6/7    306   366.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking pandas dataframe\n",
    "df2019_2. head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40d5e7e3-02f0-4036-b0da-f1da62a92580",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f19caf5-5a06-4f2d-b7f2-0c16f1d7a0cc",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecca7434-813d-4798-98d3-8dc3f132f230",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05da86c2-debc-4a85-b875-82d221042467",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79bc792c-9f11-400f-bce8-9705e712c44f",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bff566f-4040-4dc3-897c-68dd0a9b2a87",
   "metadata": {},
   "source": [
    "***\n",
    "## Cleaning Up Dataframes & Data \n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6782286-3f8a-469b-b21b-0d7741e2f6b0",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acb3aa50-c0ef-4fd2-868c-3b3f47e943d4",
   "metadata": {
    "tags": []
   },
   "source": [
    "Let's clean up our dataframes by choosing the columns we will be using, merging the level 6, 7 and 8 level courses for 2019 and 2021 together and any duplicate values.'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ce2da40-65b9-42a2-8dfe-a889de23d590",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9e4ce20-e386-4f62-af05-077a05a0685d",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 2021"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b56ec2d-3b29-4750-825e-b33ad94edca3",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "60eb8732-1e74-4c0a-b42c-50df2065b742",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting columns level 8\n",
    "level_8_2021 = df2021[['code', 'title', 'level',  'pointsR1', 'pointsR2']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03cfad87-7bff-41ee-bfa5-9517bf4e0b17",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1f6ee91c-3c8f-4652-ba79-4623ad185e82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting columns level 6/7\n",
    "level_6_2021 = df2021_2[['code', 'title', 'level',  'pointsR1', 'pointsR2']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3672a01-6f87-41de-9159-0624e4f38661",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1c99463-29fe-46ef-8821-99a29ad39d17",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a20ecdc2-246c-46e8-9258-ddeda935d1dd",
   "metadata": {},
   "source": [
    "`Pandas` makes it super simple for us to combine our dataframes by performing concatenation. The `.concat()` method allows us <i>\"...to append either columns or rows from one DataFrame to another\"</i> [10]."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d33fbf99-6f31-48f2-9a51-bfcf7fddadae",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cdd76d1-ec09-4161-b505-6500e4e01e64",
   "metadata": {},
   "source": [
    "![img](images/concat.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1731ad91-48c2-41b3-bb00-0e62cf5d153a",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1ef5b59-b66c-4b15-a8eb-f776dce546ae",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d186a1af-013c-499f-85d6-25dad2d41590",
   "metadata": {},
   "outputs": [
    {
     "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>code</th>\n",
       "      <th>title</th>\n",
       "      <th>level</th>\n",
       "      <th>pointsR1</th>\n",
       "      <th>pointsR2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL801</td>\n",
       "      <td>Software Design for Virtual Reality and Gaming</td>\n",
       "      <td>8</td>\n",
       "      <td>300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL802</td>\n",
       "      <td>Software Design in Artificial Intelligence for...</td>\n",
       "      <td>8</td>\n",
       "      <td>313</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL803</td>\n",
       "      <td>Software Design for Mobile Apps and Connected ...</td>\n",
       "      <td>8</td>\n",
       "      <td>350</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL805</td>\n",
       "      <td>Computer Engineering for Network Infrastructure</td>\n",
       "      <td>8</td>\n",
       "      <td>321</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL810</td>\n",
       "      <td>Quantity Surveying</td>\n",
       "      <td>8</td>\n",
       "      <td>328</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1360</th>\n",
       "      <td>WD188</td>\n",
       "      <td>Applied Health Care</td>\n",
       "      <td>6/7</td>\n",
       "      <td>220</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1361</th>\n",
       "      <td>WD205</td>\n",
       "      <td>Molecular Biology with Biopharmaceutical Science</td>\n",
       "      <td>6/7</td>\n",
       "      <td>AQA</td>\n",
       "      <td>262v</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1362</th>\n",
       "      <td>WD206</td>\n",
       "      <td>Electronic Engineering</td>\n",
       "      <td>6/7</td>\n",
       "      <td>180</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1363</th>\n",
       "      <td>WD207</td>\n",
       "      <td>Mechanical Engineering</td>\n",
       "      <td>6/7</td>\n",
       "      <td>172</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1364</th>\n",
       "      <td>WD208</td>\n",
       "      <td>Manufacturing Engineering</td>\n",
       "      <td>6/7</td>\n",
       "      <td>317</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1365 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       code                                              title level pointsR1  \\\n",
       "0     AL801     Software Design for Virtual Reality and Gaming     8      300   \n",
       "1     AL802  Software Design in Artificial Intelligence for...     8      313   \n",
       "2     AL803  Software Design for Mobile Apps and Connected ...     8      350   \n",
       "3     AL805    Computer Engineering for Network Infrastructure     8      321   \n",
       "4     AL810                                 Quantity Surveying     8      328   \n",
       "...     ...                                                ...   ...      ...   \n",
       "1360  WD188                                Applied Health Care   6/7      220   \n",
       "1361  WD205   Molecular Biology with Biopharmaceutical Science   6/7      AQA   \n",
       "1362  WD206                             Electronic Engineering   6/7      180   \n",
       "1363  WD207                             Mechanical Engineering   6/7      172   \n",
       "1364  WD208                          Manufacturing Engineering   6/7      317   \n",
       "\n",
       "     pointsR2  \n",
       "0         NaN  \n",
       "1         NaN  \n",
       "2         NaN  \n",
       "3         NaN  \n",
       "4         NaN  \n",
       "...       ...  \n",
       "1360      NaN  \n",
       "1361     262v  \n",
       "1362      NaN  \n",
       "1363      NaN  \n",
       "1364      NaN  \n",
       "\n",
       "[1365 rows x 5 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenating level 8 and 6/7 2021 courses\n",
    "total2021 = pd.concat([level_8_2021, level_6_2021], ignore_index=True)\n",
    "total2021"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "697cdd25-c5af-41a1-abeb-ff1f776d5c3d",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ed9157c-6881-4ae5-a533-db76be35e835",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25112745-8e20-450f-882b-439496b533b9",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85fd5608-4e2f-4707-a0a6-ca1b71aca24e",
   "metadata": {},
   "source": [
    "### 2020"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9945a09f-263c-4eeb-8ac2-e739ec517fb6",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "072a296c-2a5c-4eac-b289-1963170be5de",
   "metadata": {},
   "source": [
    "As all courses are inculded in the one file for 2020 points we don't need to combine them. We will however create a new dataframe by choosing the column titles we will be using."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5077656-a206-4362-a63e-c1b1e7209ca2",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a1076ed4-0a05-438d-b50c-5f4e705cb347",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting coloumns\n",
    "total2020 = df2020[['COURSE CODE2','COURSE TITLE', 'LEVEL']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19b80b11-395d-4f21-bf68-f405ab0594b9",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3c5705a5-c59e-4b71-a6ce-2379d212af95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming columns\n",
    "total2020.columns = ['code', 'title', 'level']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81262f07-27f1-47d5-a4c8-776251cb7f67",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b0f57f5c-ef78-4ad2-883d-8f21711bfbb6",
   "metadata": {},
   "outputs": [
    {
     "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>code</th>\n",
       "      <th>title</th>\n",
       "      <th>level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AC120</td>\n",
       "      <td>International Business</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AC137</td>\n",
       "      <td>Liberal Arts</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AD101</td>\n",
       "      <td>First Year Art &amp; Design (Common Entry,portfolio)</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AD102</td>\n",
       "      <td>Graphic Design and Moving Image Design (portfo...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AD103</td>\n",
       "      <td>Textile &amp; Surface Design and Jewellery &amp; Objec...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>WD208</td>\n",
       "      <td>Manufacturing Engineering</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>WD210</td>\n",
       "      <td>Software Systems Development</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>WD211</td>\n",
       "      <td>Creative Computing</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1462</th>\n",
       "      <td>WD212</td>\n",
       "      <td>Recreation and Sport Management</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1463</th>\n",
       "      <td>WD230</td>\n",
       "      <td>Mechanical and Manufacturing Engineering</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1464 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       code                                              title  level\n",
       "0     AC120                             International Business      8\n",
       "1     AC137                                       Liberal Arts      8\n",
       "2     AD101   First Year Art & Design (Common Entry,portfolio)      8\n",
       "3     AD102  Graphic Design and Moving Image Design (portfo...      8\n",
       "4     AD103  Textile & Surface Design and Jewellery & Objec...      8\n",
       "...     ...                                                ...    ...\n",
       "1459  WD208                          Manufacturing Engineering      7\n",
       "1460  WD210                       Software Systems Development      8\n",
       "1461  WD211                                 Creative Computing      8\n",
       "1462  WD212                    Recreation and Sport Management      8\n",
       "1463  WD230           Mechanical and Manufacturing Engineering      8\n",
       "\n",
       "[1464 rows x 3 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total2020"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0a39eed-bd5a-4e4d-af3d-b9952187ebf8",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cc9f153-2686-4b7d-b3ae-0bdd278a3958",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e516cc8e-351f-4534-8cc3-d5e6e0d0fd76",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25116ad5-adb5-4224-b99c-d948aa11adb6",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 2019"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3722891-0b7e-4081-84d7-f2b0e7d5755c",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "bbdcc35c-f126-4511-a89e-1dbdc80ba256",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting columns level 8\n",
    "level_8_2019 = df2019[['code', 'title', 'level', 'points']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95989399-cd6c-47de-afec-bfd591c8b4cd",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "074633d1-2d1c-4d1b-a538-b16767d3c15b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting columns level 6/7\n",
    "level_6_2019 = df2019_2[['code', 'title', 'level', 'points']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72c3d4e7-1efc-4834-8e11-c40445525fe2",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "38ad5005-f7ac-40ff-82e1-bd6aeacb4f98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>code</th>\n",
       "      <th>title</th>\n",
       "      <th>level</th>\n",
       "      <th>points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Course Code INSTITUTION and COURSE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>EOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL801</td>\n",
       "      <td>Software Design with Virtual Reality and Gaming</td>\n",
       "      <td>8</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL802</td>\n",
       "      <td>Software Design with Cloud Computing</td>\n",
       "      <td>8</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL803</td>\n",
       "      <td>Software Design with Mobile Apps and Connected...</td>\n",
       "      <td>8</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL805</td>\n",
       "      <td>Network Management and Cloud Infrastructure</td>\n",
       "      <td>8</td>\n",
       "      <td>329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>WD173</td>\n",
       "      <td>Hospitality Studies</td>\n",
       "      <td>6/7</td>\n",
       "      <td>182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>WD174</td>\n",
       "      <td>Tourism</td>\n",
       "      <td>6/7</td>\n",
       "      <td>134.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>WD175</td>\n",
       "      <td>Pharmaceutical Science</td>\n",
       "      <td>6/7</td>\n",
       "      <td>207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>WD177</td>\n",
       "      <td>Science (Mol. Biology with Biopharm.,Food Scie...</td>\n",
       "      <td>6/7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>WD182</td>\n",
       "      <td>Electrical Engineering</td>\n",
       "      <td>6/7</td>\n",
       "      <td>188.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    code  \\\n",
       "0     Course Code INSTITUTION and COURSE   \n",
       "1                                  AL801   \n",
       "2                                  AL802   \n",
       "3                                  AL803   \n",
       "4                                  AL805   \n",
       "...                                  ...   \n",
       "1304                               WD173   \n",
       "1305                               WD174   \n",
       "1306                               WD175   \n",
       "1307                               WD177   \n",
       "1308                               WD182   \n",
       "\n",
       "                                                  title level points  \n",
       "0                                                   NaN     8    EOS  \n",
       "1       Software Design with Virtual Reality and Gaming     8    304  \n",
       "2                  Software Design with Cloud Computing     8    301  \n",
       "3     Software Design with Mobile Apps and Connected...     8    309  \n",
       "4           Network Management and Cloud Infrastructure     8    329  \n",
       "...                                                 ...   ...    ...  \n",
       "1304                                Hospitality Studies   6/7  182.0  \n",
       "1305                                            Tourism   6/7  134.0  \n",
       "1306                             Pharmaceutical Science   6/7  207.0  \n",
       "1307  Science (Mol. Biology with Biopharm.,Food Scie...   6/7    NaN  \n",
       "1308                             Electrical Engineering   6/7  188.0  \n",
       "\n",
       "[1309 rows x 4 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenating level 8 and 6/7 2021 courses\n",
    "total2019= pd.concat([level_8_2019, level_6_2019], ignore_index=True)\n",
    "total2019"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b353a614-4bfe-4295-9680-780cabaa6a61",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b03aac71-895e-4406-92c8-bf0bf265e1a8",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73479988-9abd-4cf0-9163-df81994f0702",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51c9bc33-94ee-4c6f-8295-c3c607289a53",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9489531b-ef14-45a5-8a9e-d08128e2f6fa",
   "metadata": {
    "tags": []
   },
   "source": [
    "### All Courses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b2e012c-8006-42c2-ab91-c32d8666ad42",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acaec4c7-b931-41b2-8d43-212b1e9a5e39",
   "metadata": {
    "tags": []
   },
   "source": [
    "Now that we have three smaller dataframes for each year, it's time to combine the code, title and level for each year into one. This will enable us to join the points for each year later on."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9639bda-4b8f-49bd-8ee2-cfc426ced046",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "01b7ecc1-6680-430e-b01c-1a419e844743",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>title</th>\n",
       "      <th>level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL801</td>\n",
       "      <td>Software Design for Virtual Reality and Gaming</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL802</td>\n",
       "      <td>Software Design in Artificial Intelligence for...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL803</td>\n",
       "      <td>Software Design for Mobile Apps and Connected ...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL805</td>\n",
       "      <td>Computer Engineering for Network Infrastructure</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL810</td>\n",
       "      <td>Quantity Surveying</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1360</th>\n",
       "      <td>WD188</td>\n",
       "      <td>Applied Health Care</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1361</th>\n",
       "      <td>WD205</td>\n",
       "      <td>Molecular Biology with Biopharmaceutical Science</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1362</th>\n",
       "      <td>WD206</td>\n",
       "      <td>Electronic Engineering</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1363</th>\n",
       "      <td>WD207</td>\n",
       "      <td>Mechanical Engineering</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1364</th>\n",
       "      <td>WD208</td>\n",
       "      <td>Manufacturing Engineering</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1365 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       code                                              title level\n",
       "0     AL801     Software Design for Virtual Reality and Gaming     8\n",
       "1     AL802  Software Design in Artificial Intelligence for...     8\n",
       "2     AL803  Software Design for Mobile Apps and Connected ...     8\n",
       "3     AL805    Computer Engineering for Network Infrastructure     8\n",
       "4     AL810                                 Quantity Surveying     8\n",
       "...     ...                                                ...   ...\n",
       "1360  WD188                                Applied Health Care   6/7\n",
       "1361  WD205   Molecular Biology with Biopharmaceutical Science   6/7\n",
       "1362  WD206                             Electronic Engineering   6/7\n",
       "1363  WD207                             Mechanical Engineering   6/7\n",
       "1364  WD208                          Manufacturing Engineering   6/7\n",
       "\n",
       "[1365 rows x 3 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choosing columns\n",
    "courses2021 = total2021[['code', 'title', 'level']]\n",
    "courses2021"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c62d001a-7b13-4850-8f3a-809cf4e251b6",
   "metadata": {
    "tags": []
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   "source": [
    "<br>"
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   "id": "f0a470c9-8e35-4785-89fa-741280907a2e",
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    "<br>"
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   "id": "e450d04c-1669-406b-9f99-76c1607fadfb",
   "metadata": {
    "tags": []
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   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9270fdd1-8e6f-426d-afce-e1f71db97bad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AC120</td>\n",
       "      <td>International Business</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AC137</td>\n",
       "      <td>Liberal Arts</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AD101</td>\n",
       "      <td>First Year Art &amp; Design (Common Entry,portfolio)</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AD102</td>\n",
       "      <td>Graphic Design and Moving Image Design (portfo...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AD103</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>WD208</td>\n",
       "      <td>Manufacturing Engineering</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>WD210</td>\n",
       "      <td>Software Systems Development</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>WD211</td>\n",
       "      <td>Creative Computing</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1462</th>\n",
       "      <td>WD212</td>\n",
       "      <td>Recreation and Sport Management</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1463</th>\n",
       "      <td>WD230</td>\n",
       "      <td>Mechanical and Manufacturing Engineering</td>\n",
       "      <td>8</td>\n",
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       "  </tbody>\n",
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       "<p>1464 rows × 3 columns</p>\n",
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      "text/plain": [
       "       code                                              title  level2020\n",
       "0     AC120                             International Business          8\n",
       "1     AC137                                       Liberal Arts          8\n",
       "2     AD101   First Year Art & Design (Common Entry,portfolio)          8\n",
       "3     AD102  Graphic Design and Moving Image Design (portfo...          8\n",
       "4     AD103  Textile & Surface Design and Jewellery & Objec...          8\n",
       "...     ...                                                ...        ...\n",
       "1459  WD208                          Manufacturing Engineering          7\n",
       "1460  WD210                       Software Systems Development          8\n",
       "1461  WD211                                 Creative Computing          8\n",
       "1462  WD212                    Recreation and Sport Management          8\n",
       "1463  WD230           Mechanical and Manufacturing Engineering          8\n",
       "\n",
       "[1464 rows x 3 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choosing column\n",
    "courses2020 = df2020[['COURSE CODE2','COURSE TITLE', 'LEVEL']]\n",
    "\n",
    "# Renaming columns\n",
    "courses2020.columns = ['code', 'title', 'level2020']\n",
    "\n",
    "courses2020"
   ]
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   "id": "61ac3f79-4a0f-4e8a-898d-434d3e89e228",
   "metadata": {
    "tags": []
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   "source": [
    "<br>"
   ]
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  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "cef3d520-98ee-489b-9aa6-cde7937272ed",
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
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       "      <td>AL801</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Hospitality Studies</td>\n",
       "      <td>6/7</td>\n",
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       "    <tr>\n",
       "      <th>1305</th>\n",
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       "      <td>6/7</td>\n",
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       "    <tr>\n",
       "      <th>1306</th>\n",
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       "      <td>Pharmaceutical Science</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>WD177</td>\n",
       "      <td>Science (Mol. Biology with Biopharm.,Food Scie...</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>WD182</td>\n",
       "      <td>Electrical Engineering</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    code  \\\n",
       "0     Course Code INSTITUTION and COURSE   \n",
       "1                                  AL801   \n",
       "2                                  AL802   \n",
       "3                                  AL803   \n",
       "4                                  AL805   \n",
       "...                                  ...   \n",
       "1304                               WD173   \n",
       "1305                               WD174   \n",
       "1306                               WD175   \n",
       "1307                               WD177   \n",
       "1308                               WD182   \n",
       "\n",
       "                                                  title level2019  \n",
       "0                                                   NaN         8  \n",
       "1       Software Design with Virtual Reality and Gaming         8  \n",
       "2                  Software Design with Cloud Computing         8  \n",
       "3     Software Design with Mobile Apps and Connected...         8  \n",
       "4           Network Management and Cloud Infrastructure         8  \n",
       "...                                                 ...       ...  \n",
       "1304                                Hospitality Studies       6/7  \n",
       "1305                                            Tourism       6/7  \n",
       "1306                             Pharmaceutical Science       6/7  \n",
       "1307  Science (Mol. Biology with Biopharm.,Food Scie...       6/7  \n",
       "1308                             Electrical Engineering       6/7  \n",
       "\n",
       "[1309 rows x 3 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choosing columns\n",
    "courses2019 = total2019[['code', 'title', 'level']]\n",
    "\n",
    "# Renaming columns\n",
    "courses2019.columns = ['code', 'title', 'level2019']\n",
    "\n",
    "courses2019"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b554c0-d347-45bf-80b1-5dfedcf59257",
   "metadata": {
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    "<br>"
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   "execution_count": 66,
   "id": "210ee548-4bb2-49ee-8612-0d1d3c060cc3",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>6/7</td>\n",
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       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>WD175</td>\n",
       "      <td>Pharmaceutical Science</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>WD177</td>\n",
       "      <td>Science (Mol. Biology with Biopharm.,Food Scie...</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>WD182</td>\n",
       "      <td>Electrical Engineering</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1308 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       code                                              title level2019\n",
       "1     AL801    Software Design with Virtual Reality and Gaming         8\n",
       "2     AL802               Software Design with Cloud Computing         8\n",
       "3     AL803  Software Design with Mobile Apps and Connected...         8\n",
       "4     AL805        Network Management and Cloud Infrastructure         8\n",
       "5     AL810                                 Quantity Surveying         8\n",
       "...     ...                                                ...       ...\n",
       "1304  WD173                                Hospitality Studies       6/7\n",
       "1305  WD174                                            Tourism       6/7\n",
       "1306  WD175                             Pharmaceutical Science       6/7\n",
       "1307  WD177  Science (Mol. Biology with Biopharm.,Food Scie...       6/7\n",
       "1308  WD182                             Electrical Engineering       6/7\n",
       "\n",
       "[1308 rows x 3 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping old header row\n",
    "courses2019.drop(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf6d0a7f-363f-485c-8964-566b86b1dfb2",
   "metadata": {
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   "id": "8589c812-bc6d-42b7-9e40-b76a470ed0b5",
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   "id": "019ad6cd-b269-4d56-b781-482e57b2c47c",
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  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b167a280-8e10-4fe7-9fd2-04bb7cac6c21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combining the above into one\n",
    "allcourses = pd.concat([courses2021, courses2020, courses2019], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29a1597b-ac78-4f68-b0a8-bc43a49edfa6",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1d17e5aa-cad6-4465-a6c4-e4afd84e9183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>title</th>\n",
       "      <th>level</th>\n",
       "      <th>level2020</th>\n",
       "      <th>level2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL801</td>\n",
       "      <td>Software Design for Virtual Reality and Gaming</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>AL802</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>AL803</td>\n",
       "      <td>Software Design for Mobile Apps and Connected ...</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL805</td>\n",
       "      <td>Computer Engineering for Network Infrastructure</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL810</td>\n",
       "      <td>Quantity Surveying</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4133</th>\n",
       "      <td>WD173</td>\n",
       "      <td>Hospitality Studies</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4134</th>\n",
       "      <td>WD174</td>\n",
       "      <td>Tourism</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4135</th>\n",
       "      <td>WD175</td>\n",
       "      <td>Pharmaceutical Science</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4136</th>\n",
       "      <td>WD177</td>\n",
       "      <td>Science (Mol. Biology with Biopharm.,Food Scie...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4137</th>\n",
       "      <td>WD182</td>\n",
       "      <td>Electrical Engineering</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4138 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       code                                              title level  \\\n",
       "0     AL801     Software Design for Virtual Reality and Gaming     8   \n",
       "1     AL802  Software Design in Artificial Intelligence for...     8   \n",
       "2     AL803  Software Design for Mobile Apps and Connected ...     8   \n",
       "3     AL805    Computer Engineering for Network Infrastructure     8   \n",
       "4     AL810                                 Quantity Surveying     8   \n",
       "...     ...                                                ...   ...   \n",
       "4133  WD173                                Hospitality Studies   NaN   \n",
       "4134  WD174                                            Tourism   NaN   \n",
       "4135  WD175                             Pharmaceutical Science   NaN   \n",
       "4136  WD177  Science (Mol. Biology with Biopharm.,Food Scie...   NaN   \n",
       "4137  WD182                             Electrical Engineering   NaN   \n",
       "\n",
       "      level2020 level2019  \n",
       "0           NaN       NaN  \n",
       "1           NaN       NaN  \n",
       "2           NaN       NaN  \n",
       "3           NaN       NaN  \n",
       "4           NaN       NaN  \n",
       "...         ...       ...  \n",
       "4133        NaN       6/7  \n",
       "4134        NaN       6/7  \n",
       "4135        NaN       6/7  \n",
       "4136        NaN       6/7  \n",
       "4137        NaN       6/7  \n",
       "\n",
       "[4138 rows x 5 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorting values in ascending by the code column\n",
    "allcourses.sort_values('code')\n",
    "\n",
    "allcourses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4facae13-c617-4fb9-aa97-ca5238383320",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2028e4a7-893b-439f-9b1f-e0299f11350b",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c18bfba6-563d-464e-9bcd-dc27865c9dcb",
   "metadata": {},
   "source": [
    "As we can see above there are duplicate rows i.e., courses which have ran in multiple years, so they appear more than once. Let's get rid of these duplicates."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2c11b56-481d-4da5-861d-289cc2a43912",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3237e483-2c9a-4b81-b6cd-34fc45ad3417",
   "metadata": {},
   "outputs": [
    {
     "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>title</th>\n",
       "      <th>level</th>\n",
       "      <th>level2020</th>\n",
       "      <th>level2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [code, title, level, level2020, level2019]\n",
       "Index: []"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding duplicate rows\n",
    "allcourses[allcourses.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa5d4069-7fee-44e3-9701-d1fa420a1976",
   "metadata": {
    "tags": []
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   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "1cf53099-e5e9-4664-ac2c-58915440f7f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>AL802</td>\n",
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       "      <td>8</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL803</td>\n",
       "      <td>Software Design for Mobile Apps and Connected ...</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>AL805</td>\n",
       "      <td>Computer Engineering for Network Infrastructure</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL810</td>\n",
       "      <td>Quantity Surveying</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4133</th>\n",
       "      <td>WD173</td>\n",
       "      <td>Hospitality Studies</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4134</th>\n",
       "      <td>WD174</td>\n",
       "      <td>Tourism</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4135</th>\n",
       "      <td>WD175</td>\n",
       "      <td>Pharmaceutical Science</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4136</th>\n",
       "      <td>WD177</td>\n",
       "      <td>Science (Mol. Biology with Biopharm.,Food Scie...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4137</th>\n",
       "      <td>WD182</td>\n",
       "      <td>Electrical Engineering</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4138 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       code                                              title level  \\\n",
       "0     AL801     Software Design for Virtual Reality and Gaming     8   \n",
       "1     AL802  Software Design in Artificial Intelligence for...     8   \n",
       "2     AL803  Software Design for Mobile Apps and Connected ...     8   \n",
       "3     AL805    Computer Engineering for Network Infrastructure     8   \n",
       "4     AL810                                 Quantity Surveying     8   \n",
       "...     ...                                                ...   ...   \n",
       "4133  WD173                                Hospitality Studies   NaN   \n",
       "4134  WD174                                            Tourism   NaN   \n",
       "4135  WD175                             Pharmaceutical Science   NaN   \n",
       "4136  WD177  Science (Mol. Biology with Biopharm.,Food Scie...   NaN   \n",
       "4137  WD182                             Electrical Engineering   NaN   \n",
       "\n",
       "      level2020 level2019  \n",
       "0           NaN       NaN  \n",
       "1           NaN       NaN  \n",
       "2           NaN       NaN  \n",
       "3           NaN       NaN  \n",
       "4           NaN       NaN  \n",
       "...         ...       ...  \n",
       "4133        NaN       6/7  \n",
       "4134        NaN       6/7  \n",
       "4135        NaN       6/7  \n",
       "4136        NaN       6/7  \n",
       "4137        NaN       6/7  \n",
       "\n",
       "[4138 rows x 5 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping duplicates\n",
    "allcourses.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df7b5b80-f840-4b54-a0ea-53a944905408",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "9b3654e5-f864-4b75-9860-abdf544df3a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL810</td>\n",
       "      <td>Quantity Surveying</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1761</th>\n",
       "      <td>SG403</td>\n",
       "      <td>Applied Archaeology</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1762</th>\n",
       "      <td>SG432</td>\n",
       "      <td>Environmental Protection</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1763</th>\n",
       "      <td>TL712</td>\n",
       "      <td>Computing with Digital Media</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1764</th>\n",
       "      <td>TL719</td>\n",
       "      <td>Culinary Arts</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1765</th>\n",
       "      <td>TL746</td>\n",
       "      <td>Engineering in Mechatronics Engineering</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1766 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       code                                              title level  \\\n",
       "0     AL801     Software Design for Virtual Reality and Gaming     8   \n",
       "1     AL802  Software Design in Artificial Intelligence for...     8   \n",
       "2     AL803  Software Design for Mobile Apps and Connected ...     8   \n",
       "3     AL805    Computer Engineering for Network Infrastructure     8   \n",
       "4     AL810                                 Quantity Surveying     8   \n",
       "...     ...                                                ...   ...   \n",
       "1761  SG403                                Applied Archaeology   NaN   \n",
       "1762  SG432                           Environmental Protection   NaN   \n",
       "1763  TL712                       Computing with Digital Media   NaN   \n",
       "1764  TL719                                      Culinary Arts   NaN   \n",
       "1765  TL746            Engineering in Mechatronics Engineering   NaN   \n",
       "\n",
       "      level2020 level2019  \n",
       "0           NaN       NaN  \n",
       "1           NaN       NaN  \n",
       "2           NaN       NaN  \n",
       "3           NaN       NaN  \n",
       "4           NaN       NaN  \n",
       "...         ...       ...  \n",
       "1761        NaN       6/7  \n",
       "1762        NaN       6/7  \n",
       "1763        NaN       6/7  \n",
       "1764        NaN       6/7  \n",
       "1765        NaN       6/7  \n",
       "\n",
       "[1766 rows x 5 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping duplicates which shared a code\n",
    "allcourses.drop_duplicates(subset=['code'], inplace=True, ignore_index=True)\n",
    "\n",
    "allcourses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eab6990e-1d2f-4d82-a3cc-61238c1c6131",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d958a15a-9148-4ca9-bc1a-55910c4b64df",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae3cadc3-3e68-487d-9c5d-611d39483a1f",
   "metadata": {},
   "source": [
    "When `inplace = True`, data is modified in place [11]."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75d23006-f17a-4780-affb-a240f1a9dbbf",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de850894-7bd4-4acd-86b4-c29148cc4143",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2df5fd82-9ce4-49eb-b564-7cf65259fbb3",
   "metadata": {
    "tags": []
   },
   "source": [
    "***\n",
    "## Joining Points and Levels\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45fff9ef-6118-4a73-a9f5-05e27a51a927",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df2ddf43-cec4-4254-ac46-0a189342d6d8",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a82b69f2-0a74-4fe1-9b97-f29d4c7d3164",
   "metadata": {},
   "source": [
    "When a dataframe is made up of more than one dataframe we can set/change the index of this dataframe using pandas.\n",
    "\n",
    "<i>\"[An] Index is like an address, that’s how any data point across the dataframe or series can be accessed. Rows and columns both have indexes, rows indices are called as index and for columns its general column names\"</i> [12]. \n",
    "\n",
    "As the 2019 points data doesn't include round 2 points data, I've decided against using them for this analysis. We will only be using the round 1 points.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "749764c1-a4c2-4a7b-b801-1889c02e8e2c",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ee7736b-8023-47e4-a900-4c7e9ae8b705",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d1c1e4b9-e503-4f48-8287-6e3d89ebe746",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting the index as the code column\n",
    "total2021.set_index('code', inplace=True)\n",
    "\n",
    "# Column headings\n",
    "total2021.columns = ['title', 'level', 'points_r1_2021', 'points_r2_2021']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57ae9089-5846-4ff6-9763-5a0eb7fdb1f1",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "61f3d7c4-e45c-447d-abf9-0b80457f33d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting the index of allcourses as the code column\n",
    "allcourses.set_index('code', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aa14f4b-7eb6-406c-86f0-69e02fb9af3b",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c8fcb88-402b-4603-a978-6ab968e21649",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b70584a9-c493-42d9-9fb5-bba3cc074f69",
   "metadata": {},
   "source": [
    "<i>\"`join()` is an object function that lives on your DataFrame\"</i> [13]. By using .`join()`, it allows you choose which dataframe to join on. By default, pandas does a left join. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "740f2933-fc63-48c5-9de2-7050b5f70606",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c478ad7-1b0f-4422-8343-b727f2ae66c6",
   "metadata": {},
   "source": [
    "![img](images/join.webp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6eeb4bc-6c06-47a0-8d98-7cb6ea36a412",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ea604fc-87c4-4a00-8624-a5e416648822",
   "metadata": {},
   "source": [
    "The next step is to join the points from each year to our dataframe which contains the course code and title."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68c68a1b-1d56-44ec-a758-048eaf5e3df9",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "dabd1bc6-c61c-413b-947d-885ea372724b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Joining 2021 points to the dataframe \n",
    "allcourses = allcourses.join(total2021[['points_r1_2021']])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f174fb16-dda7-4cf3-8790-5f4879138ce9",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15766a7a-9d21-4fc0-a493-4dc6b412a1f2",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e661dd87-8051-4eb7-9819-c90ccaf03c4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting columns with points\n",
    "df2020_r1 = df2020[['COURSE CODE2', 'LEVEL', 'R1 POINTS', 'R2 POINTS']]\n",
    "\n",
    "# Changing titles\n",
    "df2020_r1.columns = ['code', 'level' ,'points_r1_2020', 'points_r2_2020']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0be2b16b-5d6c-420a-8db0-d9733d631a78",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1d21934-9772-4853-ba70-2cac1ed4996c",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "fb6f0858-73e3-46b8-b6a5-5f26a590cc4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting the index as the code column\n",
    "df2020_r1.set_index('code', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10d34257-8786-4868-92b6-5a603423e53f",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d883b4ae-b254-4d6e-ae40-ecccc618850c",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c4f37b55-1346-411b-b0cc-34ff8949f3d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Join 2020 points to allcourses.\n",
    "allcourses = allcourses.join(df2020_r1, rsuffix='level2020')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f73426b-49b8-4c76-aa51-127489a685bc",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b19a22e-2b4d-482c-b358-ec80dd1e37de",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "981b7df9-8335-480c-a866-00cc66f52744",
   "metadata": {},
   "source": [
    "By passing, in this case - `rsuffix` (right suffix), we're specifying <i>\"...a suffix to add to any overlapping columns\"</i> [14]. On the above join an error is thrown if the suffix isn't specified. We repeat for 2019 points."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8998835-e294-480c-af00-4be2972b742c",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2002db0d-b595-42c2-9ce4-c25cfac51843",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "71fa45f9-55d7-449f-965c-67fec4b4eb82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting columns with points\n",
    "df2019_r1 = total2019[['code', 'points', 'level']]\n",
    "\n",
    "# Changing titles\n",
    "df2019_r1.columns = ['code', 'points_r1_2019', 'level_2019']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96ccdc94-de0b-4464-888a-6b9edf22e3aa",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "de0c0697-66b5-4265-9c7d-0d7b25afbf61",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the index to the code column.\n",
    "df2019_r1.set_index('code', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94ad7dad-9fb0-4d07-965c-21a1b639b7b7",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "3f72592b-a024-4311-bba1-48205a03685a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Join 2019 points to allcourses\n",
    "allcourses = allcourses.join(df2019_r1, rsuffix='2019')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f9efdff-8589-421d-9c34-411f0f9b20a7",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "53658d12-3641-4481-a9fa-af0453081847",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping columns not using\n",
    "allcourses = allcourses.drop(['level2020', 'level2019', 'points_r2_2020'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dc90097-34c0-40dd-ad45-d27ed943283f",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "c752e2cd-a1bc-4a3f-98aa-0a40a652249e",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>level</th>\n",
       "      <th>points_r1_2021</th>\n",
       "      <th>levellevel2020</th>\n",
       "      <th>points_r1_2020</th>\n",
       "      <th>points_r1_2019</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AL801</th>\n",
       "      <td>Software Design for Virtual Reality and Gaming</td>\n",
       "      <td>8</td>\n",
       "      <td>300</td>\n",
       "      <td>8.0</td>\n",
       "      <td>303</td>\n",
       "      <td>304</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL802</th>\n",
       "      <td>Software Design in Artificial Intelligence for...</td>\n",
       "      <td>8</td>\n",
       "      <td>313</td>\n",
       "      <td>8.0</td>\n",
       "      <td>332</td>\n",
       "      <td>301</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL803</th>\n",
       "      <td>Software Design for Mobile Apps and Connected ...</td>\n",
       "      <td>8</td>\n",
       "      <td>350</td>\n",
       "      <td>8.0</td>\n",
       "      <td>337</td>\n",
       "      <td>309</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL805</th>\n",
       "      <td>Computer Engineering for Network Infrastructure</td>\n",
       "      <td>8</td>\n",
       "      <td>321</td>\n",
       "      <td>8.0</td>\n",
       "      <td>333</td>\n",
       "      <td>329</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>AL810</th>\n",
       "      <td>Quantity Surveying</td>\n",
       "      <td>8</td>\n",
       "      <td>328</td>\n",
       "      <td>8.0</td>\n",
       "      <td>319</td>\n",
       "      <td>307</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>SG403</th>\n",
       "      <td>Applied Archaeology</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>297</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG432</th>\n",
       "      <td>Environmental Protection</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>279</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TL712</th>\n",
       "      <td>Computing with Digital Media</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>243.0</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TL719</th>\n",
       "      <td>Culinary Arts</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>321.0</td>\n",
       "      <td>6/7</td>\n",
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       "    <tr>\n",
       "      <th>TL746</th>\n",
       "      <td>Engineering in Mechatronics Engineering</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>1766 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   title level points_r1_2021  \\\n",
       "code                                                                            \n",
       "AL801     Software Design for Virtual Reality and Gaming     8            300   \n",
       "AL802  Software Design in Artificial Intelligence for...     8            313   \n",
       "AL803  Software Design for Mobile Apps and Connected ...     8            350   \n",
       "AL805    Computer Engineering for Network Infrastructure     8            321   \n",
       "AL810                                 Quantity Surveying     8            328   \n",
       "...                                                  ...   ...            ...   \n",
       "SG403                                Applied Archaeology   NaN            NaN   \n",
       "SG432                           Environmental Protection   NaN            NaN   \n",
       "TL712                       Computing with Digital Media   NaN            NaN   \n",
       "TL719                                      Culinary Arts   NaN            NaN   \n",
       "TL746            Engineering in Mechatronics Engineering   NaN            NaN   \n",
       "\n",
       "       levellevel2020 points_r1_2020 points_r1_2019 level_2019  \n",
       "code                                                            \n",
       "AL801             8.0            303            304          8  \n",
       "AL802             8.0            332            301          8  \n",
       "AL803             8.0            337            309          8  \n",
       "AL805             8.0            333            329          8  \n",
       "AL810             8.0            319            307          8  \n",
       "...               ...            ...            ...        ...  \n",
       "SG403             NaN            NaN            297        6/7  \n",
       "SG432             NaN            NaN            279        6/7  \n",
       "TL712             NaN            NaN          243.0        6/7  \n",
       "TL719             NaN            NaN          321.0        6/7  \n",
       "TL746             NaN            NaN            NaN        6/7  \n",
       "\n",
       "[1766 rows x 7 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allcourses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37029db0-834b-4424-b213-38b8c7f01684",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57212aaf-ab44-404f-964f-3b6d33709cd6",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "b7d213aa-1508-4ba6-b3a2-dd6188c88b5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming the columns\n",
    "allcourses.rename(columns={'level':'level_2021', 'levellevel2020': 'level_2020'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5896b4ef-6dd2-4a64-b303-a8c804d49dbf",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a506ddba-842e-4a31-ab75-bfd67c50f16c",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "d4c9adf5-7b9c-4f49-869b-a5ecaeb9bf83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title             Counselling Skills and Psychotherapy Studies\n",
       "level_2021                                                 NaN\n",
       "points_r1_2021                                             NaN\n",
       "level_2020                                                 7.0\n",
       "points_r1_2020                                             300\n",
       "points_r1_2019                                             238\n",
       "level_2019                                                 6/7\n",
       "Name: CI001, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking random course\n",
    "allcourses.loc['CI001'] # Code is the index "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b690f1b-93e8-4e0f-a2f1-cd59fbad25ef",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
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   "id": "8eb6ee80-b7bc-4e86-aa79-b3a716c47041",
   "metadata": {},
   "source": [
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  {
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   "id": "273f57aa-7af0-4d06-a8dd-61f058a1b863",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "5dff974a-1d1c-4883-ac85-43063e1fbf8e",
   "metadata": {},
   "outputs": [
    {
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       "\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>title</th>\n",
       "      <th>level_2021</th>\n",
       "      <th>points_r1_2021</th>\n",
       "      <th>level_2020</th>\n",
       "      <th>points_r1_2020</th>\n",
       "      <th>points_r1_2019</th>\n",
       "      <th>level_2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LM123</th>\n",
       "      <td>Biological and Chemical Sc. (Biosci/Envir Sci/Ind</td>\n",
       "      <td>8</td>\n",
       "      <td>498*</td>\n",
       "      <td>8.0</td>\n",
       "      <td>441</td>\n",
       "      <td>412</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LM124</th>\n",
       "      <td>Mathematics (Math Science/Maths and Physics/Ec...</td>\n",
       "      <td>8</td>\n",
       "      <td>455</td>\n",
       "      <td>8.0</td>\n",
       "      <td>437</td>\n",
       "      <td>427</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LM125</th>\n",
       "      <td>Physics (Applied Physics or Mathematics and Ph...</td>\n",
       "      <td>8</td>\n",
       "      <td>489</td>\n",
       "      <td>8.0</td>\n",
       "      <td>404</td>\n",
       "      <td>405</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LM150</th>\n",
       "      <td>General Nursing</td>\n",
       "      <td>8</td>\n",
       "      <td>498*</td>\n",
       "      <td>8.0</td>\n",
       "      <td>475</td>\n",
       "      <td>440*</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LM152</th>\n",
       "      <td>Mental Health Nursing</td>\n",
       "      <td>8</td>\n",
       "      <td>478</td>\n",
       "      <td>8.0</td>\n",
       "      <td>419</td>\n",
       "      <td>388</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LM154</th>\n",
       "      <td>Intellectual Disability Nursing</td>\n",
       "      <td>8</td>\n",
       "      <td>462</td>\n",
       "      <td>8.0</td>\n",
       "      <td>420</td>\n",
       "      <td>381*</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LM156</th>\n",
       "      <td>Midwifery</td>\n",
       "      <td>8</td>\n",
       "      <td>521*</td>\n",
       "      <td>8.0</td>\n",
       "      <td>469</td>\n",
       "      <td>429</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MU001</th>\n",
       "      <td>Theology and Arts</td>\n",
       "      <td>8</td>\n",
       "      <td>294</td>\n",
       "      <td>8.0</td>\n",
       "      <td>290</td>\n",
       "      <td>290</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MU002</th>\n",
       "      <td>Theology</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>440</td>\n",
       "      <td>292</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MH001</th>\n",
       "      <td>Education - Primary Teaching (Froebel)</td>\n",
       "      <td>8</td>\n",
       "      <td>564</td>\n",
       "      <td>8.0</td>\n",
       "      <td>507</td>\n",
       "      <td>496*</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   title level_2021  \\\n",
       "code                                                                  \n",
       "LM123  Biological and Chemical Sc. (Biosci/Envir Sci/Ind          8   \n",
       "LM124  Mathematics (Math Science/Maths and Physics/Ec...          8   \n",
       "LM125  Physics (Applied Physics or Mathematics and Ph...          8   \n",
       "LM150                                    General Nursing          8   \n",
       "LM152                              Mental Health Nursing          8   \n",
       "LM154                    Intellectual Disability Nursing          8   \n",
       "LM156                                          Midwifery          8   \n",
       "MU001                                  Theology and Arts          8   \n",
       "MU002                                           Theology          8   \n",
       "MH001             Education - Primary Teaching (Froebel)          8   \n",
       "\n",
       "      points_r1_2021  level_2020 points_r1_2020 points_r1_2019 level_2019  \n",
       "code                                                                       \n",
       "LM123           498*         8.0            441            412          8  \n",
       "LM124            455         8.0            437            427          8  \n",
       "LM125            489         8.0            404            405          8  \n",
       "LM150           498*         8.0            475           440*          8  \n",
       "LM152            478         8.0            419            388          8  \n",
       "LM154            462         8.0            420           381*          8  \n",
       "LM156           521*         8.0            469            429          8  \n",
       "MU001            294         8.0            290            290          8  \n",
       "MU002            NaN         8.0            440            292          8  \n",
       "MH001            564         8.0            507           496*          8  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking random rows\n",
    "allcourses.iloc[800:810, 0:7]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dd3f819-35a9-4073-94d8-049faa00b25b",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cac80e2a-ff51-4944-8a03-adc3df714a23",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04cec776-ed6e-4161-9258-4313feb734dc",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a10a959d-2a42-4fa2-aeb4-5e095f84aedd",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3ccdc17-9afd-4806-9d9c-b31cec5ca79a",
   "metadata": {},
   "source": [
    "### Changing DataType\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3ca8e3b-721f-4c12-84ae-83a2e7d0af26",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "2b79b9c4-701b-4ceb-b628-f607dcb2c60d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1766 entries, AL801 to TL746\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   title           1765 non-null   object \n",
      " 1   level_2021      1365 non-null   object \n",
      " 2   points_r1_2021  1336 non-null   object \n",
      " 3   level_2020      1464 non-null   float64\n",
      " 4   points_r1_2020  1437 non-null   object \n",
      " 5   points_r1_2019  1297 non-null   object \n",
      " 6   level_2019      1309 non-null   object \n",
      "dtypes: float64(1), object(6)\n",
      "memory usage: 174.9+ KB\n"
     ]
    }
   ],
   "source": [
    "allcourses.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b24d16c3-9c40-44e3-926d-2af7ab1d6d38",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea8919ff-f743-4b18-ab02-ec6d20adcae2",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6101fc8-b438-46fc-8f64-83790a9b0a0c",
   "metadata": {
    "tags": []
   },
   "source": [
    "As mentioned earlier, the 2020 points do provide the level for each course. The levels are saved as an integer. As we had to add our own levels into 2021 and 2019, where '6/7' was used. So, I am going to convert the level column values into a string type to match 2019 and 2021."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4cf3b6c-1fd2-42f5-8358-9c6ee5b6f964",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "005ae5cd-0651-4811-aa99-4f220c2d11f2",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "4dfde92c-0785-4807-9a22-fd79bde27848",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting level column values into strings\n",
    "allcourses['level_2020'] = allcourses['level_2020'].astype('string')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e576819d-ca88-4b80-a009-1d3394e86ace",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b6529a9-9bca-445e-a845-bad0c0abfdd9",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0893dcad-1cea-436c-9140-ef7f5cf873f3",
   "metadata": {},
   "source": [
    "As we can see above the datatype that the points are saved as strings. We need to change this into a numerical value so we can perform statistical operations such as - max, min, mean, etc. - on the data. \n",
    "\n",
    "Some of the points values have special characters next to them to indicate extra elements - interview, portfolio, etc. - required for a course. We need to remove these characters before we can covert the strings to numeric values, an error is thrown if not. \n",
    "\n",
    "Note: 2020 points don't contain special characters in the same cell as points."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17c63d1e-2e0f-4257-ba71-a1cc75919c67",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a55aa9fd-d515-40eb-952e-027c3af23b7e",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "de9b291d-ce53-42a2-a09f-b0345c2680b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2021 points\n",
    "allcourses['points_r1_2021'] = allcourses['points_r1_2021'].str.replace(r'\\D', '', regex=True)\n",
    "\n",
    "# 2019 points\n",
    "allcourses['points_r1_2019'] = allcourses['points_r1_2019'].str.replace(r'\\D', '', regex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f85ea0d7-256c-48de-9ad8-79cbd26ff21d",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7923968a-09ab-473c-a9a6-ec6a59dd4539",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "919c80d3-6d41-409e-ae44-3d45ac8f8ac5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title             Popular Music at CIT Cork School of Music\n",
       "level_2021                                                8\n",
       "points_r1_2021                                         1028\n",
       "level_2020                                              8.0\n",
       "points_r1_2020                                         1088\n",
       "points_r1_2019                                          972\n",
       "level_2019                                                8\n",
       "Name: CR125, dtype: object"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking a courses which previously had a special character\n",
    "allcourses.loc['CR125']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "676d5b4b-fa73-4a6b-bf32-88156b3e434c",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb73c253-83d8-4976-9504-4b42b52d88e4",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "a48a0dfb-50bb-460b-b83b-3df555defeb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting points columns to numeric values\n",
    "cols = ['points_r1_2021', 'points_r1_2020', 'points_r1_2019']\n",
    "allcourses[cols] = allcourses[cols].apply(pd.to_numeric, errors='coerce', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8bc63f7-a6cd-4912-9bed-3a46bc99d65f",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5a76924-261c-4cca-9662-bb8bfce9a3e8",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "1cd4e01e-d5a8-4bdb-b954-9944433338e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1766 entries, AL801 to TL746\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   title           1765 non-null   object \n",
      " 1   level_2021      1365 non-null   object \n",
      " 2   points_r1_2021  1317 non-null   float64\n",
      " 3   level_2020      1464 non-null   string \n",
      " 4   points_r1_2020  1387 non-null   float64\n",
      " 5   points_r1_2019  1227 non-null   float64\n",
      " 6   level_2019      1309 non-null   object \n",
      "dtypes: float64(3), object(3), string(1)\n",
      "memory usage: 174.9+ KB\n"
     ]
    }
   ],
   "source": [
    "allcourses.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3dd990c-97cc-447f-965a-1e10517b6317",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44e0e50f-a719-4dcd-8d3d-1d875e8bcabd",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "333602dc-5ccd-428b-86f8-499279dfdb6a",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "854cff3b-3841-433e-8836-96a3dc5a440d",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b56a609-bc35-4d7a-9310-e8d34da7ec05",
   "metadata": {
    "tags": []
   },
   "source": [
    "***\n",
    "## Data Analysis\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebf63ec8-ed4a-4c2c-84f8-448819127901",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "619070d7-caff-42e3-8ea3-25147dd44c08",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e31f2d70-8771-41d2-b391-b6df62c5678b",
   "metadata": {},
   "source": [
    "![img](images/covid.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db9cb963-81c0-4bd3-a310-a04c20e91bdd",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0493b7a8-daf0-43c1-93e9-6a017ce10005",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb30204a-9833-4e58-821f-c341c88716b9",
   "metadata": {},
   "source": [
    "Before we start with a points comparison, it is important to note the impact Covid-19 had on the leaving certificate and CAO points. \n",
    "\n",
    "On the 21st of May 2020, the government announced that due to medical evidence the leaving certificate exam would be cancelled [15]. In its place students were offered the option of calculated grades or to sit the exam at a later date. In figures released by The Department of Education revealed over 98% students had registered for calculated grades [16].\n",
    "\n",
    "In 2021, students again had the option of sitting exams, receiving accredited grades or a mixture of both [17]. \n",
    "\n",
    "There was an overall concern that accredited grades along with a record number of applicants would drive up CAO points for courses across the board. Let’s investigate if that was indeed the case."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cefcb817-4b7a-4e65-b7fa-d4fddd4bd3e3",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4561b9ac-9433-4090-b6f1-06dbc8f77653",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "162ce946-8bef-40e9-a384-791494a3504c",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "314b5b45-99bb-4c46-9721-e26aadad80c1",
   "metadata": {},
   "outputs": [
    {
     "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>points_r1_2021</th>\n",
       "      <th>points_r1_2020</th>\n",
       "      <th>points_r1_2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1317.000000</td>\n",
       "      <td>1387.000000</td>\n",
       "      <td>1227.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>361.665148</td>\n",
       "      <td>350.217015</td>\n",
       "      <td>340.366748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>139.025919</td>\n",
       "      <td>134.311616</td>\n",
       "      <td>132.806330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>57.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>260.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>252.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>325.000000</td>\n",
       "      <td>316.000000</td>\n",
       "      <td>307.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>462.000000</td>\n",
       "      <td>430.500000</td>\n",
       "      <td>409.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1028.000000</td>\n",
       "      <td>1088.000000</td>\n",
       "      <td>979.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       points_r1_2021  points_r1_2020  points_r1_2019\n",
       "count     1317.000000     1387.000000     1227.000000\n",
       "mean       361.665148      350.217015      340.366748\n",
       "std        139.025919      134.311616      132.806330\n",
       "min         57.000000       55.000000       55.000000\n",
       "25%        260.000000      252.000000      252.000000\n",
       "50%        325.000000      316.000000      307.000000\n",
       "75%        462.000000      430.500000      409.500000\n",
       "max       1028.000000     1088.000000      979.000000"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overview \n",
    "allcourses.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fb3b10b-2c54-4595-88d9-cb36df34de92",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7075f59a-b51b-477d-b6be-e0161c89d91e",
   "metadata": {},
   "source": [
    "We can use the `.groupby()` method to group <i>\"...the data according to the categories and apply a function to the categories\"</i> [24]. We can then use various aggregate functions to analyse the data.\n",
    "\n",
    "We'll use the course levels provided by 2020 as we know these are correctly labelled.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8faaec5a-217a-47b6-9ce9-2f9086a97a28",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "5be9f087-ca8d-4339-93eb-429d95a67720",
   "metadata": {},
   "outputs": [
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">points_r1_2019</th>\n",
       "      <th colspan=\"4\" halign=\"left\">points_r1_2020</th>\n",
       "      <th colspan=\"4\" halign=\"left\">points_r1_2021</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>amin</th>\n",
       "      <th>amax</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>amin</th>\n",
       "      <th>amax</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>amin</th>\n",
       "      <th>amax</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>level_2020</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>227.586957</td>\n",
       "      <td>71.481956</td>\n",
       "      <td>128.0</td>\n",
       "      <td>454.0</td>\n",
       "      <td>235.283784</td>\n",
       "      <td>69.185183</td>\n",
       "      <td>149.0</td>\n",
       "      <td>473.0</td>\n",
       "      <td>243.724638</td>\n",
       "      <td>88.867364</td>\n",
       "      <td>120.0</td>\n",
       "      <td>502.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>238.588889</td>\n",
       "      <td>98.528888</td>\n",
       "      <td>100.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>245.714286</td>\n",
       "      <td>88.084202</td>\n",
       "      <td>100.0</td>\n",
       "      <td>781.0</td>\n",
       "      <td>256.902280</td>\n",
       "      <td>99.383937</td>\n",
       "      <td>100.0</td>\n",
       "      <td>775.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>384.831836</td>\n",
       "      <td>127.853080</td>\n",
       "      <td>55.0</td>\n",
       "      <td>979.0</td>\n",
       "      <td>393.800813</td>\n",
       "      <td>126.253491</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1088.0</td>\n",
       "      <td>408.978261</td>\n",
       "      <td>127.961471</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1028.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           points_r1_2019                           points_r1_2020  \\\n",
       "                     mean         std   amin   amax           mean   \n",
       "level_2020                                                           \n",
       "6.0            227.586957   71.481956  128.0  454.0     235.283784   \n",
       "7.0            238.588889   98.528888  100.0  700.0     245.714286   \n",
       "8.0            384.831836  127.853080   55.0  979.0     393.800813   \n",
       "\n",
       "                                      points_r1_2021                     \\\n",
       "                   std   amin    amax           mean         std   amin   \n",
       "level_2020                                                                \n",
       "6.0          69.185183  149.0   473.0     243.724638   88.867364  120.0   \n",
       "7.0          88.084202  100.0   781.0     256.902280   99.383937  100.0   \n",
       "8.0         126.253491   55.0  1088.0     408.978261  127.961471   57.0   \n",
       "\n",
       "                    \n",
       "              amax  \n",
       "level_2020          \n",
       "6.0          502.0  \n",
       "7.0          775.0  \n",
       "8.0         1028.0  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Columns to use\n",
    "c = ['points_r1_2019', 'points_r1_2020', 'points_r1_2021']\n",
    "\n",
    "# Groupby\n",
    "allcourses.groupby(['level_2020'])[c].agg([np.mean, np.std, np.min, np.max])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca6ffa99-13a2-4ec7-bd73-b1cd2a431de5",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcecb6f0-7ba4-4886-aee9-0f8a1de1f468",
   "metadata": {},
   "source": [
    "Just from this quick overview, we can see that points have increased over the years. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41bab107-f2a1-4130-9229-265e2adfe1c3",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1e70f20-f905-4214-85af-f6172b7f90dc",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "c296e338-dee9-4dcc-b6cd-f6670f28af62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1765"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of courses titles\n",
    "allcourses['title'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "396b43b6-7bed-4053-8b4c-0639b3b051d6",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ad34671-2373-4875-9c6b-63c64d614ef1",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71f7f47b-f0aa-47e0-84be-e30b515c7f0a",
   "metadata": {},
   "source": [
    "There are 1765 courses in our dataframe. It's important to note that this does not mean there was 1765 courses available to candidates each year. The number of courses varies year on year. If there's low demand for courses, they may cease that and subsequent years. Additionally, new courses may be added. E.g., a course that was available in 2019 may not be running in 2020."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ffe2b4a-40e3-4de5-afc3-a272ea9f7ffe",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b504549-bafa-47c2-bdcc-d6b790353aee",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "8b017ae0-306f-47d9-969c-fe29c14a8fb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8      931\n",
       "6/7    378\n",
       "Name: level_2019, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Breakdown of 2019 courses\n",
    "allcourses['level_2019'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce0e6157-158f-42c6-a959-7b59c879224e",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c143601f-8c56-4b23-9e50-570093529b41",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "e4fe0e8e-6c61-4b8b-bedd-a547da20549d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0    1027\n",
       "7.0     346\n",
       "6.0      91\n",
       "Name: level_2020, dtype: Int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Breakdown of 2020 courses\n",
    "allcourses['level_2020'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d6e4273-f4f8-48a3-be10-369fd949429c",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d35b3a45-7ae4-4505-b565-637f4fc15621",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "7b705e6f-9905-4d80-8743-aa9c47095c6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8      949\n",
       "6/7    416\n",
       "Name: level_2021, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Breakdown of 2021 courses\n",
    "allcourses['level_2021'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35405ffc-1150-44b8-bc63-436043290d03",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9f77fc1-3731-46a4-ab88-d189d8f6e813",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2399a59b-84e6-4351-b9d3-5d0683c0e092",
   "metadata": {},
   "source": [
    "Let's visualise these numbers using a count plot."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3852a63-6061-45ba-9255-3eec1ce91a93",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "1ac854b6-cacd-4134-92c7-d69c35888147",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='level_2021', ylabel='count'>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure Size\n",
    "plt.rcParams['figure.figsize'] = (15, 10)\n",
    "\n",
    "# Generating a countplot for each year\n",
    "\n",
    "# nrows, ncols, the first plot\n",
    "plt.subplot(1,3,1)\n",
    "sns.countplot(x='level_2019', data=allcourses)\n",
    "\n",
    "# nrows, ncols, the second plot\n",
    "plt.subplot(1,3,2)\n",
    "sns.countplot(x='level_2020', data=allcourses)\n",
    "\n",
    "# nrows, ncols, the thrid plot\n",
    "plt.subplot(1,3,3)\n",
    "sns.countplot(x='level_2021', data=allcourses)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bd1afd5-1d41-4e51-8d3a-0c922da821ea",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "795957d3-4d83-425a-a92a-94012661ae5d",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32aab8cc-f61c-492f-9174-3d15e53c0c6d",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa3b6428-9573-44d3-ac0e-8cd192eb1e50",
   "metadata": {},
   "source": [
    "2020 was the year which offered students the greatest number of courses."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b93ceb5-951f-402b-a972-bfb33c2fcf0e",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34eda028-2d1c-407a-b044-7fd2b3d84c0e",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "a877338f-2a63-4d5d-84bd-d93171f90492",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "350.21701514059123"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average number of points 2020\n",
    "allcourses['points_r1_2020'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ea8cb97-0248-414c-9623-da1106655ce0",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aecd9c02-aefd-4abd-957f-0ce9d46719db",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d33ffc9-f868-4bda-b5e1-418e5441eba3",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1269a9e1-cc9c-4989-9042-42214554d5ca",
   "metadata": {},
   "source": [
    "Next, let's take a look at the points. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a89459b-94ec-4a56-b77f-9593767fa441",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c51ec793-32d5-4be9-8e75-5ed5e4aad7cf",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6cefdde-fe41-4c8e-9cf0-2c97f41e63da",
   "metadata": {},
   "source": [
    "#### KDE Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e02ce42f-88ea-44ac-a05b-f5c20be2d9d8",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5c1ddd5-f6ce-452b-80d8-ce9bbccfb7c4",
   "metadata": {},
   "source": [
    "<i>\"A kernel density estimate (KDE) plot is a method for visualizing the distribution of observations in a dataset\"</i> [18].\n",
    "\n",
    "The KDE plot is generated by <i>“…drawing a small continuous curve (also called kernel) for every individual data point along an axis, all of these curves are then added together to obtain a single smooth density estimation”</i> [19]."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4c07158-609a-41ca-a58c-68b280594cfc",
   "metadata": {},
   "source": [
    "To generate the KDE plot we can use `pandas` built in visualisation `.plot()`. This method is a wrapper for `pyplot.plot()` and the result is a graph identical to the one you produced with `Matplotlib` [23]."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c6aafac-4098-45ec-b132-4ceb3d5b50e6",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "a0dce2be-dfb1-4556-9b65-07dbbf8f8217",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x1f0809859d0>,\n",
       "  <matplotlib.axis.XTick at 0x1f0809859a0>,\n",
       "  <matplotlib.axis.XTick at 0x1f0809e2dc0>,\n",
       "  <matplotlib.axis.XTick at 0x1f0809ee6a0>,\n",
       "  <matplotlib.axis.XTick at 0x1f0809eedf0>,\n",
       "  <matplotlib.axis.XTick at 0x1f0809f6580>,\n",
       "  <matplotlib.axis.XTick at 0x1f0809f6cd0>,\n",
       "  <matplotlib.axis.XTick at 0x1f0809f6c10>,\n",
       "  <matplotlib.axis.XTick at 0x1f0809eea90>,\n",
       "  <matplotlib.axis.XTick at 0x1f0809f96a0>,\n",
       "  <matplotlib.axis.XTick at 0x1f0809f9df0>,\n",
       "  <matplotlib.axis.XTick at 0x1f080958280>,\n",
       "  <matplotlib.axis.XTick at 0x1f080a05730>,\n",
       "  <matplotlib.axis.XTick at 0x1f080a05e80>,\n",
       "  <matplotlib.axis.XTick at 0x1f080a05e50>],\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating a kde plot for points \n",
    "allcourses['points_r1_2021'].plot.kde(label='2021', linestyle='-', linewidth=3)\n",
    "allcourses['points_r1_2020'].plot.kde(label='2020', linestyle='--', linewidth=3)\n",
    "allcourses['points_r1_2019'].plot.kde(label='2019', linestyle=':', linewidth=3)\n",
    "\n",
    "# Adding a legend\n",
    "plt.legend()\n",
    "\n",
    "# Adding ticks to xaxis\n",
    "plt.xticks(np.arange(-500, 1650, step=150))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6a4567a-9df6-46fe-a358-76f78d47fa01",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f75e625-b36b-412a-9bf1-2a7331dc43e3",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "700aed10-2e1c-44f8-b06f-16d523fa1a55",
   "metadata": {},
   "source": [
    "From the above plot we can see that the majority of courses require in and around 300 points."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0f2f489-520c-485f-a125-52ff0c4d5b3a",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "731ac593-fc33-4e49-bd34-7b5e16017915",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "590cc7aa-3126-42dd-9cce-a6d7e18c90b9",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cf6ca86-3c2e-4879-bf03-cec0a12d7f00",
   "metadata": {},
   "source": [
    "#### Pairplot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e27d9d51-167d-4f8d-8ec3-3d72a27a21b6",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "026e96d1-a3aa-4009-adb2-c6a08bc88ded",
   "metadata": {},
   "source": [
    "We can use a pair plot to plot any pairwise relationships in our data [20]. I'm going to use the 2020 level as the hue as it is the only year, we know for sure which course is a level 6 or 7."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bebad356-53da-4897-8cac-d54ebaf16c59",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "fd946ce3-8b56-454b-be6f-6a5862616f9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1f080a18340>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 602.375x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating a pairplot\n",
    "sns.pairplot(data=allcourses, hue='level_2020')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4746439e-f137-4f6b-8ade-332a01eef9ec",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "859a9c1e-ad03-41f1-9246-d5bfb37d6fd9",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "905c69f5-1d22-4b17-b59c-63233c8ede33",
   "metadata": {},
   "source": [
    "We can see there are some outliers in the data, and we will look at the further on. Another observation is that there seems to be the most difference between 2019. I would expect this as 2019 whereas a direct result of the written exams and 2021 was the second year accredited grades where an option."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17f95cef-f696-4070-aae0-a219e530d91d",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1694cdb8-159a-43c4-b13b-4e58e3cbebb1",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "327df853-ccd4-4ebe-aef3-ef00d8a3e221",
   "metadata": {},
   "source": [
    "### Top 10 Highest Course Points Per Year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c72a69b3-9fac-4309-add9-4687476de2f0",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "853678c0-a909-4fc9-b5b3-de149fab36d1",
   "metadata": {},
   "source": [
    "We can do sort a column in ascending or descending order by using the pandas method `.sort_values()` [21]."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4c909fd-79a7-4f5d-9c4b-a6ba54ac01fc",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6cf22bf-a4a9-49c5-91ec-ce228ad2a583",
   "metadata": {},
   "source": [
    "### 2019"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af77ef0b-8393-4d02-b5f7-c5a700192a4e",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "3ff3213d-403b-4182-a2da-01860772ccc2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "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>title</th>\n",
       "      <th>level_2021</th>\n",
       "      <th>points_r1_2021</th>\n",
       "      <th>level_2020</th>\n",
       "      <th>points_r1_2020</th>\n",
       "      <th>points_r1_2019</th>\n",
       "      <th>level_2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CR210</th>\n",
       "      <td>Contemporary Applied Art (Ceramics  Glass  Tex...</td>\n",
       "      <td>8</td>\n",
       "      <td>450.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>979.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CR125</th>\n",
       "      <td>Popular Music at CIT Cork School of Music</td>\n",
       "      <td>8</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1088.0</td>\n",
       "      <td>972.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DL834</th>\n",
       "      <td>Film and Television Production</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>990.0</td>\n",
       "      <td>933.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CR225</th>\n",
       "      <td>Photography with New Media at CIT Crawford Col...</td>\n",
       "      <td>8</td>\n",
       "      <td>435.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>920.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LC114</th>\n",
       "      <td>Fashion and Textiles for Product and Costume (...</td>\n",
       "      <td>8</td>\n",
       "      <td>914.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DL831</th>\n",
       "      <td>Design for Stage and Screen (Production Design)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>781.0</td>\n",
       "      <td>893.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DL830</th>\n",
       "      <td>Design for Stage and Screen (Character Makeup ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>939.0</td>\n",
       "      <td>879.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CR126</th>\n",
       "      <td>Popular Music: Drums at CIT Cork School of Music</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>801.0</td>\n",
       "      <td>878.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CR129</th>\n",
       "      <td>Popular Music: Voice at CIT Cork School of Music</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>841.0</td>\n",
       "      <td>857.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DL832</th>\n",
       "      <td>Animation</td>\n",
       "      <td>8</td>\n",
       "      <td>989.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>855.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   title level_2021  \\\n",
       "code                                                                  \n",
       "CR210  Contemporary Applied Art (Ceramics  Glass  Tex...          8   \n",
       "CR125          Popular Music at CIT Cork School of Music          8   \n",
       "DL834                     Film and Television Production        NaN   \n",
       "CR225  Photography with New Media at CIT Crawford Col...          8   \n",
       "LC114  Fashion and Textiles for Product and Costume (...          8   \n",
       "DL831    Design for Stage and Screen (Production Design)        NaN   \n",
       "DL830  Design for Stage and Screen (Character Makeup ...        NaN   \n",
       "CR126   Popular Music: Drums at CIT Cork School of Music        NaN   \n",
       "CR129   Popular Music: Voice at CIT Cork School of Music        NaN   \n",
       "DL832                                          Animation          8   \n",
       "\n",
       "       points_r1_2021 level_2020  points_r1_2020  points_r1_2019 level_2019  \n",
       "code                                                                         \n",
       "CR210           450.0        8.0             NaN           979.0          8  \n",
       "CR125          1028.0        8.0          1088.0           972.0          8  \n",
       "DL834             NaN        8.0           990.0           933.0          8  \n",
       "CR225           435.0        8.0             NaN           920.0          8  \n",
       "LC114           914.0        8.0           880.0           900.0          8  \n",
       "DL831             NaN        8.0           781.0           893.0          8  \n",
       "DL830             NaN        8.0           939.0           879.0          8  \n",
       "CR126             NaN        8.0           801.0           878.0          8  \n",
       "CR129             NaN        8.0           841.0           857.0          8  \n",
       "DL832           989.0        8.0           920.0           855.0          8  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 10 course points by 2019\n",
    "allcourses.sort_values(by=['points_r1_2019', 'title'], ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8fac882-85bf-4fad-ac9f-02fbeb78b52e",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48f1581b-a4e9-4850-bd4c-2813d4c43102",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16938d12-7010-4fa9-9aad-c2aeeb824a8b",
   "metadata": {},
   "source": [
    "Some observations:\n",
    "\n",
    "- Unsurprisingly, the top 10 consists of level 8 courses.\n",
    "- Half of 2019's (5) courses with top points, have been dropped in 2021. \n",
    "- I find the trajectory of 2019's top course - CR210 - interesting. It seems to have been either dropped in 2020 or an alternative method of selecting students was employed following Covid uncertainty. Additionally, there is a massive drop in points required for 2021. Below I will work out how much of a decrease in points this is."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67736032-49eb-4f24-9472-efceb636778e",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed77d153-caa0-424e-a07c-9a439f6fb9fb",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "bf996448-fd76-4a8a-87c9-8b6f2e7cb792",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54.03472931562819"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the % decrease\n",
    "points_2021 = 450\n",
    "points_2019 = 979\n",
    "\n",
    "decrease = points_2019 - points_2021\n",
    "\n",
    "decrease / points_2019 * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5c5e5dd-1acf-4a1a-9528-da582e124354",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6303380e-a8ae-44fe-a96e-ce931d264617",
   "metadata": {},
   "source": [
    "There is a 54% decrease in 2019's top course in 2021."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25dbfbaf-4e92-4878-b81e-9ca8671be5f9",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "439a9f47-5e73-4933-9d29-d7a75f009bd1",
   "metadata": {},
   "source": [
    "You probably notice that these top 10 courses exceed the limit of 625 points which is the maximum a student can be awarded in sitting/accredited in the exam. Thus, meaning these courses required additional entry requirements such as interview, portfolio, audition. \n",
    "\n",
    "Let's see what the top courses based solely on points the maximum points a student can score on exams only. There's a good chance this will also filter out the major outliers seen in the pair plot above."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3814e95-b514-454a-a2d5-e853b1567705",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72dd1f84-38bb-49af-b2d8-0583ace36286",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "d64c0f43-f507-41c7-8ed1-20a09592783a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>title</th>\n",
       "      <th>level_2021</th>\n",
       "      <th>points_r1_2021</th>\n",
       "      <th>level_2020</th>\n",
       "      <th>points_r1_2020</th>\n",
       "      <th>points_r1_2019</th>\n",
       "      <th>level_2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CR220</th>\n",
       "      <td>Fine Art at CIT Crawford College of Art and De...</td>\n",
       "      <td>8</td>\n",
       "      <td>400.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>620.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DN670</th>\n",
       "      <td>Economics and Finance</td>\n",
       "      <td>8</td>\n",
       "      <td>625.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>613.0</td>\n",
       "      <td>601.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CK702</th>\n",
       "      <td>Dentistry</td>\n",
       "      <td>8</td>\n",
       "      <td>625.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>613.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR052</th>\n",
       "      <td>Dental Science</td>\n",
       "      <td>8</td>\n",
       "      <td>625.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>613.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR034</th>\n",
       "      <td>Management Science and Information Systems Stu...</td>\n",
       "      <td>8</td>\n",
       "      <td>625.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>613.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CR600</th>\n",
       "      <td>Visual Communications at CIT Crawford College ...</td>\n",
       "      <td>8</td>\n",
       "      <td>280.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>587.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CR127</th>\n",
       "      <td>Popular Music: Electric Guitar at CIT Cork Sch...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>838.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DL827</th>\n",
       "      <td>Art</td>\n",
       "      <td>8</td>\n",
       "      <td>574.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>572.0</td>\n",
       "      <td>583.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DN230</th>\n",
       "      <td>Actuarial and Financial Studies</td>\n",
       "      <td>8</td>\n",
       "      <td>613.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>601.0</td>\n",
       "      <td>577.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CK703</th>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>8</td>\n",
       "      <td>613.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>577.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR017</th>\n",
       "      <td>Law and Business</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>576.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DN300</th>\n",
       "      <td>Veterinary Medicine - Undergraduate Entry</td>\n",
       "      <td>8</td>\n",
       "      <td>601.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR020</th>\n",
       "      <td>Law and Political Science</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR031</th>\n",
       "      <td>Mathematics</td>\n",
       "      <td>8</td>\n",
       "      <td>579.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>531.0</td>\n",
       "      <td>566.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR015</th>\n",
       "      <td>Philosophy  Political Science  Economics and S...</td>\n",
       "      <td>8</td>\n",
       "      <td>613.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>566.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   title level_2021  \\\n",
       "code                                                                  \n",
       "CR220  Fine Art at CIT Crawford College of Art and De...          8   \n",
       "DN670                              Economics and Finance          8   \n",
       "CK702                                          Dentistry          8   \n",
       "TR052                                     Dental Science          8   \n",
       "TR034  Management Science and Information Systems Stu...          8   \n",
       "CR600  Visual Communications at CIT Crawford College ...          8   \n",
       "CR127  Popular Music: Electric Guitar at CIT Cork Sch...        NaN   \n",
       "DL827                                                Art          8   \n",
       "DN230                    Actuarial and Financial Studies          8   \n",
       "CK703                                           Pharmacy          8   \n",
       "TR017                                   Law and Business        NaN   \n",
       "DN300          Veterinary Medicine - Undergraduate Entry          8   \n",
       "TR020                          Law and Political Science        NaN   \n",
       "TR031                                        Mathematics          8   \n",
       "TR015  Philosophy  Political Science  Economics and S...          8   \n",
       "\n",
       "       points_r1_2021 level_2020  points_r1_2020  points_r1_2019 level_2019  \n",
       "code                                                                         \n",
       "CR220           400.0        8.0             NaN           620.0          8  \n",
       "DN670           625.0        8.0           613.0           601.0          8  \n",
       "CK702           625.0        8.0           613.0           590.0          8  \n",
       "TR052           625.0        8.0           613.0           590.0          8  \n",
       "TR034           625.0        8.0           613.0           589.0          8  \n",
       "CR600           280.0        8.0             NaN           587.0          8  \n",
       "CR127             NaN        8.0           838.0           584.0          8  \n",
       "DL827           574.0        8.0           572.0           583.0          8  \n",
       "DN230           613.0        8.0           601.0           577.0          8  \n",
       "CK703           613.0        8.0           590.0           577.0          8  \n",
       "TR017             NaN        8.0           602.0           576.0          8  \n",
       "DN300           601.0        8.0           589.0           567.0          8  \n",
       "TR020             NaN        8.0           602.0           567.0          8  \n",
       "TR031           579.0        8.0           531.0           566.0          8  \n",
       "TR015           613.0        8.0           589.0           566.0          8  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 10 courses <= 625 points\n",
    "top_2019 = allcourses[allcourses.points_r1_2019 <= 625].sort_values('points_r1_2019',ascending=False)\n",
    "\n",
    "# Filtering out courses which contain '('\n",
    "top_2019[~top_2019.title.str.contains('\\(')].head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba4b9412-ceba-43f1-b16d-a3c97318ad9d",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3a82548-73c6-42ca-a1e8-097bc0616850",
   "metadata": {},
   "source": [
    "That looks a lot better. I've chosen to look at the top 15 course points as I did some research and - CR220, CR600, CR127, DL827 - require an interview, portfolio or audition but as this wasn't included in the title they are included in the filter."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ce84539-d8a9-48ae-a275-81433e181f81",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc3a04ff-ea47-4302-8351-b2834e73f229",
   "metadata": {},
   "source": [
    "- Although it required additional requirements the trajectory of CR220 is interesting. It went from commanding 620 points in 2019, not being offered in 2020 and then dropping a massive 180 points for 2021.\n",
    "- Another huge drop was CR220. The top course (inc. additional requirements) required 620 points in 2019. The course wasn't offered in 2020. It was offered again in 2021 but with a massive 307 point drop, now requiring 400 points.\n",
    "- 3 of 2019's top courses are no longer offered in 2021. \n",
    "- For 3/4 of the courses which had extra requirements all had a big drop in points in 2021. \n",
    "- For the most part there does seem to have been a points inflation since the move to accredited grades. We will work out the percentage later on."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "924f093a-3c71-446c-b640-6da33965a19d",
   "metadata": {},
   "source": [
    "<br>"
   ]
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  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "0e866f89-165a-4a1e-b40d-0187ce5a29a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f080a18b50>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating a barplot \n",
    "top_2019[~top_2019.title.str.contains('\\(')].head(15).plot.barh()\n",
    "\n",
    "# Labeling y-axis\n",
    "plt.ylabel('Points')\n",
    "\n",
    "\n",
    "# Adding a legend\n",
    "plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2862bff-1987-4e7c-83c8-915151c47907",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0716874c-d9e3-4e03-9065-c7f27408065b",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34a7c6b1-3086-4c96-9210-80755548d9d4",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a15e1d29-9497-4008-a8d3-02040ecc11ce",
   "metadata": {},
   "source": [
    "Let's have a look at the last 5 rows i.e., courses which require the least amount of points."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0730a8e6-0512-48b9-b4e7-49ff0487ad51",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "39dd8d3b-8820-440f-8eb8-8ecd1170e896",
   "metadata": {},
   "outputs": [
    {
     "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>title</th>\n",
       "      <th>level_2021</th>\n",
       "      <th>points_r1_2021</th>\n",
       "      <th>level_2020</th>\n",
       "      <th>points_r1_2020</th>\n",
       "      <th>points_r1_2019</th>\n",
       "      <th>level_2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DB576</th>\n",
       "      <td>Film and Creative Media</td>\n",
       "      <td>6/7</td>\n",
       "      <td>101.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RC101</th>\n",
       "      <td>Medicine - Graduate Entry (GAMSAT required)</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DN401</th>\n",
       "      <td>Medicine - Graduate Entry (GAMSAT required)</td>\n",
       "      <td>8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CK791</th>\n",
       "      <td>Medicine  - Graduate Entry (GAMSAT required)</td>\n",
       "      <td>8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LM101</th>\n",
       "      <td>Medicine  - Graduate Entry (GAMSAT required)</td>\n",
       "      <td>8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              title level_2021  \\\n",
       "code                                                             \n",
       "DB576                       Film and Creative Media        6/7   \n",
       "RC101   Medicine - Graduate Entry (GAMSAT required)          8   \n",
       "DN401   Medicine - Graduate Entry (GAMSAT required)          8   \n",
       "CK791  Medicine  - Graduate Entry (GAMSAT required)          8   \n",
       "LM101  Medicine  - Graduate Entry (GAMSAT required)          8   \n",
       "\n",
       "       points_r1_2021 level_2020  points_r1_2020  points_r1_2019 level_2019  \n",
       "code                                                                         \n",
       "DB576           101.0        7.0             NaN           100.0        6/7  \n",
       "RC101             NaN        8.0             NaN            60.0          8  \n",
       "DN401            61.0        8.0             NaN            59.0          8  \n",
       "CK791            60.0        8.0            57.0            58.0          8  \n",
       "LM101            57.0        8.0            55.0            55.0          8  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Last 5 rows\n",
    "top_2019.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34e888d1-af8e-4045-90a6-e2ca5c8b8288",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49b5293b-6bfe-497f-ab3d-dad040195945",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86e7be99-dc5e-4900-ab55-dea587719eb2",
   "metadata": {},
   "source": [
    "The bottom 4 courses are quite misleading. All these courses require a GAMSAT*.\n",
    "\n",
    "*Graduate Medical School Admissions Test (GAMSAT) <i>\"is a standardised exam designed, scored and developed by ACER to assist in the admissions of students to graduate-entry programmes (medicine, dentistry, optometry, pharmacy, podiatry) open to graduates of any discipline. GRADUATE ENTRY IRISH MEDICAL SCHOOLS. There are 4 medicals schools in Ireland which require the GAMSAT as part of their admissions criteria.\"</i> [34]."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e01705ce-fc2c-4d94-a25d-563cf9ca0d26",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5de539e4-f31e-420f-b3ea-24416157f2b8",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "545884a0-a99c-4ffa-8903-94e1ad73068f",
   "metadata": {},
   "source": [
    "Considering we now know that in 2019, the first course without an additional requirement starts at 601 points and the last being 100 points. Let's use these as the benchmarks to get an overall summary of the data. I think this will give us a fairer view of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29a50ac7-88cb-4053-8ff2-bee3110917f2",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "2e99d14e-cb57-4deb-a5d4-4375c8aafae3",
   "metadata": {},
   "outputs": [
    {
     "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>points_r1_2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1179.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>326.059372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>106.001361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>251.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>307.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>401.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>601.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       points_r1_2019\n",
       "count     1179.000000\n",
       "mean       326.059372\n",
       "std        106.001361\n",
       "min        100.000000\n",
       "25%        251.000000\n",
       "50%        307.000000\n",
       "75%        401.000000\n",
       "max        601.000000"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overview of 2019 points\n",
    "stats_19 = allcourses[(allcourses['points_r1_2019'] >= 100) & (allcourses['points_r1_2019'] <= 601) ].describe().drop(['points_r1_2021', 'points_r1_2020'], axis=1)\n",
    "\n",
    "stats_19"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58d8093b-d2d5-4e24-821a-baf086f91ab9",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e79a3f55-f37e-472f-aed4-1ba322ca18dc",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7380d21f-2285-4202-976a-1e1c20161fde",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c1d1731-aacf-4312-a8d6-dd696e4c236e",
   "metadata": {},
   "source": [
    "### 2020"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb3c2b10-20ff-4b5b-bc1f-1de73b8794aa",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ec1b301-d2da-47de-a59e-99a237cb19de",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "d0632421-c56a-409d-82e6-a059f6ea5348",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>title</th>\n",
       "      <th>level_2021</th>\n",
       "      <th>points_r1_2021</th>\n",
       "      <th>level_2020</th>\n",
       "      <th>points_r1_2020</th>\n",
       "      <th>points_r1_2019</th>\n",
       "      <th>level_2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CK702</th>\n",
       "      <td>Dentistry</td>\n",
       "      <td>8</td>\n",
       "      <td>625.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>613.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR034</th>\n",
       "      <td>Management Science and Information Systems Stu...</td>\n",
       "      <td>8</td>\n",
       "      <td>625.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>613.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR052</th>\n",
       "      <td>Dental Science</td>\n",
       "      <td>8</td>\n",
       "      <td>625.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>613.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DN670</th>\n",
       "      <td>Economics and Finance</td>\n",
       "      <td>8</td>\n",
       "      <td>625.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>613.0</td>\n",
       "      <td>601.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR020</th>\n",
       "      <td>Law and Political Science</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR017</th>\n",
       "      <td>Law and Business</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>576.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DN230</th>\n",
       "      <td>Actuarial and Financial Studies</td>\n",
       "      <td>8</td>\n",
       "      <td>613.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>601.0</td>\n",
       "      <td>577.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DN440</th>\n",
       "      <td>Biomedical  Health and Life Sciences</td>\n",
       "      <td>8</td>\n",
       "      <td>613.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CK703</th>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>8</td>\n",
       "      <td>613.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>577.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR072</th>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>8</td>\n",
       "      <td>613.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>555.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DN300</th>\n",
       "      <td>Veterinary Medicine - Undergraduate Entry</td>\n",
       "      <td>8</td>\n",
       "      <td>601.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LM100</th>\n",
       "      <td>Physiotherapy</td>\n",
       "      <td>8</td>\n",
       "      <td>613.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR080</th>\n",
       "      <td>Global Business</td>\n",
       "      <td>8</td>\n",
       "      <td>613.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR015</th>\n",
       "      <td>Philosophy  Political Science  Economics and S...</td>\n",
       "      <td>8</td>\n",
       "      <td>613.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>566.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MI008</th>\n",
       "      <td>Education and Psychology - Primary Teaching</td>\n",
       "      <td>8</td>\n",
       "      <td>590.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>543.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   title level_2021  \\\n",
       "code                                                                  \n",
       "CK702                                          Dentistry          8   \n",
       "TR034  Management Science and Information Systems Stu...          8   \n",
       "TR052                                     Dental Science          8   \n",
       "DN670                              Economics and Finance          8   \n",
       "TR020                          Law and Political Science        NaN   \n",
       "TR017                                   Law and Business        NaN   \n",
       "DN230                    Actuarial and Financial Studies          8   \n",
       "DN440               Biomedical  Health and Life Sciences          8   \n",
       "CK703                                           Pharmacy          8   \n",
       "TR072                                           Pharmacy          8   \n",
       "DN300          Veterinary Medicine - Undergraduate Entry          8   \n",
       "LM100                                      Physiotherapy          8   \n",
       "TR080                                    Global Business          8   \n",
       "TR015  Philosophy  Political Science  Economics and S...          8   \n",
       "MI008        Education and Psychology - Primary Teaching          8   \n",
       "\n",
       "       points_r1_2021 level_2020  points_r1_2020  points_r1_2019 level_2019  \n",
       "code                                                                         \n",
       "CK702           625.0        8.0           613.0           590.0          8  \n",
       "TR034           625.0        8.0           613.0           589.0          8  \n",
       "TR052           625.0        8.0           613.0           590.0          8  \n",
       "DN670           625.0        8.0           613.0           601.0          8  \n",
       "TR020             NaN        8.0           602.0           567.0          8  \n",
       "TR017             NaN        8.0           602.0           576.0          8  \n",
       "DN230           613.0        8.0           601.0           577.0          8  \n",
       "DN440           613.0        8.0           590.0           565.0          8  \n",
       "CK703           613.0        8.0           590.0           577.0          8  \n",
       "TR072           613.0        8.0           590.0           555.0          8  \n",
       "DN300           601.0        8.0           589.0           567.0          8  \n",
       "LM100           613.0        8.0           589.0           565.0          8  \n",
       "TR080           613.0        8.0           589.0           565.0          8  \n",
       "TR015           613.0        8.0           589.0           566.0          8  \n",
       "MI008           590.0        8.0           588.0           543.0          8  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 10 courses <= 625 points\n",
    "top_2020 = allcourses[allcourses.points_r1_2020 <= 625].sort_values('points_r1_2020',ascending=False)\n",
    "\n",
    "# Filtering out courses which contain '('\n",
    "top_2020[~top_2020.title.str.contains('\\(')].head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ea7fc5d-078e-4695-9f37-4c571c3258a1",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99043964-d815-4ac6-9608-8abd2c5029d5",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9a8ce77-0f0d-427e-8161-4ce50e11af60",
   "metadata": {},
   "source": [
    "Some observations:\n",
    "\n",
    "- Once again it is level 8 courses which occupy the top 15 places. \n",
    "- None of the top 15 courses (after filtering) have additional requirements.\n",
    "- All 15 courses where pre-existing i.e., they have ran for at least one year.\n",
    "- Points for all 15 courses increased. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a4bc1c5-0f59-4ebb-8277-d4fce5e58fd1",
   "metadata": {},
   "source": [
    "<br>"
   ]
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  {
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   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "eac8cd25-8e38-4415-aa46-55eafb896e61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f08140b400>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating a barplot \n",
    "top_2020[~top_2020.title.str.contains('\\(')].head(15).plot.barh()\n",
    "\n",
    "# Adding a legend\n",
    "plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "540cd5c6-2f1d-4f60-8b8e-c25d01961bc6",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de8034bd-00af-418b-bf29-0b140a99fab1",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1f0d472-a00b-4ac9-9af7-e1bf579c229b",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "20173062-bc95-4086-ab7c-b1ac8466ebf6",
   "metadata": {},
   "outputs": [
    {
     "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",
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       "    }\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>title</th>\n",
       "      <th>level_2021</th>\n",
       "      <th>points_r1_2021</th>\n",
       "      <th>level_2020</th>\n",
       "      <th>points_r1_2020</th>\n",
       "      <th>points_r1_2019</th>\n",
       "      <th>level_2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TU792</th>\n",
       "      <td>Community and Youth Development</td>\n",
       "      <td>6/7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DK712</th>\n",
       "      <td>Digital Marketing and Public Relations</td>\n",
       "      <td>6/7</td>\n",
       "      <td>206.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CR035</th>\n",
       "      <td>Community Development</td>\n",
       "      <td>6/7</td>\n",
       "      <td>133.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CK791</th>\n",
       "      <td>Medicine  - Graduate Entry (GAMSAT required)</td>\n",
       "      <td>8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LM101</th>\n",
       "      <td>Medicine  - Graduate Entry (GAMSAT required)</td>\n",
       "      <td>8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              title level_2021  \\\n",
       "code                                                             \n",
       "TU792               Community and Youth Development        6/7   \n",
       "DK712        Digital Marketing and Public Relations        6/7   \n",
       "CR035                         Community Development        6/7   \n",
       "CK791  Medicine  - Graduate Entry (GAMSAT required)          8   \n",
       "LM101  Medicine  - Graduate Entry (GAMSAT required)          8   \n",
       "\n",
       "       points_r1_2021 level_2020  points_r1_2020  points_r1_2019 level_2019  \n",
       "code                                                                         \n",
       "TU792             NaN        7.0           101.0             NaN        NaN  \n",
       "DK712           206.0        7.0           100.0           147.0        6/7  \n",
       "CR035           133.0        7.0           100.0           162.0        6/7  \n",
       "CK791            60.0        8.0            57.0            58.0          8  \n",
       "LM101            57.0        8.0            55.0            55.0          8  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Last 5 rows\n",
    "top_2020.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff384f24-99a0-4d06-9506-4e6ec689f38c",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cabec8a2-2354-4382-bbc7-cf60611d4b67",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "663bc23e-2c3a-4f59-903c-51984b81d95d",
   "metadata": {},
   "source": [
    "- Only 2 courses require a GAMSAT this time. We can see from `top_2019.tail()` further back up the notebook that RC101 and DN401 aren't in the 2020 points.\n",
    "- There was a points drop for DK712 and CR035 from 2019."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82c65631-9344-4716-82c8-0ac24b86d64c",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "640e146b-e4fd-459c-8c49-6bab99dd3c33",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "aee0e28a-9249-47a3-9d6d-658f6ff98d18",
   "metadata": {},
   "outputs": [
    {
     "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>points_r1_2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1344.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>337.772321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>112.153311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>252.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>309.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>419.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>613.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       points_r1_2020\n",
       "count     1344.000000\n",
       "mean       337.772321\n",
       "std        112.153311\n",
       "min        100.000000\n",
       "25%        252.000000\n",
       "50%        309.000000\n",
       "75%        419.250000\n",
       "max        613.000000"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overview of 2020 points. 100 lowest & 613 highest points\n",
    "stats_20 = allcourses[(allcourses['points_r1_2020'] >= 100) & (allcourses['points_r1_2020'] <= 613) ].describe().drop(['points_r1_2021', 'points_r1_2019'], axis=1)\n",
    "\n",
    "stats_20"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6054d951-de72-47fb-a7d0-690d33a82303",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e689f75f-8675-4283-9e07-927864fc65cc",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f12a0d6d-a231-4b0c-89d2-189adee04a1a",
   "metadata": {},
   "source": [
    "All these stats have increased from 2019. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a7dd7e3-a388-446c-bf1f-31382a555d65",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "398d9bfa-cc38-4bb9-ac68-06735c5b4269",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7ebe7c1-9fc9-4520-806f-83206b731c0e",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49ebd1f7-2ae6-4a7e-9858-c08f622cee63",
   "metadata": {},
   "source": [
    "### 2021"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0afefa3-f498-450f-b8e8-95dc2ae38a7a",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "6dd9cf1c-2573-461a-bc26-b7fb6aade23a",
   "metadata": {},
   "outputs": [
    {
     "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>title</th>\n",
       "      <th>level_2021</th>\n",
       "      <th>points_r1_2021</th>\n",
       "      <th>level_2020</th>\n",
       "      <th>points_r1_2020</th>\n",
       "      <th>points_r1_2019</th>\n",
       "      <th>level_2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DN670</th>\n",
       "      <td>Economics and Finance</td>\n",
       "      <td>8</td>\n",
       "      <td>625.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>613.0</td>\n",
       "      <td>601.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR052</th>\n",
       "      <td>Dental Science</td>\n",
       "      <td>8</td>\n",
       "      <td>625.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>613.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CK702</th>\n",
       "      <td>Dentistry</td>\n",
       "      <td>8</td>\n",
       "      <td>625.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>613.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR034</th>\n",
       "      <td>Management Science and Information Systems Stu...</td>\n",
       "      <td>8</td>\n",
       "      <td>625.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>613.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TU795</th>\n",
       "      <td>Visual Merchandising and Display</td>\n",
       "      <td>6/7</td>\n",
       "      <td>618.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>481.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LM100</th>\n",
       "      <td>Physiotherapy</td>\n",
       "      <td>8</td>\n",
       "      <td>613.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DN230</th>\n",
       "      <td>Actuarial and Financial Studies</td>\n",
       "      <td>8</td>\n",
       "      <td>613.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>601.0</td>\n",
       "      <td>577.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DN440</th>\n",
       "      <td>Biomedical  Health and Life Sciences</td>\n",
       "      <td>8</td>\n",
       "      <td>613.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR080</th>\n",
       "      <td>Global Business</td>\n",
       "      <td>8</td>\n",
       "      <td>613.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR072</th>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>8</td>\n",
       "      <td>613.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>555.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GY303</th>\n",
       "      <td>Biomedical Science</td>\n",
       "      <td>8</td>\n",
       "      <td>613.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>556.0</td>\n",
       "      <td>533.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CK703</th>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>8</td>\n",
       "      <td>613.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>577.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RC005</th>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>8</td>\n",
       "      <td>613.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>579.0</td>\n",
       "      <td>555.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR015</th>\n",
       "      <td>Philosophy  Political Science  Economics and S...</td>\n",
       "      <td>8</td>\n",
       "      <td>613.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>566.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CR700</th>\n",
       "      <td>Theatre and Drama Studies at CIT Cork School o...</td>\n",
       "      <td>8</td>\n",
       "      <td>609.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   title level_2021  \\\n",
       "code                                                                  \n",
       "DN670                              Economics and Finance          8   \n",
       "TR052                                     Dental Science          8   \n",
       "CK702                                          Dentistry          8   \n",
       "TR034  Management Science and Information Systems Stu...          8   \n",
       "TU795                   Visual Merchandising and Display        6/7   \n",
       "LM100                                      Physiotherapy          8   \n",
       "DN230                    Actuarial and Financial Studies          8   \n",
       "DN440               Biomedical  Health and Life Sciences          8   \n",
       "TR080                                    Global Business          8   \n",
       "TR072                                           Pharmacy          8   \n",
       "GY303                                 Biomedical Science          8   \n",
       "CK703                                           Pharmacy          8   \n",
       "RC005                                           Pharmacy          8   \n",
       "TR015  Philosophy  Political Science  Economics and S...          8   \n",
       "CR700  Theatre and Drama Studies at CIT Cork School o...          8   \n",
       "\n",
       "       points_r1_2021 level_2020  points_r1_2020  points_r1_2019 level_2019  \n",
       "code                                                                         \n",
       "DN670           625.0        8.0           613.0           601.0          8  \n",
       "TR052           625.0        8.0           613.0           590.0          8  \n",
       "CK702           625.0        8.0           613.0           590.0          8  \n",
       "TR034           625.0        8.0           613.0           589.0          8  \n",
       "TU795           618.0        7.0           481.0             NaN        NaN  \n",
       "LM100           613.0        8.0           589.0           565.0          8  \n",
       "DN230           613.0        8.0           601.0           577.0          8  \n",
       "DN440           613.0        8.0           590.0           565.0          8  \n",
       "TR080           613.0        8.0           589.0           565.0          8  \n",
       "TR072           613.0        8.0           590.0           555.0          8  \n",
       "GY303           613.0        8.0           556.0           533.0          8  \n",
       "CK703           613.0        8.0           590.0           577.0          8  \n",
       "RC005           613.0        8.0           579.0           555.0          8  \n",
       "TR015           613.0        8.0           589.0           566.0          8  \n",
       "CR700           609.0        8.0           740.0           768.0          8  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 10 courses <= 625 points\n",
    "top_2021 = allcourses[allcourses.points_r1_2021 <= 625].sort_values('points_r1_2021',ascending=False)\n",
    "\n",
    "# Filtering out courses which contain '('\n",
    "top_2021[~top_2021.title.str.contains('\\(')].head(15) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7747848-e09f-4162-ac9b-5a99e121156e",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "937528e0-43ef-4d84-b9e8-97b3b3c55535",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb75a060-d598-4f53-b68d-4aacb6d127c8",
   "metadata": {},
   "source": [
    "Some observations:\n",
    "\n",
    "- For the first time a level 7 course makes the top 15. Just to note TU795 has additionally requirements and isn't totally points based. It increased by 24 points in 2021.\n",
    "- All but 1 course - TU795 - have ran over multiple years. \n",
    "- The top 4 courses are the same as 2020.\n",
    "- CR700 (has additional requirements) has a big drop in points, 159 points since 2019. \n",
    "- All courses which have no additional requirements have increased again."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7807115-6dc9-475b-9769-38d116488d2e",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d79426e-1998-44cc-80b7-cb7b8236b3d9",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "c1b05245-98ee-41c0-b916-effb0de670e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f081633550>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating a barplot \n",
    "top_2021[~top_2021.title.str.contains('\\(')].head(15) .plot.barh()\n",
    "\n",
    "# Labeling y-axis \n",
    "plt.xlabel('Points')\n",
    "\n",
    "# Adding a leggend\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bed00f5-5516-45de-915b-d08e834a14ed",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "821a895a-a4cf-4324-b731-dbcdaa9141e8",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c61940c-fa14-4ba4-9a41-5674a5a2688d",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "d7dd7883-131e-43d3-8e2f-76f57d4d2a36",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "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>title</th>\n",
       "      <th>level_2021</th>\n",
       "      <th>points_r1_2021</th>\n",
       "      <th>level_2020</th>\n",
       "      <th>points_r1_2020</th>\n",
       "      <th>points_r1_2019</th>\n",
       "      <th>level_2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DB576</th>\n",
       "      <td>Film and Creative Media</td>\n",
       "      <td>6/7</td>\n",
       "      <td>101.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TL780</th>\n",
       "      <td>Early Childhood Care and Education</td>\n",
       "      <td>6/7</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6/7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DN401</th>\n",
       "      <td>Medicine - Graduate Entry (GAMSAT required)</td>\n",
       "      <td>8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CK791</th>\n",
       "      <td>Medicine  - Graduate Entry (GAMSAT required)</td>\n",
       "      <td>8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LM101</th>\n",
       "      <td>Medicine  - Graduate Entry (GAMSAT required)</td>\n",
       "      <td>8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              title level_2021  \\\n",
       "code                                                             \n",
       "DB576                       Film and Creative Media        6/7   \n",
       "TL780            Early Childhood Care and Education        6/7   \n",
       "DN401   Medicine - Graduate Entry (GAMSAT required)          8   \n",
       "CK791  Medicine  - Graduate Entry (GAMSAT required)          8   \n",
       "LM101  Medicine  - Graduate Entry (GAMSAT required)          8   \n",
       "\n",
       "       points_r1_2021 level_2020  points_r1_2020  points_r1_2019 level_2019  \n",
       "code                                                                         \n",
       "DB576           101.0        7.0             NaN           100.0        6/7  \n",
       "TL780           100.0        7.0           234.0             NaN        6/7  \n",
       "DN401            61.0        8.0             NaN            59.0          8  \n",
       "CK791            60.0        8.0            57.0            58.0          8  \n",
       "LM101            57.0        8.0            55.0            55.0          8  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bottom 5 rows\n",
    "top_2021.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccf374c5-97cf-4c68-88f0-0411a40476d5",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e3d7484-0263-451c-8a68-175341c200cc",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ee36ff1-d02b-4903-8ac1-c6bdd1d46620",
   "metadata": {},
   "source": [
    "- There's a big drop in points for TL780 - 134. It will be interesting to see if the course is offered in 2022."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bdb5523-a12e-4e03-9535-9ee531b77159",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e77400a-c975-4e52-b165-68a1fe05cac6",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ede4b28-7385-4da5-90a5-50257495047b",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "5f2055d2-6d2e-4f06-b8f6-0b9b31705140",
   "metadata": {},
   "outputs": [
    {
     "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>points_r1_2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1177.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>353.434155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>120.612747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>261.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>321.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>451.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>718.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       points_r1_2021\n",
       "count     1177.000000\n",
       "mean       353.434155\n",
       "std        120.612747\n",
       "min        100.000000\n",
       "25%        261.000000\n",
       "50%        321.000000\n",
       "75%        451.000000\n",
       "max        718.000000"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overview of 2021 points. 100 lowest & 625 highest points\n",
    "stats_21 = allcourses[(allcourses['points_r1_2020'] >= 100) & (allcourses['points_r1_2020'] <= 625) ].describe().drop(['points_r1_2020', 'points_r1_2019'], axis=1)\n",
    "\n",
    "stats_21"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49f156f5-9450-41f1-9e3c-1fbd66f5acc1",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83f68534-d64a-4762-99fb-faab42d2ab06",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "666ff2f6-8bb0-422c-bab9-e8a6ad58c621",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1624828e-6bfc-44e7-870a-2c5556aa8004",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e964195-1a2c-41f6-9ddf-51ce1e174a02",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33431950-02e9-4acb-a4f6-eb0f0938883f",
   "metadata": {},
   "source": [
    "### Histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b9c8dec-05c3-4ffe-8e9a-3985182a5562",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a734e39a-7fd2-4bc3-be2c-09eba0138eac",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "4341c640-d58a-4ec0-a690-09abd72c8fa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f081b64460>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating a histogram for points \n",
    "top_2021['points_r1_2021'].plot.hist(label='2021', bins=50, alpha=0.5, color='orange')\n",
    "top_2020['points_r1_2020'].plot.hist(label='2020', bins=50, alpha=0.5, color='blue')\n",
    "top_2019['points_r1_2019'].plot.hist(label='2019', bins=50, alpha=0.5, color='green')\n",
    "\n",
    "# Adding a legend\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1e2d956-5fe7-4dc6-b5d5-3ce35341214c",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3437a0d-4ade-4101-9901-9beb0005e85e",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "23a6da1a-c327-4174-ba13-1c071b401c93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021 points skewness: 0.335913\n",
      "2021 points skewness: -0.806062\n",
      "******************************\n",
      "2020 points skewness: 0.422401\n",
      "2021 points skewness: -0.668812\n",
      "******************************\n",
      "2019 points skewness: 0.374804\n",
      "2021 points skewness: -0.350403\n"
     ]
    }
   ],
   "source": [
    "# Getting skew for 2021 points\n",
    "print(\"2021 points skewness: %f\" % top_2021['points_r1_2021'].skew())\n",
    "print(\"2021 points skewness: %f\" % top_2021['points_r1_2021'].kurtosis())\n",
    "\n",
    "# Formatting\n",
    "print('******************************')\n",
    "\n",
    "# Getting skew for 2020 points\n",
    "print(\"2020 points skewness: %f\" % top_2020['points_r1_2020'].skew())\n",
    "print(\"2021 points skewness: %f\" % top_2020['points_r1_2020'].kurtosis())\n",
    "\n",
    "# Formatting\n",
    "print('******************************')\n",
    "\n",
    "# Getting skew for 2019 points\n",
    "print(\"2019 points skewness: %f\" % top_2019['points_r1_2019'].skew())\n",
    "print(\"2021 points skewness: %f\" % top_2019['points_r1_2019'].kurtosis())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff072928-6cc4-4130-ae4a-2bab34cca521",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcc3c0fd-f9e2-4b27-9fab-c851abb139c3",
   "metadata": {},
   "source": [
    "The skewness of all years is between -0.5 and 0.5 so we can say it is normally distributed. \n",
    "The negative kurtosis suggests the data is less outlier-prone and this makes sense when you considered we have filtered out the majority of outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb20a08c-0a1c-4fac-aaa3-766cf3e7e204",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48916044-2f65-44a2-81eb-045de2b2d920",
   "metadata": {},
   "source": [
    "As we know which courses are level 8 for sure, we can check how these points have changed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d514649-52a4-441f-80da-21d86c6cb026",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4073a217-c90e-4d4b-84fe-8fa3137b523e",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "f074736b-d1ed-4cf5-bedb-e68b2b3c9cc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Level 8 Course Points')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating a histogram\n",
    "allcourses[allcourses['level_2021'] == '8' ].plot.hist(bins=50, alpha=0.7)\n",
    "\n",
    "plt.title('Level 8 Course Points')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1310cf1-697a-4675-b4f4-5018f56d4384",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99edfa26-a3b5-4f06-9cd3-6d4e6c0e2b62",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db581b84-4028-4ace-b96b-c2bbc0be4ca8",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0112714-1455-45e4-8883-f94545ab0056",
   "metadata": {},
   "source": [
    "Some observations:\n",
    "\n",
    "- The majority of courses require in and around 300 points.\n",
    "- There is a rise in points across the board.\n",
    "- The implementation of accredited points from 2020 seems to have had a big effect on the number courses which now require 450+ points. \n",
    "- Compared to 2019, the number of courses requiring 500+ points is over double.\n",
    "- 2020 pushed up entry points requirements across the board and as a result the requirements for courses on the high points side increased to reconcile the inflation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3642b5e8-f98e-4e64-af8c-5423cd2e3c0e",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2925ba04-08a8-43f0-b404-e4d161177129",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03b10120-0002-43e2-be11-738686aeeed3",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1bb3928-291b-47e6-83b8-256821439e3b",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb0bb966-cecf-493e-9e70-93008de83afe",
   "metadata": {},
   "source": [
    "### Boxplot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ed93d55-8bac-4bca-9c9b-affd1307e99a",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "675c4870-b9a0-4ce4-ab09-97077db0c6b0",
   "metadata": {},
   "source": [
    "Let's have another look at the distribution of the points now, we have removed major outliers i.e. points which exceed 625 points."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0dc5d42-316d-421a-a9e1-73545003864a",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb98924f-75e2-49bd-8ef7-0156655218cb",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "8de81e0f-20bf-4a6c-bac2-e8852af2e4ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating boxplots\n",
    "\n",
    "# nrows, ncols, the first plot\n",
    "plt.subplot(1,3,1)\n",
    "(top_2019['points_r1_2019']).plot.box()\n",
    "\n",
    "# nrows, ncols, the second plot\n",
    "plt.subplot(1,3,2)\n",
    "(top_2020['points_r1_2020']).plot.box()\n",
    "\n",
    "# nrows, ncols, the thrid plot\n",
    "plt.subplot(1,3,3)\n",
    "(top_2021['points_r1_2021']).plot.box()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6133ad8c-1c5a-4770-81fa-32d366ca1b21",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b73a15eb-fc7d-47c7-a0bf-882100a31949",
   "metadata": {},
   "source": [
    "Some observations: \n",
    "\n",
    "- No major outliers, which makes sense.\n",
    "- The median (blue line in the box) has increased with each subsequent year. \n",
    "-  The mean (box) has increased over the years too. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab1d5620-9cf3-42ed-b413-5e006349ba7e",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88b31497-a393-49c7-a966-b1fba59f71bb",
   "metadata": {},
   "source": [
    "Let's work out how the mean has changed over the 3 years."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2b6a674-070b-4757-9ddd-6b3ebfbdf480",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "eeb3a1d4-8289-444c-948f-c12418ddc165",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting average points for each\n",
    "average_2019 = top_2019['points_r1_2019'].mean()\n",
    "average_2020 = top_2020['points_r1_2020'].mean() \n",
    "average_2021 = top_2021['points_r1_2021'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31fa96e6-f2a8-47aa-8e6b-7899e86b0057",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a815520d-a529-4b70-9f3c-2f3347df7c5b",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ff9f1fa-dca9-4033-9e97-e0644dee52d1",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "94bbc1de-4e2c-4e54-9844-403ba553b517",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "An increase of: 3.501528290730442\n"
     ]
    }
   ],
   "source": [
    "# Getting the increase\n",
    "avg_increase = average_2020 - average_2019\n",
    "\n",
    "# Getting the %\n",
    "increase_percent = (avg_increase/average_2019)*100\n",
    "\n",
    "print('An increase of:', increase_percent)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9856874-900c-47b0-86a9-857c9ecdaed2",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4bc5695-1edf-4692-8b91-8ae5b35e0a71",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "25a026df-5940-4f9d-bb5f-a17c571e4b57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f0828013d0>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating an figure\n",
    "fig,ax = plt.subplots(figsize=(15,8))\n",
    "\n",
    "# Generating a scatterplot\n",
    "sns.scatterplot(x=top_2020['points_r1_2020'],y=top_2019['points_r1_2019'])\n",
    "\n",
    "# Adding a title\n",
    "plt.title(\"Average Points 2019 vs 2020\")\n",
    "\n",
    "# Adding lines to show average for 2019 and 2020\n",
    "ax.axhline(top_2019['points_r1_2019'].mean(), linestyle=\"-\", color=\"black\", label='2019') \n",
    "ax.axhline(top_2020['points_r1_2020'].mean(), linestyle=\"-\", color=\"blue\", label='2020') \n",
    "\n",
    "# Adding a legend\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b687891-d03e-4267-bfae-1d878e10b545",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41dc8195-dee5-4ecc-b2c1-3db37afc4420",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a3e2623-9a21-4432-a26a-b340122cc90f",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ceb639d-871d-4d1a-9f97-9065de1f6b55",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c851fbaa-479e-400e-99d8-89c9402972f7",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b671041-da57-4851-af91-3f607290a012",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "48e7e00c-c288-43c3-b595-42496737e4af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "An average increase of: 3.8598273466896362 between 2020 and 2021\n"
     ]
    }
   ],
   "source": [
    "# Getting the increase\n",
    "avg_increase = average_2021 - average_2020\n",
    "\n",
    "# Getting the %\n",
    "increase_percent = (avg_increase/average_2020)*100\n",
    "\n",
    "print('An average increase of: {} between 2020 and 2021'.format(increase_percent))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c552fad-65d1-450d-aa3b-080e8e77dda9",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceadc202-5553-478c-95b0-d480716c0a3a",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "d0ced7e8-fae7-4448-9cda-2308eb4bde18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f08286c310>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tr6+PlStX8tnPfpa/+7u/A6C5uZnW1lZaWlpobW2lqakpu/2BAwe45JJL2LRpE3PmzAHMDN/Bgwez2xw8eDCbMRRCCCGEEEKIUmSN3zAzDIOrrrqK448/ni996UvZ1xcuXMjTTz8NwNNPP80nP/lJADo7O/n85z/Pddddx1/91V9lt29pacHj8fDKK69gGAbPPPNMdh8hhBBCCCGEKEUCv2H229/+lh//+Mf86le/4txzz+Xcc8/l5Zdf5vLLL2fHjh2cffbZ7Nixg8svvxyARx99lD179nD33Xdnt29rawPg1ltv5ZprruHss8/muOOO42/+5m9G89KEEEIIIYQQ44SaQL3gjAMHDuS90NXVhcvlGqXhjJ7RuO7m5uZsgCpEhjwXwoo8F8KKPBfCijwXwspoPhc1moa37SCpjiM4GqcSa55On66PyliszJgxA8CyEMikWeMnhBBCCCGEEINVo2nU7/xv2h64HaO3F1Vbi/+ya+H0vx5TwZ8dmeophBBCCCGEEAPwth0kkg76AIzeXiIP3I637eAAe44NEvgJIYQQQghRRZpSuBOduPfvxpPoRJMWXBNCquNINujLMHp7SXWMj+nIMtVTCCGEEEKIKtGUwvXmTqL33JSdDui7ch1dJ52OPnFqa0xKjsapqNravOBP1dbiaGwexVGVTzJ+QgghhBBCVEl9PJIN+sDMCEXvuYn6eGR0ByaGLNY8Hf9l16JqawGya/xizdNHeWTlkYyfEEIIIYQQ1RLpsJwOSGcY3A2jNChRDX26Dqf/Nc033Ueqow1HY/OYq+pZimT8htn+/fu58MIL+djHPsYnPvEJtm7dCkA4HGbJkiWcffbZLFmyhEgkAsCOHTs477zzWLBgAeeddx6//OUvs8e67bbb+MAHPsAJJ5wwGpcihBBCCCEGoPyN2YxQ9rXaWmgIjNKIRDX16TodjS10Hv9eOhpbxk3QByOY8QsGg35gK3AqYABfBN4EngLmAHuAYCgUCqe3vw64BEgBV4ZCoRdHaqzV5HQ6ueGGGzjttNOIx+Ocd955nHPOOYRCIT760Y/yla98hfvuu4/777+f66+/nsbGRr7//e9zzDHH8MYbb7Bs2TJeeeUVAM4991z+6Z/+iY9+9KOjfFVCCCGEEMJKl8eP78p1xWv8PH6QNX6jTlPKnHYb6UD5G+ny+CfN2suRzPhtAn4eCoXmA2cArwPfAF4OhUInAC+nvyYYDJ4CLAHeC5wHbA4Gg44RHGvVtLS0cNpppwHg8Xg44YQTOHToEC+++CKf+9znAPjc5z7Hz3/+cwBOPfVUjjnmGABOOukkenp66E1PFzjrrLNoaWkZhasQQgghhBDl0A2DrpNOx7txK7719+DduFUKu4wRmcI7sTUriN64muiaFbje3Dlpqq6OSMYvGAz6gHOAfwQIhUJHgaPBYPB84OPpzX4A/DuwBjgfeDIUCvUCu4PB4NvAB4FfD3YM69b5+NOfaga7u6VTTunjppuiZW+/d+9e/vCHP3DmmWfS1taWDeJaWlpob28v2n779u2ceuqp1BZMFxBCCCGEEGOXbhgk3A39a/ok6BsT7ArveDduNd+vCW6kpnrOA44AjwaDwTOAV4DVQEsoFDoIEAqFDgaDwWnp7WcC/5Wz/770a+NWIpFg5cqVrF+/Hq/XO+D2b775Jrfccgs/+tGPRmB0QgghhBBCTHCTvPDOSAV+TuD9wBWhUOi/g8HgJtLTOm1Y5VuLflUSDAYvBS4FCIVCNDfn99BobW3F6TQv8ZZbugY38gENfAv7+vq49NJLufDCC/nMZz4DwNSpU2lvb6elpYXW1laam5uzYz1w4AArVqzgvvvu4/jjj7c+q9P+vLW1tUX3Yrg5nc4RP6cY++S5EFbkuRBW5LkQVuS5EFYG+1wYyV5iFn346qYdQ/0keM5GKvDbB+wLhUL/nf76GczArzUYDE5PZ/umA4dztp+ds/8s4EDhQUOh0IPAg+kvjba2trzv9/b24nCM7tJAwzBYvXo173nPe1ixYgXJZBIwC7Vs27aNr3zlK2zbto2FCxeSTCbp7OzkH/7hH/jGN77B+9///uz2hexeB/O6C+/FcGtubh7xc4qxT54LYUWeC2FFngthRZ4LYWWwz4VWU2dZeCdcU48+QZ6zGTNm2H5vRIq7hEKhQ8DeYDB4UvqlBcCfgOeBL6Rf+wLwXPrPzwNLgsFgbTAYnAucAPxmJMZabb/97W/58Y9/zK9+9SvOPfdczj33XF5++WUuv/xyduzYwdlnn82OHTu4/PLLAXj00UfZs2cPd999d3b7zIN98803c9ZZZ9Hd3c1ZZ53Fd77zndG8NCGEEEIIIcaNyV54RxkjdKHBYPB9mO0cpgC7gH/CDDxDwLHAu8DnQqFQR3r76zFbPiSBr4ZCoZ8NcArjwIH8pGBXVxcul6uKVzE+jMZ1y2/khBV5LoQVeS6EFXkuhBV5LoQVeS7spTN+lmVKR6yPXygU+l/gAxbfWmCz/QZgw3COSQghhBBCCDF5ODUNb/tB9PY2tKapxJqOITmOmrAPxYgFfkIIIYQQQggxlCbqmlIYh/bhbj1Y8b5OTcP15530vrsLpRvw7i5cx86l68TTJ0XwJ4GfEEIIIYQQYkRkmqgXFlgpZ61dZt8jg9gXwNvZRl/rAeI/eax//6Ur8bbMIOxtrNYljlkjUtxltIzU+sWxZrJetxBCCCGEGNvsmqjXxyPDui8AXQmi2x7K33/bQ9CVGMyljDsTOvDTNK1k24OJKJlMomkT+m0VQgghhBDjVakm6sO5L2B0JSz3NyZJ4Dehp3rW1dXR09NDb28vSlkWt5lQDMNA0zTq6upGeyhCCCGEEEIUUf5GlEUTdRoCw7ovgAo0We6v/E0VXMH4NaEDP6UU9fX1oz0MIYQQQgghBNDl8Vs2Ue/y+GGA5UpD2Rcg1tiCf9UaIls2Zvf3r1pDrKkFJkFxlxHr4zcCivr4iZEj/VSEFXkuhBV5LoQVeS6EFXkuJqZsVc/OMDQE6K6wqmegr5uew4cq3hcK2zk0E2uaPqEqeo6JPn5CCCGEENU2lLLwQojyVPvnTDcMEu4GcDeYL1RwLKUURncXjp5uNLeHHqUq2t8AdE3D0DQMzcFk+ttCAj8hhBBCjEtDKQsvhChPtX/OhhJEOjQN92u/oa1gqmbitA+SKiNrl9k/PMj9xzsp/yiEEEKIcWnIpd2FEAOq5s9ZJoiMrVlB9MbVRNeswPXmTrQyizB6wq3Z9XmZsUS2bMQTbh2R/cc7CfyEEEIIMT4NsbS7EKIMVfw5G2oQabS3WbdjaC9vHehQ9x/vJPATQgghxLiUKe2e91oFpd2FEAOr6s/ZEINIrWmq5VhUU/OI7D/eSeAnhBBCiHEpU9o980Eus/ao2+Mf3YEJMYF0ewM0Xn8HvqUr8F10CY7pswb9czbUIDIWmIZ/1Zq8n3n/qjXEAy0jsv94J+0cRFVIuWVhRZ4LYUWeC2FlsM/FUMrCi7FP/r4YXVaFXfxXrCVx8pmDKoZSjUIxDk2jobON5JFWVFMz8UBLRWNxaBqecCtGe9ug9h/rpJ2DEEIIISakoZSFF0KUZrUmL3LvzXg3bjV/7iqkGwZdJ52Od+PW7C9rKm0NkdJ1tLknEvU2pg9aWdCW0nU6G6ZCw9RB7T+eSeAnhBBCCCGEKFZqTd4gAj8Y+i9rNKUwDu3D3XpQendWSAI/IYQQQgghJpFye+ll1uTlBn9DLaCkKUV9T5wpsSh6ZxjVPI1YYFpZ0y01pXD9eSdHNuVMFV29jq4TpXdnOSTwE0IIIYQQYoQNpZH5UM9b7jq7TAGlom09/kFNq9aUwrXvL2jtR2h/8M6Km6i74p3EH9uC96JLcPgbUfVukkcO4ZoVI17vqXg8k40UdxFVIYuvhRV5LoQVeS6EFXkuhJWJ+lxUo8jJYLkTncTWrCjK4tmt26tmASV3ohPnwX2E776x6PyNG7YQaSjdVsF36B303W8TfeKB/vu2dCVaywziJ5wmWT9KF3eRdg5CCCGEEEKMoKE2Mh+SCnvpZdbkJWbMIeFuGFpwFenA6E5YN1FvOzzg7s6a2mzQl9kvuu0hku/uGpl7N85J4CeEEEIIIcRIGmIj86EYai89TSnciU7c+3fjSXSiKcvkkv25XW7L82v+gc+f6gxb3jelGyNy78Y7CfyEEEIIIYQYQUMNvoYis24vt4l5uQ3ZM1NUY2tWEL1xNdE1K3C9ubPs4K/L48eorcV/6dX5TdQvvZqjHt+A+6umqZb3zdDUiNy78U7W+ImqmKhz8MXQyHMhrMhzIazIcyGsTNTnYjTX+GXOP5h1e5WuD7Ti0DS8bQdQvb3osQg4p5CqraVr1nsGHIND03D/4bdENt+Wt8aPWcfRNWe+rPFDGrgLIYQQQggxZlSjkXlVGIZ1hGCnCn39DMOgr96NM5nCqHOjN02jy1Pe2sGUrpM49a9ovvVBUu2H0erd9Pn8dLm8Zd+70aqmOhZI4CeEEEIIIcQIG2oj88HSlMK1+w30XW+idAND03DNO5GuuQNnzIba188u08lJp5c9fsMwUPX1JGumoDzeioO+0cy0jjZZ4yeEEEIIIcQkUd8Vhf3vEP/JY0SfepjYT34I+98xXx/AUNYHwtCrmWYCtyNfXT6oNYajWk11DJCMnxBCCCGEEJOEMxYlvO2hopYIgRPfC/XekvsOeYrqEKeK2gVuZa8xrMJU1fFMAj8hhBBCCCEmCaOny7qPXnd3WfsPZYrqUKeKDjVwG/L5xzmZ6imEEEIIIcQkoTe1WLZE0JumDfu5hzpVdKhtMIZ6/vFO2jmIqpio5ZbF0MhzIazIcyGsyHMhrIzmczFRqz9qSuH6806im3IKnKxeR9eJY7uVRGbfoRZnGcr5xwNp5yCEEEIIIUSZJnL1R90w6DpxcOv0qhEMD2WqaGaN4dS7H6Pn8KFBtcEYrWqqY4EEfkIIIYQQQuQYchGRMa7c4Cc/0GuCIweJ3rl2VINh3TBQx8wi4awrOXZRTAI/IYQQQgghck3w6o/lZO4ss55LVzLlIwvw/u2nMOIxlNtNQ7SDaEMTKV2v6vlF9UngJ4QQQgghRI6Rrv44koFQudNYrbKe3W+8hvtDH6P9pq/lBYPuY2aQOOH0soK/iTyNdqyTqp5CCCGEEELkGMnqj5lAKLZmxaCakld6Lm/kMMa+3XgXX4yjucW+iblF1tP3d39PZMvGoh6AyXd24Q0fLvv8k7mJ+miSjJ8QQgghhBA5htyovAIjtZ4wE2CGczNtS1YQ3/4MqbbWommsVllPPdZpOQVW6QZG22FoaB7w/Ml9uyf0NNqxTDJ+QgghhBBCFMgUQEnMmEPC3TB80xBLrSesIssA88mtuBYsspzGapX1dNj0ADQ0heYvPQ02c350Y0i9+MTgSeAnhBBCCCHEKBlqU/Ky2QSYStMsp7HmZj196+/Bu3Ersakz8K9akz8FdulKnLPnctTjK+v8iZdfwLdkxaRtoj6aZKqnEEIIIYQQoySTWSsqduLxV7VVgV3BGudZHybmn2aZ0Sxq+6DrcNoHab7pPvTODlS9Gx2dVEqnu85TcryZ86faWolvfwbP4mUoTSt5flFdkvETQgghhBBiFBmz5xBYdzeBb92Fb+PDw1Lh0mrqpv/L15GaUl/RcVKGwdF6N8rlRe/uhnov3bOPH3C8uedPtbUSf/YJ1Ky5EvSNIMn4CSGEEEKISWuke8qV0xSdk06v+jh1w6B7/hk0rrsbI3yEvv3vEn1sC3osgm/1OrpOHDjY1JTCtfsN2P8O7dse6h9zGftnp47e8QjOeBSjuwu9qaXs8Yuhk8BPCCGEEEJMSiPdU86uKbrm9ZPqbe2v6HnHI+a0ynSQ1+MN4Hr990TuvXlI46xLdEJvN7HtP8b76SC+f1iJqncTe/5J6r907IBVROvjEfRdbxL/yWP5RWI23UTjhs1Ey8jeqb178iuLSg+/ESNTPYUQQgghxKRk10phuHrKWZ5v20O4FizKbqN5/Tj2vJXX18+79+1s0DeUcTr7jqLq6vEsPJ/wppsI33Mz4btvxPO3i1A93QMfINJhtm6wKBLT98qvB+w/ONL3W+STwE8IIYQQQkw4mlK4E5249+/Gk+i0DkhGqJXCQOdT9I/Nde6nidx/a15wdPTNP1jv1xke+BrTNKVg/7v0vvYKkQfym7BHHvwOU8poGK/8jaBp1i0ddH3gIG6k77fII4GfEEIIIYSYUDJTKnOzZlbZqBFrpTDA+QxNZf/snD2vKDiy633nVAx4jRn18Qjh+2+xzdilwm0Djr/L40fNOxHf0pX57RiWrKDr5e0DBnEjfb9FPgn8hBBCCCHEhFLulEKrSpfD2VPO8nyr16E+/Ilsrzx9xrFFwVFix4v4V3w9vyLnV64nsuX28qdN5mTbrIIvrbF5wPHrhkHX3PkY7/8wTevvwbd0BZ7Fy4hvf4ZUW+uAQdxI32+RT4q7CCGEEEKIiaXUlMKcAia5TcrpDENDYFirepY8X70XMLOVRX39Lvoi8Refy/a+qzn5dJIOJ6mD+wa8xoxMti3TQD365Nbs8f2XXUusabpZUKaE3Mqiff4m1LyTiBVUJC3Vf3Ck77fIJ4GfEEIIIYSYUOyalVtlo4qalA9zEFJ4Pg1wJzrz2jQUBkcJb4D6OSdkv456/LjiEeuG7G437v27i1o+dHsD+FetIbJlo9lA/YLl1Myag9Yyg1hgGskygr6iiqSr15kVSMPtZQdxI32/RT+Z6imEEEIIISaUkZhSWFbxmDKOYbUWESDhbiAxYw4JdwMpXc/72qTwr74B39KVOJpb0g3Zv0Hnd26wXPNXFwsTfeoRPIuX4V7wKdB1Oh9/gKPOmgGDPrCZPrvpJtD17Lgkcze2ScZPCCGEEEJMKMM9pbBa/f/s1iJ6N2617alndW7/iq+T7E6gd3dDX5/1sSIdpA7uo+vl7bgWLEKhcH/yAmq6u/C1H0bVu0h5fHS5fNbXUOb0WTF2ScZPCCGEEEJMOJkphcORjapaP7pBtDewOndk612QiBN9dFNeT8DcYyl/I47ps/AsupD4s0+QePkFFDodN15J+NtXEV7/VfifX+Pa/YZl9lIqco5/EvgJIYQQQohxpRrTLIdkiP3oMuN3TplSeTBlc27nMTNxHvseao47Ht9Fl+C76BIc02dlj9Xl8eNfdW22qItrwSKi2x4qaiav73rTMoCVipzjn0z1FEIIIYQQ40a1plkORSXFYwrljl/z+vEtXZkNwMqpjGl37mSsE895nyW8aX3/FNBVa0h4A6Dr6IZB0qC/pQPKuim8blhO35SKnOOfZPyEEEIIIcS4UbVplkMwlOxX7vhTba3EX3gazwXLCXzrLrwbtw4YwFqee8kKVDJJ5ME786eAbtlIXSwnC9kQyMsw2jaTtwlgh3P6rBh+kvETQgghhBDjxxgoMjKk7Fd6/I7mlmyRFXSDVF2dWYSljHYIXSedTtO3vsvR117BwCC+/RncCz414H3JBI3Re24y+/kVZhuXroSZx5XMOIrxSwI/IYQQQggxbgxlmuWwMAwqWWGYLbKy8Pz8Juqz56C1zC4reNQNgz6Pj9izjxfdh1L3pTBgNQJNNLzvQ6j2w6j6epIeH912VT3FuKeMifPGGgcOHBjtMUxazc3NtLW1jfYwxBgjz4WwIs+FsCLPhbBi9VxoSuH6806im/IbiXedOPg1fppS5lTRnCbqpY7l0DTcr/+eyL03o3n9uM79NDWz56I1TSPV243ua6TLYz0VUlMK3+G99L35JxwNfpTbA04nyf3vMmX+6USbjiFVoq+ephT1PXGmxOPokXY0XwMk+9D7jmIkEkTuv8V27WP2OuNRaurqSCYS43Ktnvx9YW/GjBmA9e8iJOMnhBBCCCHGl5opeC5YjtINc01azZRBH6rSYjGaUnj376IjHfR5Fl2Yl7nzLVlB/KXn8CxfZRmMKqXQjxwm9mT+FEtVW0/4uzfiu+iLJE77oGXwpymFa99f0NqP0P7gneb5P/W57HRNx/RZNF5/h1nEpSCgy1xn/PEteBaeT0fumEe4OI4YHVLcRQghhBBCjBv18QjRO9cS27aV6FMPm/+/c+2gi7tUWiymPh6h7/Wd/S0R0gFUdt8nt+I6ZyHRTdbH8IRbiWy+tbiNQjSM65yFRLZsxBNutT23o7s7W8SlsCVD6uA+OjZcg9EQKCq+Uh+PEH98Cw3LVxWPeYSL44jRIYGfEEIIIYQYczSlMA7tK+7VN8QeeoVUZ9i6rYHd8SIdoBuo2lr7lgiZ1y2OYbS32bZRyOxntNtMY4x0YHQnBmzJYHkv4lE8C88nuXd3Ve+fGD8k8BNCCCGEEGNKZlrika8uJ3rjaqJrVuB6cyeaUtniLrmGUtzF4fZYHs/hdltur/yNJHa8iG/JCtA065YIGLZj0pqm2rZRyOynmpptz61c7gFbMlidt6aujuiTW7NBazn7iIlFAj8hhBBCCDGmlJp+adnH7uqbUaji7GAZkj3dZh+8gr54yYKsWEaXx49n+SriLz2HUVuHf+XXi/bt2vES/su/adnXLxaYhv/L38jfZ+lKNF/A3G/VGuKBFttzp+rq8V96Naq2NtuSIfdYDaut+wkmE2amMPHyC8XXW2YPQjG+jVhVz2AwuAeIASkgGQqFPhAMBhuBp4A5wB4gGAqFwuntrwMuSW9/ZSgUenGAU0hVz1Ek1ZWEFXkuhBV5LoQVeS5ELvf+3URvXF30um/9PSRmzOmvTtkZhkATat+e/CqfFRQr8SQ6iW+8Dtc5C82pkxh07XgJ95pbzb56FjSlcMU70ToO45hSy9G3XodUEufM48ChSLUdwTj1/STqvZb7OzQNT/gwhNvRXG6oqyfVGUb5GogHWsqs6hlFj0VxBJowenowerpINR9DwqaaqCfRSXTNivwegpqG86wPE/NPG1eFXeTvC3ulqnqOdMbvE6FQ6H2hUOgD6a+/AbwcCoVOAF5Of00wGDwFWAK8FzgP2BwMBh0jPFYhhBBCCDEKBprOqRsGCXcDiRlzQNezQR/0Zwe9rfvKyv5lM3jPPkH0qYeJP/sEnuWrSmbAlFI4kr3Q14dR56Jm/mmQStK35y06H70PGhrpdvls9zcMg6SzhmTNFPrcXjobmonOPp7Ohqklg77stde6CTdNp3POSXQ0NBNumUXkuBOJue178OVmSlNtrcSffQI1a+64C/rE4I12O4fzgY+n//wD4N+BNenXnwyFQr3A7mAw+DbwQeDXozBGIYQQQggxzHJ76dHYjG/1uuIsnscPhUGKTbGX5M7fEXv28QGzf5pS1Hi8+C+7FlXnou/IQTTM4K7oXIBT0/DufZujb/4BdIPo9+/Fd9EX4aPnUtPZge/E96I3WU/VzJwv01bBdc5C0DR8J59BbObcAYM+q3tVTt9BKG7ePh7794mhGcnAzwBeCgaDBvC9UCj0INASCoUOAoRCoYPBYHBaetuZwH/l7Lsv/ZoQQgghhJhgLHvpXX0zzd99jN4jh1ANjRiaon7frqJAJ5MdzA3+MgVWstm/jVstp21qSuH60//Qft+GvD580ce20HDVejobppa3/VOPELjsWtpvuWbA6aaZtgqehefn9f/zX7GW+Pz3lQxQM83XHZEOIvfeXPHU1kymlMy9kKBvUhnJwO/sUCh0IB3c/SIYDL5RYlurnHzRkxkMBi8FLgUIhUI0N1tXQBLDz+l0yv0XReS5EFbkuRBW5LmY3IxD+zhSWMzlzrVM2/QEdae+n+Rvf0n4uzdkA53A19bjOGYmqcMHcUybQeBr6/O+71uygvj2Z7LHqumK03zce6zPmw7iMuvejKO9NHz+yxCP0fyek223z47zya14Fi/j6Bs7i6abTr37MdQxs/KOkTyyz+zzV9BLL3LvzZbbAxi6nr0HnsXLiDz7RFnnmqjk74vBGbHALxQKHUj//3AwGPxnzKmbrcFgcHo62zcdOJzefB8wO2f3WUBR5ZZ01vDB9JeGLPIcPbLIVliR50JYkedCWJHnYuwbzPTCcrlbD1pP1+w4Ql9PN7F0UJd5PfzdG/BcsJzYtq1mtmz1Oho2PoxqP0zy7T8R3/4MqTazCbqqraXP5SFi8Xx5Dh/KBn2eRRfmZ+C+cj0d7e1512g3TqVpGAXTNI3eXnoOHyLhrMs/p8trbm9xHKvtAdyJzuw9sOvdZ7fvRCR/X9hLF3exNCLFXYLBoDsYDHozfwYWAn8Ange+kN7sC8Bz6T8/DywJBoO1wWBwLnAC8JuRGKsQQgghhMiXmYoZW7OiqK9eNdgVc3EEmmzX8CndyP45sukmtGQv8eNOQJs1Fz0WyR6jVKuCTA8/14JFxRm4+zaYgW4Z45xy0ml07Xip6HWr3nhdHj81J59RWS+9gnsgffjEYIxUVc8W4JfBYPBVzABueygU+jlwG3BuMBh8Czg3/TWhUOiPQAj4E/Bz4PJQKJQaobEKIYQQQogcpfrq5dKUwp3orLifnmVvvivXwbQZtsGWkbMKyOjtxWhvyytg4lt/D96NW0uufcv28LPJwNEZHnCc/q9cT3z2e/BcvKqs3ni6YRCbORf/FWvL7qWXew+kD58YrBHr4zcCpI/fKJKUu7Aiz4WwIs+FsCLPxdg2UF89sCnQUkE/vbzefA0Buj1+Gpua6GhvLz7u0pXEX3g6bzpnYMPmomIsA8n08Gv4/JcJ372+KKtmVRQm28Ov/TCqrp6U10dXunVD4fhLXbfV9ZYq7JJ7DxzTZ+FfdS1JA4wyzjXRyN8X9kr18Rvtdg5CCCGEEGKMs6ucmTu90C4raFdRs5BdxcnCNgRaQyPqwDt50zn9q9aQCLTgjoVRnWEcbg/Jnm6Ux2e5FtFsoN6KEe3E9w+X0vnDzWZ1zpw1frbtIwD2v0M03Y5B5bRjqKRiZiUVNq1aMURzr2sSBX1i8CTwE0IIIYQQJWWmOBZl83IDI5u1eHSG+4ObQSoMkhwnnUFgw2aM9jZUUzNdgRY8h/ejerrQe7rhaC8qFiN+/614Ll6Vl3V0aBru135DeMvGbPYs8KWr6atz0fTt+0iFO1BNU4kHpqFb9NUbbDuGQoXFcrq9AepiYdviOdKKQQyVBH5CCCGEEKKkcpp/l5MVrJaUrpvTOhumoimF5y9/JHlgL9FtD/UHYl+6Btf5/1CUdfSEW7NBH0Dq4D7C37sT3z9cmt+fz26aaqTDth1D44bNRP3TBgz+rKZu+i76IpH0uCqdJitEOUaquIsQQgghhBjHMhmnxIw5JNwNRQGJXYGW4So6kikk4z2yn+Tut7JBH6QDse/dQe2M2UVFWoz2tqLMpOuchUQK+/NZFK+BdIBrUwym75Vfl1XtNJs1XLwM30WX0LD8y9mgb6DzCzFYkvETQgghhBBDVk5WsFoyGbP441vMCpe6YRmI6bFIUdZRa5panJksVdWzYJpql8ePL92OoTC7aeg6sXLWNcajeVNFfUtXDNs0WSEyJOMnhBBCCCGqYqCsYLVkCsm4zllI6tB+0DTr3nbOKUVZx1hgGv5Va/IykzVzTyi7N55tO4YlK+h6ebtlG4hCNXV1eVNF0Q3pzTcGDLYdyXghGT8hhBBCCDG+pAvJKBSJXzyPJ/hP+JauzF/jt+obpGYeR1edOy8ATek6idM+SGDDZjhymL49bxF9+gcVVfVM6Trx+e+jccNm+l75NYauE9/+DKm21rICtmQikZfhy/TmK7uqqKi6obYjGQ8k8BNCCCGEEONKbkNzPRYhHnoU998vp+kbt6H39KB5G+jzNxKvddkGbp0NU/E4pxC/+0Y0rx9daQRW34DR2402ey5R/9SSH/h1wyDqn4Zr1lxipaqdWmkI5E0VTbW1En/pOQIbNpNMJIZ1mqywNtR2JOOBBH5CCCGEEGJMybQ6SB7Zh8flLQqCMoVk4o9vwbfsMqJPPED0e3dmm7unnE66jpk9YLasy+PHd83NsO8dotse7M8WfvkbqMC0kvtn2zF4vAQ2bCHZ2wPu4rHanregPYbn4lXE/NPQG6Q336gYxnYkY4UEfkIIIYQQIo+mFPVdUZyxKEZPF3pTC12e4jV7hb3ohpKlcmga3s52VCKK0dOD4XAQ3bYVPR7Fv+pakgZ5mbCuk07HveZWjESMwIYtGF0JHFOmkOpKoDdOK+ucumHg8DcRvmNtfkXQzbcR2LDZbBlhc38spwW2zC7r+nXDoHt+bi9C+76BYmSMZDuS0SKBnxBCCCGEyNKUwrX7Ddj/DuGcNXO+1evoOrF/vZNl8HP1zTB1OkakvaJA0KFpuN98leThgwXr9NagHz1Kx4ZrLNddZRqaa0rhaj1IZBDrs4z2I5aZHqO9DWwCv6FOC9SUov6NVwlP4PVk441VFnairbOUqp5CCCGEEGPEWKgqWB+PoO96s6gvXnRTfl+5wuBH8/ph/ztE11xC9MbVRNesKNnTLvdafe2HSLUeLO7Ft2UjeritKMCqj0ey+3sO7KGho9UyELPqg1d0j5umWVbUVE3N9jep1LTAMtgFjtK3b/TktiPxrb8H78atEy4Ql4yfEEIIIcQYMGaqCkY6bPvi5a13Kgh+XAsWFQeLNlkwq2v1r/qG5TmVbhS9RiKGa/+72f3L7YNneY+vuRn/l79BZPNteZnGeKAFbKZeDnla4CRYTzYe5WaRgQmT6cuQjJ8QQgghxBgwVrJAyt9o3xcvJ7DJrawJoFBlZ8GsrjXVesDynIamil5z1tbm7V9uHzzLe3zHWvS5JxHYsJnAdRsJbNhM4rQPkiqx3i4zLTCvj19Bv8BSCu+d3XiFqCYJ/IQQQgghxoIhTh+sli6PHzXvRHxLV+YHNqvzA5vC4KecYDHL4loTv3ge/8qv550z8JVv4sxprq5qa/FfsZaUTR+8AQMxm3tsxCIknVNI1daRck7BGCDTM9RpgUMNHIUYDJnqKYQQQggxBoyVqoK6YdA1dz71LTMInPhejO5u9KZpRVU9c4MfOsMQaMI3ew7RTQMXx7C6Vj0WwTHj2P5efL4GjHoXKecUGr91F31//D2GrhN94nv4V62x7oO3eh3JvbtxnvVhszVCGed1TJ+FI9xO5KavVTTFdijTAovunfTtEyNADfQbjXHEOHDgwGiPYdJqbm6mra1ttIchxhh5LoQVeS6EFXkuRmaNXzXbL5Q8fjqY6bY5vuW1rl4Hs+aitR3CoWmEv3cnqYP7sr354i88TaqtFTCDNd+yLxG59+b+/ZesIP7Sc3guXmV7z6zO23jDdzn62v9k1xImXn4BPRaZUI27Jxr5+8LejBkzACwrKkngJ6pCfgCFFXkuhBV5LoQVeS5M5QZORduXEcgNFFgWHqvbG6AuFh6xIPGoN4CndS96uA0cTuL/8gK17zkJhQJNw6itI/b9e7P7+zZsBrcX1RnG6fagdyVQDicpr48ul6/kfcieN9CEY89bRO6/NT+A3P4MniuuJz5jTtWuV1SP/H1hr1TgJ1M9hRBCCCHGiEqmD1aaISzVe67HG8D9+u/zMmj+1etItR6Enm4MTcM170S65s6vWvCXe60OTcP92m9o37Kx//yXXUs09Gg26+df8XUczS2k2lrNKbBuLwl3A5rHX9F9yD2vO9GZDfqy9+TJrXguWI5TmfdYpl+KiUKKuwghhBBCjEO2gVzrvmwPwNyedY72w9btEjrDePfvygZ9YPbk0w8fJPbkQ0SfepjYT34I+9+hviuat3+1+g56OttIth7Au/hifBddgub1E3ngdlznLMyOM7L1LlwLFhUVQhlSNVSbYi/OY2YS2XK79NUTE4pk/IQQQgghxiOboCW583fEnn3cXDNXM4XonWvTve5WWhaPcbjd9L3y64F78m17iMCJ74V6L1C9NYmaUhh7dxP/yWNF0y1Vzow1o7cX57yTzAqaudNOh9ATz66gTqr9CKmD+6SvnphQJOMnhBBCCDEO2fWCMzCbr0c33YS+681sUJP4xfPFLRquXEeyp6eoD55dTz6juzv7dbX6DtbHI9nm6dnjPLkV17mfxqA/gFS1taRaZpBw51cXHUpPPMu2CktWkPh/P5a+emLCkcBPCCGEEGIcsgtaul7eDqSncer9AVKqrZX4C08TuOHuvN5zyuMjsePFvD54dj359KZp/S9Uq++gzXFqZh1H146X+q/Nps/dUHriZdoq+DY+TODaW/BcsJz49mfQYxHpqycmHJnqKYQQQggxRpWq2pnbC87RfoTk/j0YySTuBZ8CILHjRQwtf82dHouQdHn62xQYBl0eP56LVxF/fAuexctQmkbNaWfSMHsOnZvy2y10eRqyBWdyp0k6mlvS6+/qmFLvgta9KI+vrEqgdtMt9ZlzaVp3Fz2HD5XsczfUnni6YRB3+9BOPI36GbNxzz9d+uqJCUkCPyGEEEKIYTTY3nlWa+j8V6wl5W/MC6oS7gYc3gDuWIRIblXMVWvQc4Iqu4bqmcDJvebWbOAU8wao8zQQWHc3Ro91A/cujx/f1TejH9yLs95NZOtdxT31vrgapk7HiLTbXnsmY1d0nU4HqXAbqowgbCjN1KtxjErbagxnL0Uh7EgfP1EV0k9FWJHnQliR50JYmajPxVAKoLgTncTWrCjKhHkWLyP+7BN5x7Hb1rfxYXOdXJl9ASsZs6YUrj/vRM8pzJJ7bu9Fl6A0LVskptS1OzQN7/5d9L2+E0PX6drxEp5PLib+wtPZaZfVbGRfTZW8x9UqiDPZTdS/L6qhVB8/WeMnhBBCCDFMKi2A4tA0GjqP4Nv1Os7ODjyLl+G76BJ8F12Co7nFDBbShVfyjmOzTk7rOGxmlsoM+ioZc308QnTTTSjdsDy3I9BYXBnU5trrYmE6NlxDdNtWYk89QurgPqLbHsK1YNGgi8aMlEre42oVxBFiMGSqpxBCCCHEcKmg1UCmiXl4y0Y0rx/fkkuIP/tE0fTJTKXL3OPYrZPTkn1EH9xYWdas3DHnbGd1blXrKr/Ngs05M+0cym3PMCoqaScxhNYTQgyVZPyEEEKISahajbdFaZW0GvCEW7Nr9FwLFhF56DtFLQ78/3hFtmpn7nG6PH78V6wtqvAZefTeirNm5Y45s13i5RfyKoJmzp1say372ku1pii131hQyXs8lNYTQgyVBH5CCCHEJJNZZxRbs4LojauJrlmB682dEvwNg0paDRjtbf0ZNJs+en373yGVDqhyj6MbBil/Y3ZqqGfxMuLbnyF1cF9x1qxKY85sp8cixLc/g+eC5QS+cRtN6+/BUA4MXce37LKyrt3ynEtX0vXy9oraM4yGSt7jobSeEGKopLiLqApZZCusyHMhrMhzMfrsCoF4N27tL/M/wibyc5Gt4jhAgRV/Zxsd16/C6O3Fd9ElxJ59vOg9CmzYTDKRsDyOJ9FJ1KYYTOypRyp6j8sds9V2rngkO45smwdNw3nWh4n5pw1c7bIzjGpoxNAUNfEofS5P2esTR0u596vSbYW1ifz3xVBJcRchhBBC9KtW420LY3kK6WiNLdMmoGfWPGqSfXjf/iOB9kN4ehN5Y4gFpuFftaZ/+uTSlUWZoZh/GokZc0i4G2zbIthmzVaXn1nKjNnuXKW2y4zDMX1WNuirOfkM4oGWsloyJGbMIe72kaj34jz5jJLnzxjt567c+1XptkJUkxR3EUIIISYZu0IgQ11nNJZL1Y/m2DSlqO+J43j7dTpy++xdejWupql0zXqPOVVT10mc9kECGzZjtLehprbQ8L4PoXd2lNVQPLeRueoM43Q4OLrrTdwLPmU2cq+ZMqzXmTuO7vln4Fv2JSL33jzs93u03lvpxyfGG5nqKapCUu7CijwXwoo8F6NvuD4oD2UK6XA/F4MZWzU+2GfutUPTCN99Y/HUza/eSHL6rOwYqhVMjPZ03mqdv5znInMuzes3M4wo0DT48MdJ1HsHfQ2ljOVfckwG8u+IvVJTPSXjJ4QQQkwyuZmhzDqjgQKMsgKSsVyqvsKxVeuDfaZvm/9L11ie3zjaP4ZqnNOpaXjbD6K3HyHw1RuJ/eQx+t7604DXm+HQNDzhVoz2NrSmqcQC00jpuuX9Kfk8DOJZsDpmWSIdaF4/nkUXEn1ya39GdfpMtFPOGpZAzK4f32iukxViIBL4CSGEEJNQZp1R9kP4AEFfOQHJcE0hrYZKx1a1D/bpAEjVuy3P7/D6SAWaqnJOp6bh2vnftD9we8500quIA31v/WnA9yK3j2B2/1VrSJz2wbzgr5znodL7bXdM40MfG/C6lb8R17mfzgZ9mXsXuf9WfBsfJu72DXiMio3lX3IIYUOKuwghhBCiJLuApLAn3FguVV/x2KpUACcTAMV+8hj+L3+jqNdd+KG7ULox4DnLKV7ibT9IJB30ZfaNPPgdvJ9dZgZxV6wt+V7k9hHM7r9lI55wa9525TwPld5vu2NyeOBlPF0ePzWz51reO6398ID7D4b04xPjkWT8hBBCCFFamdmNwUwhHSmVjq1a2ctMABS95yZS8RiexcvMHn0YZp+9tlaMzg5w++zPGWgqK+Oq5/QBzMh87Vm8jFSgqeR7Ydjsb7S3QcPU/hfLeB6y9/uOR3DGoxjdXehNLfY3yuaYqXA7TJ1pv1/6XASaLe+dqq8vue9g5b6vee+Jx18yey7EaJLATwghhBAlVRIEVTKFdKRVMjarD/b+y69D1zQ0pcoOZnMDTmdXgvD61bb30S6YQDfKmgKqNU+zfJ80j5f4s0/g/fAnSo5Va5pqHTw1NedtV8nzoPbuIVzGmkW7YzrS02BLjlsp8/1Z+XUiD93Vf66lK0l6hmGaJ2P7lxxC2JGqnqIqpLqSsCLPhbAiz8X4MxIVDEfjuRioQImmFK54J9qBd0nu3UXXL36KHovgW70OY9Yc6GirqPJmOffRqrl3/b5dRG9c3d8MPVOw7/SzSEyb1X/sPW/AvneIbnsoe/zA5d8k1dON85iZxGbOtSzUkpFZ4xfZstGskHnup6mZdRzqmFnE/FOz+5b7PHgSUaJrLimrsqfdMWs/9DHaOzrKuqeZMTuPmUmqox016zi65s6XYGwCkn9H7JWq6imBn6gK+QEUVuS5EFbkuRifrAKSan6gHunnotzgxa4tgeeC5cS2ba04CB7MffQkOolvvA7PwvPzq1ZesZb4/PehG4ZlSwNDU1DvIvbIPdkG7l0nlh6nQ9Pwdrajv7uLyJbbbIu8DHQdmlJ4/vwa4du/WXQO3/p7SMyYU9a9aWxqKvlc2L0/gQ2bifmnSdA3Qcm/I/ZKBX5S3EUIIYQQA8pMk0zMmEPC3TDuP1CXW7DGbu1ZpiCL7X5gWZBloPvo0DQaOo/g2/U6/s42HJpGl8ePf9W1xVUr7725/7zpcabaWok99QjRpx42A9NEon+cm27CFe+0vB+Zsdbt/Qsq2Uf0qYeLirx4w/2FUga6jvp4hNTe3RUVQBnUM2bz/iQTiXH/jApRbbLGTwghxolqNXYWQlB2wRq7tWcGhu1+mlLUd0Vx7HmLyP23lj091q6dgn78Kejd3SXHq/yN1Jx8Bt5PBzF6ulH1bmLPP1k0Tq39MBS0N7DMfi5ZkS0+k9nXOHIIzT+17IAs8Yvn8S1ZkZ+lvPw64lUsgDKWW4gIMdZIxk8IIcaBzAez2JoVRG9cTXTNClxv7rQs6S6EGFi55fgt2xIsXUnXy9st98v8rBq//rds0AelM4MZdu0UHHt3ZwuY2I23xxvAs/AzhDfdRPiemwnffSOehZ+h9y9v5m1vVeXSMvv55FZcCxblnwtVcvx5Y/M3oscixLc/g2fxMnwXXYLnguWk5pxQ1V9YjeUWIkKMNRL4CSHEOFD2tDQhRFnKDRhyqzf61t+Db+PDMPM49FjEcr/Mz6rSjYr7ANq2U+jpIvLoPfhXfD1vvP7Lr8ue1x1uLe7h98DteBZ8qn+cdlUu7aazalr/uS69ithPHiu7j2Hm/uqxCLGnHiH27ONos+bS7apulc3C98e7cWtViw4JMZHIVE8hhBgPypyWJoQoTyXl+AvbQGieBvv9cn5WK52CaNtOobaO1MF9pBKJ/j6AmsrLntkFjehJfBddYhZ6mXmcGXiV2UphyslnEFj9LVRtHbF/foLku38pewrlUNsdZKa2J4/sw+Pyltx3LLcQEWIskcBPCCHGAVnHIkT1lRMw2K2ttdsv87OaePmFovVtAzX4jgWm4V+1JjvdM5tp++cnULW1OFumk9zzNmga2rwT6coJ4uyCRkdTC0adG71pGl0e64Iplv0DV6+jr7eHaDqLOJgG5YMNyAbbPkTWQQtRmrRzEFUhZXWFFXkuqmck+qiNFHkuhJWRfC7yAoTGZtANjEh7UbAwmJ87y75ys+ehzzg2G3iVClAcmoY3fBijswOtppa+vbtxNE0Fh0Zq/17iLzyd7SWY25ohtwdff9B4NfGf/zPJd/9S1rgLWykAw9rCw45diwar/n+54x/ovouJQ/4dsSd9/MSwkx9AYUWei+oa7j5qI0WeC2FlpJ6L3ADBeex78H3uC/Ttfgt0g8SOF/FcvCobIA0mAMmcw+5ntdwm7q4/7yS6qaDK5kvP4Vl4PvHtz6DHIkXjcGoa3vaD6O1taF4ffeF2tFQSR0MjupECfzMxX6Pl3xvZMcej1NTVkUwkKp6eORhWQXCmYX0hu/5/QF4fQ8+iC4szrePwl2TCnvw7Yq9U4CdTPYUQYpyYzOtYZArXxFXJWq7c7XOfBaDs5yNTfEXz+vEsPJ+O76zLD64e34J7za3mz5pd0ZNy19YaRtGnL7tCTblBXH08kg36sts8uRXP4mXZ/8eeeqSohUTdG6/SnhtQLl1JNJMhXHYZTqXwHD6AfszsvCxYJhiNP77FvCe5QdPVN8PU6ZYZ0aGyC4KZPafyqe3p98q1eFFRv8PC+yvEZCWBnxBCiDFtIk1zFfkqfW8tt79uI05No+/1naAbxAuydrkcmoazrTUbIES23mUZXOX2xrMKQJyKbDP2iq+pnEJNdgEnKvv/wkDIMqDc9lA2SIw+8QCeC5abTd0LxpTZNxNYZo6hef2w/x2id64tmZ0c7C9l7IJg38aHi9ccDrC+MPNeZe5RyfsrxCQl7RyEEEKMadLKojRNKdyJTtz7d+NJdI6r3o6VvreF22teP1pbKx0briG6bSuxZx83p0I+vqXoGJk1cMm/vFkyQFCalg2oujx+/FeszW/5sGQFkS23lz3Gwmsqp3+g3TYGhvl/TRW3nigRLGb/rBuWY8rsW3hPXAsWEd32kO21DLm/qM2Yjc6ObEXQplu2lNWiIVOgBk0rqz+jEJORBH5CCCHGtlIZkkluyB+8R1ul723B9q4Fi4g8VJy1c52zMHuMTGDs2f0GqdaDdP/uP/EtWWEbINScfHo2oNINg5S/sb8B+eJlxLc/Q+rgvrLHWHhN5fQPtNxmyQq6dryE//LrUB/+RFEgVCpYLPxz4Zhy9809RsnsGUP/pUypIDgztd158hkk3AMXZ8m0j+DDHzfvkTR0F6KITPUUQggxpkkrC3vlrBcbyyp9bwu3HyhrZzntcskKun71b9SdvQD/iq9np3uq2lr8V6wlNnMeuq4DZtBYU1dHn6aBbtD18nZSba0VjbHwmsrpb5e7jeoM43C7Sfb24j7zQ8Qz2xYEQpYtGZauJP7C03l/thpTZt/441vyWlBkgmPb96dEkKt5/ANOAbUas/+KtaTiUTzp71dCNwwS9V60U84adP9AISYyCfyEEEKMaZYfaCvsJzZhlbNebAyr9L3t8vjxXX0z+q43UbqBc+7xloFJzcmnE00HHkWBcaY4yvfvxTF9Fk3fvo+j3d3QEDCDqpygz/XmTjrusaisuXwVhqbh3r+7KKgp55rKKdRUtE1GiSbmuQGlamgETcM3ex6OehepaBg9Fsneo9wxZfZ1r7kVIxEjsGEzqUQCI9CEb/ac/OqiOfvZBrmBprLWbhYGuE4FkS23kzq4L7uP8aGP2T0+tiZzISwhSpF2DqIqpKyusCLPhbAymOdiorSyqDZPopPoINoNjCWZ97amK06fy1PyvS1sc1Bz8hl4Fn6GSE6Tcf+qNXSd9kGSuo57/27rtgAXXULs2cfxr1pD4rQPkkoHe7nsWjk0fft++rriRG9dY9svzqFpeMKtGO1tqKapxAPTLM9R6n5Uu4JtuT9Dhefv9gaoi4Urak3BzOOIrrkEo7cXR3MLrgWLUJqG86yPEPNPtTyv3f2eevdjtDvrhnz9YmKRzxf2pJ2DEEKIcU1+g29tImRDM+9t83HvIdLWZjnuTDDiaD9Mau8eNK+fVG8rdaefRTT0KJ7Fy8xpnxhEn3oE95wTSJaoyjnl1DMJ/NVHSARaqIuF09MpPSR7ulEen3n/bLKpR3//X6jaOpzHvgfXRz5R1C+ue/4Z1L/xKuFBVKEdzgq25fwMlTy/xX66YdAz/wyavn2f2TuwaSqxpmOo3fuXbNCX11PvJ4/ZX4/N/U6F29GmzZJ2LkJUgQR+QgghxDhVznqx8UBTCuPQPtytB4s+2Nut04tvfwaFInVwn9nTLld6qqtdYNw5/TgAXG+8Wnzcl57Dc/EqmHmcdSuH2fPofGwzDctXES7stXfPTQQ2bM4GfXmvr7ublNtTVo/Bwa7ZHGq2sNLzOzQN12u/oX3LxryMq378yajaWrMqaJk99eyCdK1xqrRzEaJKpKqnEEIIMY5lMjmJGXPKqn441mQCuyNfXW5ZmdRunZ5rwSIgvwpl9uucQird888gsGEzges2EtiwhZ75Z1Afj+Bt3Wd93HMWmq+nWyYUVtbsfGwznoXnYxiGdSuC9jbL15M7f2de25934u6OWbffGEIFW6sKr55df8KbiJbf6qPC83vCrUTSQV9m28iWjTh7us17p2llH8+u2im6Lu1chKgSyfgJIYQQYtQMmGUq0Z8useNF/KvWZIOPwqmumlJF0y79q9aY00HP+aTtcY3eXgi303XS6Wbxl9//N4auE9/+DAD60V6cNTX4lq4k8YvnSbW1Amaw4gg0WWauDMxAMbrpJttG6lZZL8f0WTjdbstCMqXuo+b1ox/YS/Q768rOlFVaZdUuyNXbj5iZ6JbpqJ88Vtbx7LLXzsN7x3UBIyHGEgn8hBBCCDFqVGe45Ad7u2DEecYHcH/44yS8AXwbH0ZrP4yqqyfl9WW3swoqI1s24r3oEpzTZ9kGaLm95Pq6u0n84qe4FizC/amLcLjdeS0gMm0S9FgE35XrOJpK4Vt2GdEnHiiampoZg3PaMfguugSA+ONbcK+5lYTF1FTH9Fn4Lvoi4eu/PHDwZtHjsLD5evzxLTRctZ5kPG4ZRFa6ZlRrmmp5D1VTM7phEA9Mo/H6O+h7fSfoBokdL+K5eFV+YF4wNbVwHaIjYH0OaeciROUk8BNCCCHEqNCUwqlUyQ/2dsFIbNosdMNAMwzY/w4RizVgdtlCR9NUOn+4Oa9nXe4aP9/qnGAn0ITnU58juu0hPEsvJdlxBO/SL+GcdgzJA+9i9PbScM3NJGvr6PL4cXVFYUotnguW45w2neThg2bT95ysYPLwIWJPPZw9p5GIQXqabm7Wy+l2Z4O+zNjLXSOnUDiPfQ/ei/4JzeFE7+lB1dYS+c4Nee0ScoPISteMxgLTijKu/lVriAdacADu139Px7035/XoS8w/A13Xyy9kM236uC9gJMRYMaLtHILBoAP4HbA/FAp9KhgMNgJPAXOAPUAwFAqF09teB1wCpIArQ6HQiwMcXto5jCIpqyusyHMhrMhzMTFUo+2AO9FJYuN1eBaenxeA+a9YS3z++/IKvFi1ItCUwhs5TPKVX5sZpZdfyDZY927cigLLdheZqZa5rQamnHwGR3e9hdHbhTbvJLrmzkc3DLyJKJ1rLkHz+vFd9EWiP3mseLyXX0f8lLPQDQNPIprXyiCvqmVOhjA3EAxs2Exnw9Ti+2PXjmL9PSRmzCl6Pzxv/C+RdKDl+/I3cLjc6G2t2cyfqq3Ft+wy4s8/mXefhtL6I791RTPxQAuGYeA7sJuODdfYthqxa99QOJ7m5mY62tulnYvII/+O2CvVzmGki7usBl7P+fobwMuhUOgE4OX01wSDwVOAJcB7gfOAzemgUQghhBCjzKqQSG5BlrJFOkgd3Ed8+zN4Fi/Dd9EleBYvIxVosv5gbxjZTzOZMYSv/zLRbVuJPfs4nkUX4mhuyU4VtSoYErj8m6haV3aqZeypR4hu28rRP/4v0e/fQ2zbVqJ3rjXbR2gazmgY7+KLabhkNdGfPGYWfymoVBm5/9b+YiOd/VnGVFtr9toC122k8Ya784K+zP6pRCL7tUPTaOg8gm/X60xxuXFMn5V3C0qtkUucfCaN19+Bb+kKpkyfRXL/O0XTPaNPPJAtjGNVaEVTCneis+yCMIZhkHROIVVbR8o5BcMwqI9H6Ht9Z+nCLgMUksmMI/n6/+KKR+j2+MdtASMhxooRm+oZDAZnAYuADcDX0y+fD3w8/ecfAP8OrEm//mQoFOoFdgeDwbeBDwK/HqnxCiGEEJNFpdm7obYdyMhMT0y1tWZbMqjaWrwf/kTe2KymBBqz51hW5fQsXkb82Seya/QyUxdVZxingsiW2/unOqbX3umxCAb912v09kIihnvfHtpzpir6lqzASKVKrkl0uD15Uy5Tba3En32CwIbNJJ1T0GOR/HtQWwsNjYAZ9Llf+w3h3KmTl11LNPRo/vRMm2mOKV0nOmMu9b4A+sF9KN268qhKh8+FQWSlfQRt35sGP+hGySm8pQrJDGc/QyEms5HM+N0NXAvoOa+1hEKhgwDp/09Lvz4T2Juz3b70a0IIIYQYQCVZG8vs3e438Ni0AdCUwtF+eNBtB3LHZ8Sj+K9YW1TCv9vjz25rF2Q6Y1HroKa2Lu8YmXYXRkOAjg3XkDq4r/84T27Fde6n8S1dSdfL27PHUbW1OGtrs9Mmc7d3zjy2ZAuJZE83viUrsts4ps+i8es3YXS0g6bhW13QsmDpSjhy0JyqadUe4YHbCXztBnzr78G7ceuAwU/metXUFtA0y7FmCtiUe6/tWifYbV9TV0dix4t59yEzhTdzPrv2Dd0ef8XjEEKUZ0QyfsFg8FPA4VAo9EowGPx4GbtY/QtV9LdcMBi8FLgUIBQK0dzcPKRxisFzOp1y/0UReS6EFXkuhpeh6yR/+0vC370hmy0JfG09zr/6KEor/n2vcWgfRwraALD/HaJ3ri3aHyD521+SPHwQ79IVKN38pznx8gvosQh1046hfoD3tnB8jumzaFp7Jzhq0AKNMG0GdTnjTB7ZZx3gHe21zBhNOX4+6pQzqXPkrxCxO46zZQbU1WczcZnrVb29ltsnjxwqKgoT+Np6nLPnUq9pGMle2u+/Fc/iZah6Dw63m467+lsqNN1wN54LlpvZOIxsRdCpdz9GsqPduj1CZ4T6D5wNQH3Ju9tPDwRwHD6Ib+nKvDV+/i9dgzZtOlM/fl7Z97qmK07zce8pOofd9vT20vCFr9D5g/vM+6BpTDnlDNTJ76Mx530xPvQxps57jFS4HUegKTuepE0LB7txiMlH/h0ZnJGa6nk28JlgMPh3QB3gCwaDjwOtwWBweigUOhgMBqcDh9Pb7wNm5+w/Cyiq3BIKhR4EHkx/acgiz9Eji2yFFXkuhBV5LoaXO9FJLB1UgfmBOfzdG2ynYbpbDw7YBiCzP0DiB/fh+b9/Tyzdny2btZp5HOGaevQS722mGEs4Z3ypg/tov/lqpt79GO3OOujoyNvH4/JaBnjJQDP+K9Zms3KZqZjhLbdn2yOUdZzWA6gPfyKvkmXM48cVj9gElifTZxgENmwmlUhgpLfX0+PWaurwXLyK6D034Vm8jMi27+Xdy6OvvUJs29aie9Nz+BA1jc3W0x8DjYP6mXEHmnFOmULTN25D7+km1X6E6I8ewn3F9UQquNd9Lg8Ri/Pbbu/20t0yG/eaW7P3tNPjRw9bZISddTA1PakrPZ5KxyEmH/l3xF66uIulEZnqGQqFrguFQrNCodAczKIt/xoKhS4Gnge+kN7sC8Bz6T8/DywJBoO1wWBwLnAC8JuRGKsQQggxrg1QNKNQZq1V9utMA3Or/SMdZnGTdI+6zPei2x6CqdNLTkHMTClNvvJry+Onwu2W+9lNCezyNJDyN+YVhYlvf8acymlxrV0ev+U0S23eSXS7fCTcDXnFQ6y291+xlmjLbOLTZtHZMJW4RbGR3HWFznknFV1rZu1b3nuQniqaaY+Qd850e4TBUPUuwnespW391+jY+E06t37XzGza9MArNf2y0u0zU04rKchSyRRgIUTlRruP321AKBgMXgK8C3wOIBQK/TEYDIaAPwFJ4PJQKJQavWEKIYQQ44NV0QzH9Fk43W7c+3cXFW8p7JOXWRdmVXRDAWiaZeBmdHaA24edzLot7+KLLY/vCDQB1oVm7HrLKY+P2LNPlNXcWzcMuk48vb/Ze309SY+PbpfPPiipmdI/LVNT6LV1lNMGKxP0eFooutbEjheLM5Xpgi26rpM47YMENmzOa4+Q0vUSZ7NXaUP2Svv4Vbp9KYUFXRzTZ9F4/R3gcNLn9g76uEKIfmX38QsGg27gRMALxIA/h0KhROm9RpT08RtFknIXVuS5EFbkuRhemlK4dr+BvutNcw2e24OjwU9k8222FRLz+uQFmlD79hDdZNEQHfAd2EPHhqsH7L9WKNOTzrK33ZXrqP3QxwiHw1WpKlmN6o/l9pmzkrmfZiVRRWTLxryqnN3zz6AuFh6RvnR2PRDHmsL77WhuwXXup5ly4ntJBpol8BN55N8Re6X6+A0Y+AWDQT+wBbgAOAp0Aj5gCvBjzGxcpGqjHTwJ/EaR/AAKK/JcCCvyXAwvTSlcf96ZDdy8S1cQT6/HyxgogCkVLDg0Dffrvy/OWA0QbHkSndlm6rmN051nfZiYfxqNTU10v/OXioOtoQQ2pdpYVNI8vfCYhcGo/4q1pAJN4PaO2cBrtOXeb7tfDkg7B5Eh/47YG2oD90cwWzCcHAqFvOm1ej7glPTrj1RroEIIISau3IbM5TSGFqZKG2rXxyPZoA+w7eVWqvVCqfVZKV0nPv99eDduLbvFAOSvB8v0tlOz5hLzT+vft8L1iQONtRS7JvQOTcOd6MQ5ZUrJtg12rFoRRO69GdxeaT5eQu5aU9eCRdmgD6SdgxDVUs4av3Mx++115b4YCoV2BYPBy4BDwzIyIYQQE4Y0ZB6cQd03i+CpVCPtwcgEW2SycGWuextoPVippt6lVNqAHqwDtPjjW/At+xKRe29G8/qLWiGUWh+XHW9n2Lr9RLrBu7CWux6xZIEhuYdCDFo5Gb924Eyb770P6LD5nhBCCAFU3hhamAZz3wqrdCZefgHf0pVjokLiQNm5SqtKgn3mbsCMskWA7DpnYXYKa6qtlfgLT+O5YDmBb91VdmbT4fZYZgodbnfp8UxyedVQz/irQWVbhRCllZPx+ybws2Aw+DzwKv1r/M4APg1cNnzDE0IIMSGUmsInv8G3N4j7VljJUY9FYOZx+DY+bFbeHELlxXIMJvuWMZgqkXbB8UBFWCyziwUVS1NtrcS2mVNaE+6GsjKbyZ7uogbvviUrSBa2dRBFMr8Y0CqsRiqEKM+AgV8oFPpRMBh8FfgHzEbsHiAO/BH4SCgU+tPwDlEIIcR4N9gpfJPdYO5byeAp025hGIO+oU7prXgaaU5wnC0ag8LZlUArETRatTqoOfmMIT+nyuMj/tJzeBYvM6csYhB/6TncZ36o7GNMdrnPcE1XnD6XR6p6ClEFZbdzGAekqucokupKwoo8FyJD1vgNTrXu21CycJUcu8bjoeP6Lw+qBUIlf1/kn9NLx/Wr0Lx+y0qQ2dYJFtdeWBG0xxug/o1Xh3S/K33PhvO9mQjk3xFhRZ4Le0Nq5wAQDAZdwEnA26FQKFbwvbNDodB/VmGcQyWB3yiSH0BhRZ4LkSvzATfzG3wpa1+eofZhG86gu/DYvqUriG7bWrTdQC0QoPy/LwrbSTimz8J30RdJth6wbFvReP0ddGy4pv/ar74Zpk7HiLRbBlrV6HtX7jHkFyIDk39HhBV5LuwNqZ1DMBj8IPAO8O9AazAYvLZgk58NcXxCCCEmgcwUPufJZ4zbsvaVtlaohsG2K8gop0BM/nVFcXfHBrxGTSm8kcMY+3bjXXwxNSecgnP2PHxLV+C76BIczS1AeqpkoKkq901TCu/+XdmgDyB1cB/Rpx6h5j3zLddD9r2+M/u65vXD/neIrrnEthDMUO/3QMfIvde+yGEpeiSEGDHlFHf5DvDNUCj0UDAYfB/ww2AwOC8UCmWKukgjJiHEkMhUJzEeVHPapSveidbeiqp3kfL46HL5rLNO1fiZGKBAjOV1LV1J/IWn0WMRy2vM7BNO7+OYPgvfhZ8nvGl9XkGT+EvP4fniati3J9tbcChZrfp4hL633yi6ntTBfSiXx3J9nqHr2a9dCxZl2zNk7kM5hWCqxSpDKkWPhBAjpZx2DqcCWwFCodD/Ah8F5geDwceCwWA5+wshhK1Bl2IXYoRVoyWFphSuP+8kuuYSwt++ivD6r8L//BrX7jeyz3y1fyYK2ztAfsESy+va9hCuBYtsr7FwH9c5C4k8+J38Yzy5lYar1sPU6XkN5YeU1Yp04Jw+y/J6jLq6olYQ/ivW0rXjpf7tSvWHGwGF9w3dkLYFQogRU07g1gVMzXwRCoWiwHnp155BMn5CiCGQ/m5i3CiVOStTfTxSHARtewh915vZZ77aPxMD9sazuS6V/ufd8hoL9rELqJKJBEakvWrBlvI3kjxyCN+SFfnXs2QFye6ubCVI3/p7zCzeyWfiuXhVf3ClaaMbaBXct8TLLxRfyyj1WBRCTHzlTPX8D8xWDndnXgiFQj3BYPAzwI+B+uEZmhBiUpD+bmKcqEpLCrsgSzf6n/kq/0wM1BvP9rpygpHCa7Tbx+reqBLfq1SXx49vzglEtmy0bJdQ1ApC1/OvPdCEb/ac4mmnI9QfTvmb8u5Fqq2V+EvP0bhhC32J+LD3WBRCTG7lZPxWA88VvhgKhY4CnwU+Ue1BCSEmj4GmoQkxVgyYOSuD3fNuaCr7zA/Hz0SpYiNdHj++1QXXtXQlqt5trt1bvQ40La8wS+G9SOx4Ef+qNZb3ZqD7lil2knz9fwcs/KIbBrGZc/Et+xLxZ58g+tTDxJ99As/Fq2zfh7xrr/fSdWJ+VnAkK2gamjLvbc698HxyMX1TaodUTKYco1GYSAgxtkgfP1EVUlZXWCnnuZBy5pPPeP77oiqtFf68Mz/jtHQlzDyOrrnz0Q2jKj8TecVhGptBN2zbFwC4u2MYv/43lG5gYND18nb0WITGDVtIhtuJ3rm2aCxAUQ+8uljY8t7Y3bfBXGt+cRw3SY+XHneDZa8+qyI52XGPQjEp9/7dJO67JdtkPnOv3VdcP2C7i6EYz3/Pjue/L8TwkefCXjX6+F0K/CPwXsADxIE/Ao+GQqGHqjXQIZLAbxTJD6CwUu5zUY2+WWL8mOx/X/QHLodR9fUkPT667ap6DuJnIvMhP/74Flyf/CxOj4fIQ3eVbGpOZ5j4vRuyAQmk15+tWkPk9usGbMhebhVSh6bhCbditLehNU0lOaWO6DX/VHbDd8sAZvU6qJlSFJx2zz+juBl7iW3tmrxXkyfRSXTNirKvt1rciU5io3Deapjsf18Ia/Jc2CsV+A24xi8YDG4EPoXZ1uFVoBPwAe8Dvp5u7XBdtQYrhJh8itblSNAnJjDdMIi7feD29b9Y8MwP5WeiPh4h/vgWPAvPR+/tJrLtwaJCMYVNzRtvuBvPpz6XbXWQne7p9g643rDcbJJD03C/9hvCWzZmt/OvWoPm9ZPqbbU9fuG1FRW+2XQTnguW5/Xq0/ftxlNbS3LfnuzxrbY1enuJP74F37IvZXsDDmc2LDPtteheDfcaQ1lLLYSgvOIuXwROD4VCBwte/59gMPhzYCcggZ8QQogxY1L3hox04DpnIdEnt+JdfPGATc2N3l763vwjsScL+ttte4jGDVsGLMxiV4W0MJvkCbdmg77MdpEtG/FcsJzYtq22xy+8NtviOICjuQXPoguJPrk1v5/g9mdItbXmbZvhOmdhXkP44eztN1ChneFSlcJEQohxr5ziLgOt/pXVwUIIIcqiKYVxaN+wFpiY7L0hlb8RpWnZD/mWxWRympoD0NNt044hPnBBmzLbXBjtbZbb1cw8tuyCOSWL45Bu0J4O+jLHjz65FdeCRUXbZvfPuVelxl8tpQrtDJdqFCYSQox/5WT8Hgb+NRgMFk71PAP4OjBW1vgJIYQYwzIB2ZFhLjBRbgZqvMovbuIi5fHRlbNGsMvjx3fyGaja2myfuNwMmP+KtUSf+F7BQTXr9gwNAbpnzi2ZobLLJqmGxvxTNE21zjpNqSNww90ooM/lKZkBs5oq6b9iLbrHa57Tpp+gQmXX+Dn9AXysAN0gseNFatL3aiJnw0Yr0yiEGFvKLe7yJeDzFBd3+WEoFPpeqX1HkBR3GUWyyFZYkedC5BqpAhPu/buJ3ri66HXf+nuGtXLiSCinKiik19O9/nsi996M5vXjOvfTOGfPQ59xLN1ef3HRk6tvhr6jxf3tygjKKxrTa78hkrvG79KriP/LdupXXUtdTQ09rQcHrELq0DS8+3eZ01V1na4dL+FZvgpj1hyc8Rjh9auLnrHADXeTcnnhyMG8wi7+K9bSdfKZ1BXej3FS8XIykH9HhBV5LuwNuarnOCGB3yiSH0BhRZ4LkWukAjK7yomNGzYT9U8b1x/m7YJnzwXLUX/9sWwArSlFfVcUZyyK0dON3jSNLk9Df4uDnO8ZTdPQNQXRCDV1dSQTCTPTV0FGyK4dRGFQ79Q0vK17SbUeQNXWEftpCNcFF2eDTs3rLy4yUxCElfoFQrfHb1lopnv+GXjCrSRf+bWZ6Xv5BVJtrXn7laqiOqnXjI4y+XdEWJHnwt6QqnoKIYQQ1TBSBSYsKycuWUHkOzfguXhVfg+68fZBvlRxk3SFxtyMWFI36N75O3xLL8HV2QENjcVZr1VrSDz1CKmD+/oDpZlzy8r0Ze6hc8oUOn/xU1JtrfkbFVSNTOo6ndNmUe/yQGeY+i9dDSiiay7B6O3FtXhRNujLXFvRNN14FM/iZXltJ1JtrdAZRnc3FE1p7PYGqH/jVcIFz0Om4EtmP7sqquO5B54QQuQqp53DMcCjwEcwp3deHQqFfpXz/WgoFPLZ7S+EEELAyJWyz6xnCmzYTPKVX2PoevZDfvSem/BtfBiOHETf9aaZodI0XPNOzJuWOBZYZZnsgmdDU6iGAJpSuF//PR3pKpWO6bPwXfj5vNYNvqUr81ocxF98jsCqa0m1HUbVu4k9/yT1M48tOf3WMhhaupL4C09ngz+7oF43DDPDBhDpQEsl+wvR2K3RSweQmlI4Ih1Enn0iP4h76bnsuQpbYbhj4eI1n09uxbN4GfFnnxjwFw8Tfc2oEGLyKCfjtwk4AHwc+Bjw02AweEUoFPpR+vuTo0yaEEKIIckEZFPvfoyew4eGtcCEbhgk43GiOW0CwPzQrkXD6PvfIf6Tx/KClvqWGSTqvVUfy2DYZZm655+Bb/U66/V06emKua0JXOcsJPLgd4raNHgWLyP21CPUnHAKnr9dRHtOYOi/9CpSPd0l+7tZBkPbHsq2ZigV1Bdem2/pyrxg1iqwdbjd2fMWtV54ciuN199B1O4XCHZZUk0r7xcP0gNPCDFBlNPO4ePA5aFQ6PehUOhu4BPA7cFgcGX6+2Pn16NCCCHGNN0wUMfMGpFS9nal/x01U4qnE257CGc8OmxjqZRdlqkuFqbrxNPxbXyYwLfuInDD3Rjv/3B/trIgSClV5RLA+9llRYFh5MHvMEUb4He6NsGQ8/hT8K2/B+/GrbZTIQuvLfGL57PBX+LlF7J/BrIZvWTmXDbnTRrYPkt2z4HzrA+XNV3Tbv+JVPVTCDE5lBP4OcnJDIZCoZ2YweA3g8HgVcM0LiGEEGJI7HqXpboTlsGD0d09LONwaBoNnUfw7Xodf2cbDq2Mf3pLZJl0wyDu9hE99ng6p84kUe/NBi+2QUrB15ledkZvj+V5UuEOc9pootOy56LdeVJNUwcO6q2uTddpvPpmAl+7Ace8Ewlc9W0Cq7+F96JLzGmcbm/J8xolgjC75yBWZqEf6YEnhJgoypnq+QrwSeDHmRdCodDbwWDw48DLgHt4hiaEEGKiGokqiXa9y1zxTsvphHrTtKqeH/pbGIRzWxisWkPitA+SKmyinkP5m8rqjVeocB1lYseL+FetyWuh4Fu9DmPWHHzzT8dR77I+T9PUkgVNSq3X1ChdOCd3naKjuQXPogvz+gxm1grqsQi+pSvxfHF1djrmYNaJDrWHnfTAE0JMFAO2cwgGg38NBEKh0M8tvjcTuCQUCt00TOOrhLRzGEVSVldYkedCWGlqbKT3v/5j1KokWvadW72OrhOrf/6GziOEr/9ycV+5DZvpbJhqu5+7O4b6n1/ntzVYdhmOU88kGe0sGSxng+p0kNLjDVAXC1u2KrDsrbdqDfq8+USv+aeSPRcLz5PJgA1UATN3jZ9n8TK6dryE65yF/VU6d7yI65yFxJ56xNx/48PE3T7b66uk7YQYf+TfEWFFngt7I9bHLxgMbg+FQouqdsDKGB/5SO/AW4lhUVNTQ19f32gPQ4wxk+W50FJJSCZRTicph3TJGYjT0Ol7+3XQc/790RSOeSehj+D9y7xvOJ3Ddl5HdxfJd3cVve48dh6pepf92Hp70Pe/gxZoMl9QGsrhIHlon3nfNIVz5hxSrmpMulE4kkehrw/ldGIYBug6yXf+Unw9c45Hr62zH3cqSWrXm2W9t1oqierrg95ukocP9l/XtOkYKFKH9pd1TjGxTZZ/R0Rl5Lmw96tf1cII9fH7P1U+nhBCjGmOrgTJ/XuG4cP4BNbXlx8YgPl1MgkjGPjpDmfJ81UjoFfOGtBUUSCknDUD7OfESCVJHT4EgGPaMSQPHew/jm6Q3L+nSsGyQcpZg+PoUfr2vAW6gWPadMtx4yx9LpVK4WhuMYcYbsfIvNcW763ucOI0DPr2FlzX4YM4Z84p+5xCCCHKM6H+Nn3mmfbRHsKkZabc5f6LfBP9uXAnOomtWYEx0346nCjWlOzlyFevLDmNcDRVs2G378g+OLgvWzkz0y6B6SmiU2cOMIYj/S0PPv9loj/cXLRdw/p7iM+YU+klFil8lmtOOAXPeZ8l8uCdBWsTZ1iuTbS8Z+km6XosYvveeg7sofOGK4te9118GbGnH03f9+kylXMSm+j/jojBkeeilBm235lQgZ8QQowo6e81ONOmj0gj98GqZsNuhSL2L9sJrF6H0duDqq0j9tMQ3uWXldyvsKDIFJsiLJn+dkNW8CzXvf9DRJ/5AZ7Fy8yWEBhEn3oE95wTLO+B5T17ciueC5ajzZpr/942BCyvy3nKGWZLCFm/J4QQVSOBnxBCDFJudcLsa7W1ON1u3Pt3D1u1yoyRqIw5HONRmlaVKonDdv1VDOhTHh+uv/4/hAsaric9vpL7FV7b0e4ufEtW5Fe/zO1vN0SFz7JCkTq4j9hTj+RvaHcPSvT1ix93gu37YlelMzZtlrmPBH2TitXPtBCieqod+A3Q8VUIISYOqw+t/lVr6PzODaQO7hvWapXVnI44GuPRDcPMHGWCiEEEfcN1/XYB/WAadne5fLhmHofnguUo3TD75808jm6Xz/aara7Nf8Va4r/5ZV4GLv7Sc7jP/NCgrzNvnAXPMppW0T1Qjc14l65ApdfqJV5+AT0WIdU0tej9KPxw3z3fzO7VdMXpc3myH/bdic6yg/qx9ksQUTm7n2njQx8b7aEJMWFUu6rndaFQ6NaqHbAy0s5hFElZXWFlMjwXuaXlnW53NujLGK61a9k1WWNknVwl46nGczGc11/toLLS9gN219Z4/R10bLhmwDENNgjKG2egCbVvT37LixLnK2qPsXQlzDyOrrnz87YvdW8bm5poa2ur+P6PtV+CiMGxe+6n3v0Y7U6p6iryTYbPF4NVqp3DkDJ+wWDQAVyf6eM3ikGfEEKMitzMlXv/7rygD4qnCFYtMzHW1heO9HiG4Xy57w0zj8N7xyMQbredilrue2mV3dSUor4nzpRYFL0zjGqeRiwwzSycYnNtenc3gQ1bSPb2gNtrO6bBBkGF43ScdAaBDZsx2tvQmpqJBVrQLQq71Mcj2aAvM9botofwbXy46Jx26ycD6+7G6OtBq6mreI1lNddkilFk89ynwu1QohCSEKJ8Q53q6QRuAMZCA3chhBhVA00RrGZmoprTEathqOOpNCCu9vUP+N4MMcDKv74miHeitR6gvahq5gdtry25603id63Df/l16F7rgKaSIKjUPdeUov6NVwmXuD6HpuEJt8KRw3gXX0zi5RdItbVmz2t0doC7YC2jzYf75M7fEX72cXxXrgOPt7Kgfqz9EkQMit1z78j0shRCDNmAUz2DweAjJb7tBJaFQiFHVUc1OMZHPvKR0R7DpCWNNIWVyfhcOLriJPe/A7oOmoZz5nGkXB4gt7l1TtZE0wbdi63UuUZDOePRUkm0VArD4cjrjTeYa6nm9Vf63lS6feFYa447nr533i7a3zn3BFLOKTgSMZIH3u2/tmnTSbUfweg7ap6nuQVVW1d0vY7eHpJ73io6v3POCaQKmqAP9VkdcIwW98KRSpK0Om5zC6nDB9P34ESSu/9c/r21O2ZVehyKkWT1TCqfn2QyOdpDE2PMZPx8Ua5f/epXMISpnv8APAx0WHxvLAR8QggxZqRcHhzzTjIbVhc2/k725X84BfPrQTYuL3muUTDQeDIf6lIFgYaWSvZ/2APQdZL73xnwg3s51282Ye9DOWtK359K35sKtnf29tCXc33K4cRI9qEcTrTm/myGHm6Hvj40pZE6fNAM7qZMwTh6tD+gypwHrO+RwwGaVhQE4dCK7kvJez7A9TmSR/uDvsz+6TGn2lrN99bivqUcTpwzj8v/cJ8OGDPHMXS9eBub49kes8T2Yuyy+pmuUVI3UIhqKedvxdeAF0Oh0POF3wgGg3XAN6o+qkF65plnRnsIk5YsshVW5LnI50l0Et94Ha5zFqLSv4xL7HgR95pbJ/xapP4G4SdkX8sUYyHSQfTG1UX7+NbfTaKgOXm5U0IzUzHjj28x77emUXPyGcRmzrVsQO5JdBKtoFhMudu7E53w3/9BdNvW7Gvei75IzUnvJfXubqLbHsopiPJNHKd/gGS0M3s/fBddQuzZxzF6fXnn8SxeRuypR7L3KHNfHB1H0N/5S1HbB+PE95KYNqt/XPt3l7znA12fb9frhG9dU7R/45pbSU6fVbKATXas7UdIvv0nun7xU1JtnrxzdHv8FRXEqbSAjhg/5N8RYUWei8EpJ/D7PqDZfK8PWF+10QghxATW7Q3gu+iLRLZszF/X5Q0UZ1cmmhLrsMpdr1fJurr6eIT441vwLDw/LwjyX7GW+Pz3FW2faWdQFCjavDd2/eeKGpVHOkA3invkHdiXDfoy9yK67SEa3vehvPuRePkFy/598e3PZO9R7n3xLr6YxI4Xi9o+eM78cF57BPxNeWNyNLfgOvfTOJJ9eBKd5rNa4vq0pqnW79nUaWbgWyLoyhSR0Tx+XD3dUFOD96Iv5t1zXdcravdRSXsQaf0ghJisym7nEAwGtVAoNJY/mUg7h1Ekv3kRVuS5yDfWWjCMpFIZpG6Pv6yArpL7596/G+P1V4k/+0TZ99uhabhf/z2Re2+urGBLiSxTJsubG4D6lq4EjLwsYIZv/T10z5ybdz8c02fhX3UtelcXyXfepusXP0WPRbJjq49HsvfF0dyCZ9GF+YHi6nVQM4XonWv7X7v6Zug7SnTTTWheP55PfS4/+3jlOrrnn0FdLGx5fQ5Nw/3ab4p/iXHaBy0zqnYcmob7L38kufstswegpqHmnVjUBqJapPXD+CP/jggr8lzYK9XOoazAL922IQ74Q6FQ70DbjxIJ/EaR/AAKK/Jc5LOfXndP0ZTGicauKXni5DNJ6XpZQZS7dS/Ga6/0T5NNV5HMBEu5WRw0DePX/1YyuKqPRyAepaaujmQigcPtofM76wbVh9Eui2Q55fS090MyRceGq22DUqv7AVjeo8xz5WhuwbVgEapmCs6Zx2LU1pFqnAqaRuyaLxaf645HQNdxdsUJr/9q0fd9Gx8m7vbZXlumqqfR3oZqaiYeaKko6ANwd8dQ//Pr/KBz2WU4Tj2TZLSz6hm5yfzLl/FK/h0RVuS5sDfkPn6hUCgVDAb/DDQBEl0JIcQgjLUWDCNJNwy6559B4/V30Pf6TgxdJ/rE9/BcvCqbbSk1VU9TCkekg0g6g5ed8vjScxBoKs7irF5HzXvPtLzfqqExG4x5Fp5Ph8U0yty2BFZtAfKCocZmKNHsvOuk03GvuTUbsEU9fpRS+K9YW5xdTE+l1A3DXOcGEOnAhTm91OoeKX8jjumzLKe1dh97AvX7dllPsw23k5gxB7fNNFztwLs4Tjqd+jdetcyQpXSdzoap0DA1/SZXPinIGYsSLpzy+sQDeC5YTmzb1upn5KT1gxBiEquk5NUTwAvBYHATsA/I/g0cCoX+tdoDE0KIiabsdWE5JtJ6pLpYmI4N1+R98C630XZ9PJINkiAdIDy5lcbr7yCpG0TvMacsuhYvMte27d1D8uwF+K+6KX8a4XtOxKEn6Xx8Cw3LVxEubDz+5NZs4RQo7sNYH4+gOsM4lSKyZSOpg/vwLl1B/CeP2fbOswxqDYP4/PeZxW3SAWFhHz27KYmZ+5F5Jrq9Afyrrs27t0ZvL5F7b8a7ceuAv3DIBI6FRYeSe3fhmdaS7eVndW1DZfR0WQZiSjeG5XyT+ZcvQghRSeC3Kv3/GwteN4B5VRmNEEJMYJnsj92H/UITbj3SULItdo2/DTAi7eY6tYK1bf4TToLurmxQllmHlkzE8Sw8n+Te3dZBh2bWM8sNzDUofi8yRVZ0Y1DXVSrLaduI/Y5HUHv3FD0TSX/AdgxdM+eW/IWDZdGhS68i/vNncc45YVgzZHpTi2UgZvT/brmq5xvML1+EEGKiKDvwC4VCcwfaJhgMzgqFQvsG2k4IISarSqoP2n74H6frkYaSbbHb12gIoADXuZ/OBn2QDuB6+whvvi0/C7ZlI803bKJt8214F19seUznWR/GN//0vMDcnegsfi/S2cHsWKqZRbIJdJ3xqGUGrnHD5uwYsmv9NA2n22yTUOoXDnWxcDboy96nB7+D54LlKLvqnVXKkHV5GvCtXpc/TXbpSuIvPD0s56v0ly9CCDGRVLu76Z8A34BbCSGEGNgEW4/U7Q0UT72cd6JttiV3miv+JnxX35xfmTKTqQE8s+fl3StHcwsY1pm4VPiIbasE/xVrSfb0oAoDApv3QqHM4yxdWVQVcyhTeG0D3W7rqZHJRCLbjiJvrd9PHsN/+XXoc04wz2H1Cwe7IHP2POKBacOaIdMNg64TzUCsJhEDwyC57x30WCR7zb7V1c3IVfLLFyGEmEiqHfhZVpARQoiMibRmbbhNpPVImlLU73od/dCBvKmXvtXrbLe3KtjiveMRCLcXZWr0GcfmZbw8iy4kuXe35f3TvH5UbS2ptlbi258xe95pGrWnnUXHvRtIHdxXNK1WFfS9yxzLwDCDlJnH4dv4MEZnR1Wm8NpNSbSdGtkQoHvmXBquWk/4+i/nZ+/uvxXPBctxzZprOU3Y7jnTZxxLStfpnn8GgQ2bMdrb0JqaiQVa0KvYdzITiDXV1nLkq8vNabuZPoSawpg1R/6OEEKIKrBrzD5Y8jezEMJW5gNvbM0KojeuJrpmBa43d6Ip+Z2RlcyHf1VbC/SvOcuU9h9P6uMR9F1vEn3igfxpiptuwtu6D0+iM+85qI9H6HohRGD1OgJXriXw1Rvp+mkIdJ3EjDnZoikOTaOh8wha636abtxEzcln4FqwiOiTW0n84nl8S1bk3T//ZdcS/X/PZF9PtbUSf/YJHI1Ts0Ffdmz33GT+kgIwNIVv6cq8YwUu/yY1J51KYMMWuuedTJenwQzKIx244hHb59puCm/mXNBfBTWwYTOB6zbSuGEz3fPPMKdG2jwTumGQjMdti6UUniPD7jnr8qTbSrzxKuHrv0z41jV0XP9l6t94dVh+ZlPhNjMj29ZK7KlHiD71MLFtW81AP01TCneiE/f+3UXPjBBCiNKqnfETQghbE23N2nCbUOuRIh22RVCSO39H7NnH8zNsPd14/nZRtupmpuCI3tOdnaLn1DRcr/2GcEETceVymwFEb05GD8WU095Pn78Jt8+P8jTQ9O37OfqH/8Ho7SGVSOT178uMLTuttqONnnd20XzjJlIdbTj8jUQe/x59r79q3yTdrhBPGVN4swFXQRP3pAHMnmObXSyVmbSbJlzqObNc2zhMP7OOQOn1hBOu2JEQQoywamf8hBDCXqkPvMJSZhpcbpZrPMo0Vc9klbKv5wQluRmpKUoRefA7RQVHpqQzPJpSeFv3Fhcl2bIRR0Nj9jyZ7FHs2cchlSJ61RcIf/sqwusuJ3VoH4n/92OzdUN3wnJs2ZYHzdNwnX4WbTeupuOOtbTd9DVcH/wojuaWbOZS3/VmySxe7r0odS7I/yWJo7nF7De44Ro6b7iS6DVfhP3v0D1zbtEzYZWZ9C1ZQdfL20tOE7Z9zkbyZ3ba9JIZ7nIypUIIIezJGj8hxIiZSGvWRGW6PH5c804sLoKy7DLizz8J5Ge9Uh1tNoVZ2qCxhfp4hFTrAZtCJzH8q9bktye4/DrCD9xuufYttm2rWaDlH6/E6OnG0eBHudwon5+YNwC6jrOnm46C/XN7/mWmUxaOxSrDVlZLgZyAKzN1taysW0cb8ReexnvRJTiappI8tJ/49mfQY5FBFWUZyZ9ZpWmlM9wTrNiREEKMtEEHfsFg8BNAKhQK7ch5+ZShD0kIMVFJD63JSzcMuubOp75lBk3zT0OPx0ju3UP8+SdJtbUCBdP6bNoIaI3N5heRDlSdy3Ib5WsgftyJBDZsgXA7Wr2blJ6ynMrpnD2vP/ump4g9mROULl1JfSJO19z56O1HbKt6Zs5raPm/+7QLkMqZwpsbcClU2QGP8jeixyJEf7g529bBfe6ncZ71YWL+aRVnjEf6Z7ZUxU35xZEQQgyNMsr8izsYDP4H8M1QKPSfwWBwDfB1IAncHwqFbhnGMZbLOHDgwGiPYdJqbm6mra1ttIchxhir5yJb1TP9gbd7vK5ZE0BlVVoz29Z0xUi6fHDkoO2auBpNo/613+Rn7S67lq7T/5qkruNJdNL1vTvx/O2i7JTQzBq/xGkfBMC7fxd9r+8E3QC3h1hOYRkwg4aGjQ+jY+DsihNe/9Wi73suWI76649Rk+yj4/pVxd9fvIz4s0/gW3YZ2uzjSP75T/mtKubOH9TzrSmFa/cb6LvexDn1GFJHWkn84vm8INkq41fNdXCZ90t1hnG4PSR7e8DtHbaf2YH+HZE1fpOTfL4QVuS5sDdjxgywmYVZScbvVOC/0n9eCXwciAP/CYyFwE8IMQ5ID62Jo5IP4lbb+q9YS9NtW1E9CVKdEVTzNJRSaEDtm6+S+K//oOn6O9BjnTgap3G0tpa6WAQj0g6NU/Etu5Tkrj/TdP2dpOJRHP5G4i2zqUt04tj9Fh2bb82bUur7p9VEH93Uf/6VV6HHO1GeBoxYFO/ii0m8/EI2uMpO3+wME581D/9la4g80B+IBi6/jlRfH57Fy+je+Tt8x84jlb7exL//DM/sOUO7wX1H81tfpBubl5q2Wa2CQLbvbcvsUQuyJlSxIyGEGAWVZPzCQBMwF3gpFAq9J/16LBQKeYdviGWTjN8okt+8CCvj8bkY730GB5OBK/daC7c3NI3YNV8syoLlZqIy+zjaD5N6+w26f/ef1L3/Q+YUSU1jynvPoP3bV/UHY9/ciLPWZQZyLg/JIwdRU+pIHjmEc9oxxJ57Ev3IITwXfgG9syObXXPMPR6t+RiMg3tBcxC++8bicV10CVPmncjRN/+Aoev07HwFz99+isiDd/YHN0tWEN/+DKm2VnOfJSvhzL9GaQ7Y/WccU2oxerpQdS5SR3sxuuIkfvwYviVfJPLQXfnHeek53Gtuta1+WXg/u70B6mJhiHRQ4/HQkdOPL3MNgRvuJunyVCXr5tA0POHWdH++qcQC00il+/O5E53E1qwo+d5W23j8+0IMP3kuhBV5LuxVK+P3S+A+YDrwzwDBYPA9gNx1IcSEMN6nkg01A1fqWi0zdpdfh+b1k+ptzW6Xu/bMcp9LryL6zA+zTdL9TV/PHmPKRxZARwcdoTvxLDyfcLqgSSbblYqE8X3uC/S1HoCerqJsWPLQfmKP3INvaX7Akh1X31F63/yD2RsO8F70xWzQl9kmU7Al/uwT+JauRJs2nYQ3gK91L+3pDGKGqq2l6fo7MM79dDboKzyOXeGRwnvjmD4L30VfzE5ttbuGZF+fGXhVIehzW7TCSJz2QTP4k0IqQggx4VTSzuEfgQiwE7gh/dp8YFN1hySEEKNjvJeLr2T8lV6r1faR+2/Fde6n87bLLbZhuc+D38F1zsL+rx+6C9eCRTiaW/D97aeIPLAR1zkLi6tYbnsIvdMMRqYcMzNbGTT3+zXNx5iD0A3LdglTTj6Drl/8tP81m6IpzmNmmsHfC08T2XQTdbEwRsK6MbrR24vz+JOtC79omm3hkcJ74zpnYV5rCrtrqFYhE0+41bIVhiecXkNYRssJIYQQ40slgd/fhEKhb4ZCoRtCoVACIBQKbQf2DbCfEEKMD2Okz6CmFO5EJ+79u/EkOtFUmZ1yKhl/pddqs31uVczCvmt2+6icGSiZr10LFpE8fLBkFUulGxg9CfSebpsgrAvAbM2wZEX+uJauhCm16LFI3n5WwY2qrafr5e2k2lqz90S53NbbutzoTdMsv1dz8un996JQwb0pvGbLa8i9t0NktFu3yzDazUk8mWqew3V+IYQQI6+SqZ4PA09bvP4g8Ex1hiOEEKNnLJSLH8p000rGX+m1Kn+T5fbGjGMJbNiM0d6GappKPDANPb1OzHYfjPyvNYWqdeNonpYXaBTtpylSkQjOmT7rVg8uL47mFlJtrcRfeo7AV2/EONpDqv0I8Reextc0Dd+yy4imq3smdryI/7JriaT782XW5nU+thnPoguz/e+0hkYMPVXcg3DpSqirx4hH8V+xlsi9N+cVronNnJe9F+Xe/8zX2WvYsJlkIlH1QiZ27TJUk9kuQwqpCCHExDNgcZdgMDgv/cedwGnkLxacB/wwFArNGOAYdcAOoBYz2HwmFArdEAwGG4GngDnAHiAYCoXC6X2uAy4BUsCVoVDoxQGuRYq7jCJZZCusjLfnYiys8RtKUY1qVNlMnHxmtsBH3ri6Y6j/+XVR4FNz4ikcfe33JH7xfH+1yfT5rPYJXP5NcHtwuNwYmoNUdxy6u3HUu4n+/Ce4/8+5JNsP46x3E9naXyzFv/IqCDRCsg/l8WF0tBO5b0NRMRXPwvOJv/Qcvr9fTjIep+uFULZQS/P6e4n84H7qTj8LpWlMOfl0jra1UdtyDL1/+B8MXc9m+jKtHLTaOhynf4BkVwK1dw96NGxmHjWF5gtg9PUSfeQeHNNn4V91LUkDjDLalOTef83rx/WZi3D6AkS23NZ/TavXYcyaAx1tVS80lFnjF7FY42cYxqgUOBpvf1+IkSHPhbAiz4W9UsVdygn8dMCwOcAh4MZQKPTgAMdQgDsUCsWDwWANZqGY1cAFQEcoFLotGAx+AwiEQqE1wWDwFGAb8EFgBvAvwImhUChlcwqQwG9UyQ+gsDIen4vR7jPo3r+b6I2ri173rb+HxIw5A+5fyfgdmpbtdWfoOl07XsJz8SrLQNG9fzeJ+27BtWCROS0Rg66Xt+Ne8Clizz6O7x+vxOhOQN9RnGd9hJh/KvX7dmX3cTQfg3PqNMLfu7O/sMulV0ODn8TPn6Xvtd/h+9IaNGUQeeB2nMe+xyzksvut/rH9378HlxujK446dh41U2o5+torRQFb8/p7OPrubqIPfzcvMDQcDhyNU0kFmsDbgNq3h+imm/AuvpjoUw8X3Z/A6rVEn3gI38qvo7fMIL7xOlznLOy//h0v4TpnIbGnHgEqr3qpKUV9VxTHnreI3H+rGQCe+2mcs+dhzDgWo0Sfw2rIreqpmpqJB1owDGPUfvkxHv++EMNPngthRZ4Le0Oq6hkKhTQwG7iHQqGPDWYAoVDIwOz5B1CT/s8AzsfsBwjwA+DfgTXp158MhUK9wO5gMPg2ZhD468GcXwghyjXafQaHOt20kvHXxyJ0bLgm71zRe26yDF6UvxE9FskGOZlxGRhoXj/0dhN76mEzWPjJY/iuXAez52T3mXrb92i74cqCQi93Evjqjfg+cxFtv/tPHPX12TYMde//EB3fWZc/ticewHPBcmpmzyV86xr8a24lmq7QmWH09pIKdxD/58fxLF6WDdLiLz1nFlB54nvm9EVdJ7qpv7iK1T1PHjqAHougmprp8vjxXLwqPyBKt37IPTedYTSPv6yMmW4YoOtE7jerhaZ6W4lt22pmRjdsJpwO+jLHzrw33WUefyApXaezYSo0TE0PSMed6LQs+jOcbRyEEEKMjLLX+A026MsIBoMO4BXgeOD+UCj038FgsCUUCh1MH/9gMBiclt58Jv3N4sEsIDPT4piXApem96e5uXkoQxRD4HQ65f6LIvJcVM5obCTwtfWEv3tD//TIr63HOXsu9Vol9bgGOI+uk3z7D5YFPmq64jQf954Bx5UJfFwLFhVX2bznJppvexD/qm8Q2XIbqY5262IiPV3oetL8ujvRH4iVKPCSPLDXbIHg8lgHyQ4Hnk8uzp+Wmh5r5vrAMI/R3IJR78K/4ut5U0szU0f9q76BY86JNDocGB/6GFPnPUYq3I5WV0/HHWuzzd4z565tmYHz7T8WvX+OD5yNOnKQVLgNR2AqTJuO0jSSR/ZZF9mxuV81vT04DxUf3/lXHzWriA6R3Xisnolqk78vhBV5LoQVeS4Gp+zALxgMzgU2AO8DPLnfC4VCxw60f3qa5vuCwaAf+OdgMHhqic2t0pNFv85MTzHNTDM1JOU7eiTlLqzIczE42vHvzSuqEfP40Ts6qnoOd6IT3t1tGTj1uTxELN4394zZeC5YjnPadJKHD/Y3OrcJ0pLv7iL61MN4Fi/DYVdMpM6F5nabX6crZ5bKwhmaQmkaqraWoy43vivX5Wfhlq4k9vT3cf3NIgJfvZHku3/B0PW8pux9Lg8KcEyfhWfh+US3PYjm9aeziXNwNB9DMh6l4ar1xAMtpMI5lU6ddTB1JppSxRnAK9dxtKeHaDooy9yHzh/chy/Zl1f8JTN90uXyWgevjc3W1+/QskFf5vjh795QtYycx2Y8ds9ENcnfF8KKPBfCijwX9tJTPS1V8uvBHwE6cBWwvOC/soVCoQjmlM7zgNZgMDgdIP3/w+nN9gGzc3abBcgCPiHEpJCZrpmYMYeEu6Hqa6s0pXAm4lBbj3/F1/Mqafovv862ZL+KdYKuk2o/wpS5J0BNTfqAmmU7A4fPT+rgPmJPPULsl/+Cf9Wa/HNdejVGjZPo80+ZwYYC/2XmNomXX8C3dGVRSwYt0IzzuPfQeP2d9LgbspUnfevvoWHjwzhPPIX6D3wEerroi0VwTJtB/NknskFfpiVBl8ePf9W12X6BqTZzmmV407fpnVJLdPbxdDZMtSx0A/lVL33r78G7cStdJ52OESnO1LnOWZgN+iC/Z6Jd24R4YJrl66lEwjpDWKWWI9Vo4zDodiRCCCGGVSXtHN4LnB0Khaz/FSwhGAxOBfpCoVAkGAzWA38LbASeB74A3Jb+/3PpXZ4HfhQMBu/CLO5yAvCbSs8rhBAiX6aaZDidqXJMn0Xj12+ib+9ujFSS1JwTLANNh6ahtR8h+uwT/VUgV3ydVHcC54zZeW0SzKDuKnRDz2aPen76FHz6Ippvuo9UuB2Hzw91dfR1tOE++29wfDoIXj/R0CM0r/suycMHUV4fDV/8KmpKDarWBXW1dG6+vb+gTDprlnA3oHn8uN7cSUduldKvXE/qmJkENmw2A6aClgRJA/sgqozsmdV6Sss1mm5Pdr0hmD36Um2t0BlGzwle89om6Lrl6654ZFhbjgy1jcNYqIwrhBDCWiUZvx3AmYM8z3Tg34LB4E7gt8AvQqHQC5gB37nBYPAt4Nz014RCoT8CIeBPwM+Byweo6CmEEBPOcGRO6uORvOIdqYP76LhrHUYqiTZrLt0un+V+nnArkftvyS/OsvUualpm0vnwJphSi/eiS/BddAmexcuIPvNDMIy8rF3vS89y9PVX0Zqm0nHXDfT89w70t14neWg/4Xtv4Wh3gvr/s5Cjb/2JyAO303HzNUS2bCS86WbCm9bT9+YfoaYG53Hz8C6+GGPfHuq7opbXZfT2ErlvA9RMobNhKnGr7GlDwDJTOZQgqjBj5pg+C4e/ifizTxB96mFizz6OZ9GFOKbPyp7HLsNr9fpINFYfSsbZ6n3IZDeFEEKMrgHbOWQEg8H7gCXATzDbOGSFQqF11R9axaSdwyiSudbCijwXg1dO5iTbuqGC6o527SIC37qL+HHW2T4A367XCd+6puj1xjW34PA30vnjx0jteTvb7oF6N85jj4OeXtAUyUMHMXq7cM49Ac3loW/XnyHZB7qOc+axJI8cwnnK+0gEpuFtP4SeiKI5akh1HEHV1ZPq6qbrF8/h+dtFRB78Tn9W7/JvkppzPI6D+0jt+nN/Ni0z7hJtMArvcW4vvqE0LM9tqeF0uwlf/+WiDF3j9XcQnTF3yMevVsuRcp6lcrYZSjsS+ftCWJHnQliR58LekNo55HADP8VsxTB7gG2FEEIMgV3mJFPEwzIwXL0OZs1FO3IQVe8i5fHR5fLlfTi3axeRappauuF40zTroh973ib87BP4L70a/SMLiH5vo1ko5VOfI7zxmzkB2nVoLafS96edpFJJao6dR+TRe7I9/XxLVhB7eBOeT55P54tmgNeeG+CtWoN36SWEc9pPaF4/yYN7caaSpA4dILHjRTyLLswr5FIqe5c3rTERwxFuz7a3GMoUxdwpoO79u60L3xgMOlirdsuRcn/JUM4UzqG2Iyl3vKPRYF4IIca7sjN+44Bk/EaR/OZFWJHnYvAGypy4E53E1qwo+oDtuWB5thecb+lKmHkcXXPnV/wBPrNt5gN2jc9PcudvLVskZIKswFdvpGPjdXgv+iLx9FrA3LE1XnVTtjef5f6r1xHedFP2/4X7N9+wiSPfvAwAR3MLnkUXZouz5LZgcJ2zkPizT1QUuNndz4GqZQ4UhHgSnUQHcdyRVM61l3t/hrLGr5y/L2QN4eQj/44IK/Jc2Bt0xi8YDM4JhUJ70n+eZ7ddKBTaNYTxCSHEpFFutmLAzEmkw7bPXebP0W0P4blgOfVTj8l+OC+3eEfhB2zf0hUkfvFTPIuXUXPsXPre3Z0N2jLnM3q6zHHatHfo2/1Wfgbzya14Fi8j9tQj6f278/5fuH8q2l/YxLVgUTboKzyec95JZpXNSjJBNvezVKGXcoKQzJq8om08/iFn6qqmnGu32Ebz+nF2xXEXPMtDKQ4zkIEy4UIIIewNNNXzNcCb/vPbmL30CiNIA3BUeVxCCDHhVJKtGChgsAsMjZyWp9lAsCB4KWeqYOEHbHQDPRYh9tQjNK651TKjp+pc+WMpHFtBawSjtzdb6VLV1qLqXen/m/38NK+/f82gpqGmTc/eE9sG75pGqmWGeX0VBBuDmaJYThAy3IFQNZRz7YXbOJpbzOm8679q+SxXcypqnkEE6GLkyDRcIca2koFfKBTy5vy5kgqgQgghCpSbrXBoGp5wK0Z9PU3fvp++7q6iVgSWgeHSlcRfeDp7HFVbC3X1g1tfVfABO/HyC/iWrCD65FZiP3kM/6VXE3nwzpz2DVeT6u7O68GXOy3Uf8Vaok98L+8UmUA1u8bv+afwX3o1seefxH/F9ehth/Onls6eQ/f8M/Bu3IqzK4F69vGiYKXm5NOJDiKbZnU//VesJRWP4kl/v+gDbJlByLAGQlVQTlaycBvXuZ/Ovjcwcpm3kVhDKAZHpuEKMfZVvMYvGAweC8wE9oVCob3DMqrBkTV+o0jmWgsr8lzkK6fioUPTcL/2GyJbNuYVNkmc9sGiZuL51SM9pN5+g+ijm/ICQeeJpxCdNjvvg1fhb+W7vQHqYuG839K74pGitWmO6bNouGo9Rnsbqq4O1deH0duN5vVz9MA+uv/j53g/HST5zi6Mehc1J51K6mgvDrebVG8vzlSKyJaN2YIu/ivWgsuF6j1KstWs+qmddCrK34yzK0Z7OpuUkbumzOpDpv+KtSROPtO26fpAMvdFdYZxKohsub2/+IzFB9jxsH6vXOVUCs3dxpHsI/ztq4qOU071Tjuyxm98G+w62YHIvyPCijwX9qpS1TMYDE4HngQ+DLQDTcFg8L+AJaFQSCIuIYQYQDnZCk+4lc6nHslr+B196hEaZh1HzD+taBpVbvXInt/9J03f+i56rBNHgx9dT2IYoJTKZm5yPzhP+cgCfOd9Fufh11H1dRhOJ0f//Ac8ngaMGcfiu/pmoneuxXnse/BesNycYooiNfNYuu6/rbi1wqVXEfvnJ+h760/mB747HkF1tBG+5Rpz2ua5n6Zh+WVo/kaMOjcpZw3qyCE0XwPOxqkk3W5IplDtraSiEeupnOlsmtUUyni68flgZTJzbqAj5wOsXTZrXKzfK1M5WcncbTyJzlHJvI2HqbOTlkzDFWLMq6SdwxbgVeDvQqFQIhgMuoFbgAeAzwzH4IQQotpGcw1KtzeA/4q1RO692TZQMKKdeBaen1et0r/i62jJFK7dbxC9c61lpkM1T8N99t/Q/u2v5WX8tGnTcXcnSMw9mZSuUx+PEH98C57V63D2HaVt3VfyM4TNLcT/+XGS7/4F3+p1eO/6IY63/kj47hvzMpC+z3+ZjhuvzG+Y/uB38CxeZu575TrQDaL33GS2dyiowOm/9Gpiz/ygP6P2T6tx1NcT2XxbupjMSsvAwqnM91A3jKJgRcPMOgz5va1gCudkDUJGM+gd61NnJyuZhivE2FfJur2PAleFQqEEQPr/1wIfGY6BCSFEtWWyXbE1K4jeuJromhW43tyJpixnRFT93PVvvEr0ie/hWbwM39IVNF5/B93zz8gLFBweX1G1ysjWu+h99Tew/x00rz/7evSem8wgFnD2dBN54Pb8LNW2h0ju3Y1jSh2e3W+YWZqebjwLz6fO35QNsvK23/8O3s8uM7/edBNTErHstNPseLZsRKVSlsFRtqLmSadjRNptK3BGHrwT1zkLs1/rnR1540n84vls8AdkA+DIltuz11x4f6v13mY+wOa9ZvMBNhOEJGbMIZHORE4GuUGvb/092fd8sly/KJb5ZUDuz6zvynV0e/yjOzAhRFYlGb8wcApm1i/jJCBSzQEJIcRwGc1S8Lnnjj31CGC9/qWvu8u2TUN020PZ9geZ1zNZKL39iO1+R19/lWi6t1/g8m/S+dJzNLTMsN3e6O3Jfq23HbbczuiKWzeCz6momQmgbCtw5ixBMM/bv02qrZX4C08TWH0Dyb27qJlzAtF0htBq6lg139uJNIVzOEnmTeSazBlwIcaLSgK/24F/CQaDDwPvAHOAf/z/27vz+Ljq+97/r3NmZEmzacaSbbyBMQEMSUzapmm6/EhabmjvhRRfbnJiX0OTBhlwKTgNEOPgmCU4xmEpS8AEDCWAYzgh1FD45QLXvb3c/pomN2ka0gZcFhvwgoyW0SySZWnm/P6YRTOaM9JoH43ez8dDD6Sjc858z8zxMB99vt/PB/jGxA9LRGQSTOcalEofuylStk1DSbBUkIUym+e5H2cakF335vT10XXftwisWosn0lx2f6O+YfD8hlFmPOSrfBYWV0m4VIJ0Dh4YsfUEplmyTzoeBcPASaeJPnRnvtG769SxCXxt9QFWZGz0xwCR6lbxVE/bth8CLKAFOB+YC6yxbfvBSRqbiMiEGs0Uvul67N5ghPD6jcXTpVa30rP3hcFAjtJpVMnIfMJXbCo+bs06zFCEnr0vANnea6vW4j1hMf1H3yd8xddL9vcuPon43+4abLHwzOOE119XPOVy/UbiP3iUxAtPZ6atfuESAqvWkoo0FwVHuQCK3/10ydjCl11Lzysv5X82miKE/6L4cUIbtpDyekns2ZUP+spNHZvo13a2TuEUEZHaVXE7B8uy5gCbgf8OLAIOkanyudW27WOTNsLKqZ3DNFJZXXFTbffFdJaCNw0D3/7XSb+9L9NU3TQxlp9Gz8krhrQIiJHYfh3+z/wpnuZ5DLx/iJ6X/450PEr40mswTlnBgEvJ/aZ4J6mudub4gqSinXjCEahvoPO2zaSOHMwEfUMLrFx7C57GAOlYNL9/34G3oe0QDg49e18gHY8Suu1v8BzvzbRxaG4h1eAjdvWXKi7bPvTaHdPAe/KpOCcsxfjgfYzGRgYCIY75mzJtJQquDxixzUD+MVTmf0artvcLqQ66L8SN7ovyJqSdA5mqnqcDV5KZ6nki8HUyPf2+PL4hiohMnsJKniw+ieBtj0BXx9RP4es/TuKZxwcDkw1bSsfaeRT6+3H6jpFKQ8NvfJL6M87C6e0Br5d01wdQV1/yjm6mHZx4jPZvX58/f+SKr9N05dfpuumvSgqsmMEw6YPvEC1ssv4X11G3cCGd391eFDz1+kM0pFNQ34DhnUNvY5DQNbeUBrEua+BMwyAYPcpAdt/k3ucHs3fbHyZ+4ocGd06nS6aJ5YuzOI77/8Vyh2p6poiIyLBGE/itAk6xbTua/fnXlmX9FHgTBX4iUqVGzARNUWDQmIgSu3tI8ZG7i4uPmIaBp76ewPmfJ/HiHkJrL+P4r/+VWGFwdtm1OMePE/+bu4vbOaQGSqpvdt33LZo3fZvAn2/AO2T9nO+c8/Lnze0fvf9Wmjdtp/mGu+jv78dpitAbjGSqkRY+fxu2wJw5Iwex2ee+q/DY1a0kXniaVHsb5uF3MU/7aNngbLRZPK0vEhERKW807RzeB3xDtjUCRyZuOCIiE6tctUe3lgCTarjiI1mNiSjH/+M1Yrsfwnf2uQwceqc0OPvubdQvWlpyHelu9/Mf3/cr6kJNpN4/XLQGrlylzeP7/o10dxSnKUIyO/Wy5Pm7+2bSb+0r2Tb0OXV97p/cie+c8zDq6xl47+1hX4eqee1ERERqwGgyfo8D/8OyrHuBg8BS4ArgMcuy/ii3k23bfz+xQxSR6TCdjc4n1BiqPU7GtVfU3DjaCcd6B6t3DmlxkBt7Oh7Nf29kr8PwBdwrZ6bTOH3HSO59vqgKp1sVzdz+A4feAZ8/8/yUef6MtFOyjWQcf/Y6jPBc6O5yP9Y085k//4qVrq+DaRh4OtxbSUxJFVYREZEaM5qM32VAkMy6vvuBTUAIuBx4OPu1c6IHKCJTbzobnU+00VZ7nKxrr6S5sRGemw/IMoMx3cfunZP/3mtkxowvQHjd1aXVQF95CaO+IdMX76Vnaf7mfYTWtEKoqbQKaHZ/JzWA0TR32OcvV100x7NwCZ5kHOcn/xteexXnJ6/g9Xhcj/UuPZnEC09nAliX1yH3GqTefH3aqrBK5UzDwJ/sxn9oP4Fk94x8nxARmQ0qruo5A6iq5zRSdaXa4k92E9/YWnHVxnKq4b4Y7Tqxibp2Nx7TJNDVhtPRjtncQjyygFS2x15+rPtfh54knkAQ53gf6bbDxWv81m8k+Y976f/VzzJZs5eexb9xG33BCL53XsccSNG//w2cdJqeV14i9PkvkfjRMwy8+1Ym0FxxVn7NXt1HP07ov/w3jr/x6/z+gT9eBQ0+nDPPItkYdH/+NmyBujnEbt+c39a85a/pf+PXRWMN/fkGzECQ6L23DG5bs47E8z/IVAt1eR3yxWB+/mOo92GYBrFdD4ypUmfNZK2r2ERWU62G9wupProvxI3ui/KGq+qpwE8mhP4B1hb/of3EbtxQsj100z0kFy2r+DzVcl+YhkFjTwxvIobT20O6eQE9AffebKO59tEEFpV+QK4zTRpf/SnRB7YTWHMpRn0jc05YSPpYL3i8xP92F01rWkn39jDw3n7o78dY+Vsk5i/Ba5oEox9ATzJTBXRuC/0NjdDxQVErhPy4u7vw1jeSevPXmH4/Rr2P/qNH6Hnexn/l9fnrLdw/dx7DMAgeepv+117FSaepW346XXduKQmYIzd/h4H6BujuymQRTRNvx1GcOfWkgiF6fKH89bs+R1+6Cqc3CX19eM/6OPH5SyoO+kYTkBQH5fOIR+YXBeXibiL/UFIt7xdSXXRfiBvdF+VNVDsHEZklKlqPVmXKBWGmYeBLdGMefpeB9/aTfPm5stkmqPzaS3vTmfhc+vLl9g1GjzJwcD/BVRflWxrE7rm55ANyoOMIHQ9k2yn09hB79N6Sa011dxH9ztbBLODipXhOOBEHcBwHJ5nADM+lv9GP0d+PM6QVQq76pRkIE4p14Gmej9ObJNV2iB6XKZhu1TL9iSidW6/NP0+Rqza7rsdzEnGScxdgBsL4E92YB/fT/94Ben/2/9Hw8d8jsHQ56UUn0hNoci/m8ug9BFatJbFnF8Hf/XTFWaRyhWHcAhKPaeL/1U/p2rG9OLP60U8o+BvJGNbQiojI9FDgJyIlcuvRSrIlLn3aqkG57E7htMah7QTKBQGVXntjTwwOvVPc0mDNOhoXLCLZGCwZW7mWBkM/IKc72ksLrgz52fDWFVf6vG8bke07cQ68QWdh8HL510i89Bz9r/2yJOOVC1wLK4ca9fWE1l4OC5eM/FoP+cCf7u5yH2tTGH/be3iinUVTPsOXXk3s6cdIHTmYH5vTFC5fDGa0998oApJAV1s+6Ms/pzu2E9l6P91N8yp7vFlqJv6RSERkthpNcRcRmSUKm2GHbrqH4PadY1qzM1XKZXcCXW1l2wkMbaWQU+m1e+OxklYLsd0P4U3ERh5bQUuDoR+QPXNb8gVNcpU4hxZgGWgr7qLj9PXh6U2W9PGLPvBtgp+1ip6TXCuExkSU9Nv7Sq9h1wMwb+GIr/XQoi+JF54mtPbyorGG/+I6uu/YgvOrn+eDvvzYHrwD39nnFo2trqHBvRjMb/3uqO+/0RT1cYYE27kxOR2aRjSSSooWiYhIdVDGT0Rczahm2GWyO+U+0BsYJUGA61TRYa7dOdbj/pi9vRWNzTBNwlduJhmMQMF0wj7DJHz514g+8O3BSpzXbaP/3f2ke5IkXnoW36f/pOh8Rn09qe4oZjCMb9V5mVYQZAJHp+9Y0ePmM17RzswUVbdr6O4Ef6jkmgsNzYym41FYuITQ9odxujvx+v1033FDJqNXpmegUTAB1enrYyCZdM22xsPzR/1Hh5Eyt4WvtyfS7J6tbG4Z1WPORoV/KMmtAVURHRGR6qTAT0RmvHLTzcp9oHdMoyQIGG1lQqd5gfu5m+cPOzZPywL8532eukVLcYBg23vEFyzNryUz5swh+ZNXaL7+NtLxGJ7wXKJPfDc/XTN82bXQ0Jg/Zy6zZs5bQOD8zxdP21yzDiMYKhpfLtg1wnNxyvTyG67VRWFw3LviLPcP/P4Q/kP7SR05WPzcDH2ucIp/borQu/jkCQkihgtIhr7enoVL8sF24Rq/RGRBUVAu7mbUH4lERGYxVfWUCaHqSuJmqu4Lt8AtvH4jiRefxfeJP8g3LTfq6wlfsYnU8tMxBlI40Y58ABS/9stlKxPmA55EjLqGBgaSSbzBEAP/9oviVgNrL6fuwx8j/UFbprhKIEhvY5DGfb8kdvfNmMEwgT9dXXzMmnWYi5aSWH5m0dq7XNEY46TlzGmeT6qrHU9ThJQvSE+gCV+2AqXRPI9UXy91hknHTV8puYbQF/+S7gfvKLvGj6Fr/DZsoee00oB3tMFxINlNLFvt0dOyoOS6w5d/jZj9N0Vr/KZqOrFbJUrPwiVE/uoGUl2dGM0tJIa02pDJp/+PiBvdF+JG90V5quopIjVtaHancJph4oP3Caxai2GaeH/rd0lEFpQUfAlfsQnviafQ8JufLJomSXcXZiCMb9+rJJ7YQeDcC+jMBpGhNa0kX/67zLkxcHBIPPck/oF+nL5jGKbJnNM+DA0x8PuJfOVGcBwG3tuPGQyT6mvLrwsMXHgxjfMXDRaa6T9O4pnHM332VnyEvn/9CaQdul95kcAfr6J+yUnEl60g3TQPf18Szztvcby9zXU6paflBJpuugdnSPYs7Tj0nLyCxgWLiJz2YZzeXtLN88u2uRhNlUwonmqZam8j8aMfMvcbd5Lu68Pj93M8ECKw8dbMtNKpnh7oMv02deQg/b29JJafkdmgoE9ERGqMAj8RqQmF080Chw/kpxmm2tuIP/UIAKEVK2nwdpUEMNH7tjH36pvpvGNLUSbOiTTnA57AqrX5zGH2AUnHo/lzQybDVrf4JDrvHDxP8zfupH//m3QVZtUKqno6fX0YaSe/9q4xEc1nB/1/cA4dt15XfNyLe/B9+k9obDmBpL+JOfEYHQ/eTnDVRe7r1HBwIs2ZSqNDAqu042S2F1QhLTtNr9xaxe4u/NnfF7bRcJtqGcv+rqWlhWR7O9QzuJZwCmeflJsa7DUymU2tTxMRkVqkqp4iUlNMw8BrGOUrOpYJYPr3v1FSodNIO/n9hxYocau4GW79KtFH7y06T/pYb2nlzGxVz9xxjmkMrqvLPp7vnPNKqnTGntyJ7+xzBwNFMm0UnL6+shVAo39zT0ml0bEoVyXTa0B8YyuxGzcQ29iKb9+rmEYma5oLxpOLlpH0u2cSK2UaBv5kN/5D+wkku/OPMRY9gTDhKzeXPlc7vp2veioiIlJrlPETkZrSmIgS3bGd0OrW4rV9V24mEQjjo0yhkSFT+3LVLQsDnsLjchU3I1vvZyCZZE6jj+Ovv1pU0KTu1DOh79iwlUXza/yyhWZyj1e2EqZp4gBGrkhLy/xMVc/2NhIvPJ2f1jrnjJV03bM1k1UcWml0DNyqZIav3Ex0x7crnv45VmMpvjOctOOQCs8tnqZbpq+iiIhIrVDgJyK1JdqZWduXC4IaA3jnL8Dx1uFLROkNRkoDmHVXE/vhY0WnyWUIcwFP4okdJcFk4KL1mVYDTQ7HTJPQiYmi4DD4X9cy8N4B10CzfuXH8Z56BpgGA/WN+QAm93jOQffj6k4+lQHTzFckjUfmE16/keiO7Zngb88uwpdeTffjD5Bqb8Ooryc9pNLoWLhN3UwlYkWBLpRvkj4eo11fWAkjECK+Z5caj4uIyKyhwE9EJoVpGDjvH8TfdqRo7ddkPE5hiwHCzfkMWM/eFwic9zm67i7OFBW2IfB664g/ei+Bcy8oqf6ZyI655/SV+Dduw0nGiWz7LsaxXpyeJPiD+HriON466mNdpI710nLTvfS9+xZ0tOMc7yP58nMlAWNodSud39lK4I9XkTZNjJW/nb+e3OM1zZ1HuGU+0Qdvzx8XueLrGC0L6Ak155/LVDpN8qOfYO7WHTgftGEGgnR//yH63/j1YJXOQNOErKEbWrY/gHv2dMKDpzLTc8cTYI7U509ERKTWKPATkQmXm5r3wQRNzcudc2iDdWCw9YHHi2fJSThz5jD3+tuJ7tiO7+xziwqylGSK/E0Ekt0MvPsWia6Owal/pkFq2alFFTCT/qZMhc//eJXE4zsya+1Mk7ozzyLd0U77jlsHg8ZLrybx6s8In/XbpONREi88TeSrNzNw4A2cdDo/rTC2+yHmfuNO+k0T/6H9RQFyvz8A/f0EV6/DEw5jNPgxGhron9NQ8hym0mmiTS2Y4Xk09sQIfukvMfv7SfUdm5BsXzlTFTyVK8YyngBTjcdFRGS2UeAnIhNuoqfmufbpu3IzzG2G7i5izzxelE2LvfQsobWXgc8/YqaoMHiJP/XIYPDiC5UEL42JKInHdxRlB4NrWklkHz93/uiDdxDZsIXu53bTfN2tHN//BqTTxHbvLBlLOpkg/s2vlgTIpNMknv1+5rHsR/OB5hyPB8/i5a495tKOQ68vhO+9A3RNYNBdzkQET24B/dDjJyvAVONxERGZTRT4iVSJSj4AzxgTPDXPLZCM3nsLkQ1b6HrojpLKl4FVa4neewtzt94/YqZoNMGL0d1VkkU00o7rtTpA4+kfybdjCK1Z595uIZ12DZCdaCe+s88l8dKzpdNQr9xMYsXHJqTf3niNJ3iqtGiLsnMiIiLjp3YOIlUg9wG4XFn8maZc6f8xT80rE0g6w1TMdPr6GEgmCV21pbhs/1Vb6M1OEy3hOJR7xk3DoM4Ao76h5DHdrtUTCBYFiMmXn8sHf/mxrG5loO1Iyfjp7so8h6bpOl01eu8tZdsOGNn2DiXPSbb9QzUpF6S6XdtEtoYQERGZjRT4iVSB0XwAnglyU/MqDrhGUC6QNOobXbc7OJn/NkXymaLQTfcQ3L6zJJtUadDdmIjStePbzDn1zKLHTO59viSgC1/2NfoPvFUUgKXa20g8/wMiG24g9IVLiGy4gcRLz+L09ZSMP5fRqjvjrEz7hnLZUxcef8A9EPX7XfefVsNlhkVERGRCaaqnSDWYhKqF0yk3NW/eXY9z7Oj7456a57rGa3Ur8b99wrViZuKlZ7PVLMMjTkWseGpktk3E8XffJvSlq0jHujKN1E0TQnMJfuES6D/OnDPOwvH5MSiteJmOR+l/500Se3YRuPBiAhevh7o5+f0K166lHYf44pMJ5YLYCgubDBzrdX1OBobcX9VgpKItUzH9uaamWIuIiAxDgZ+MS+5D08AHBwn4gvrQNEaTUbVwuqUdB+OEJSS9DUBmeoE/2T2mD9iFa7yM7i7qgK4Hvp3p15eI0bz5DtKJOAADbUfwffpPoG6O67nyH/QTMeoaGkh3tBNcdRHJvc+Tam/D07IA3znn4X3/EE2hBKlAiB5faDDr2D8AZipf0MWorye09nKSLz9H4I9X0X/8GD2LlgG4BquJl54lfMUmjCXLGOjro66ujvDGbRgNPgYCQXp8oaJWDbHFyzON0u+9pWxhk8LgpS4QJL7v34hs2ILTdwyjvpH43z1F4298svxzMUlBz0jnH65oi8cw8L/2i9LrnsAiNRPdGF5ERKSaGU7t/M/NOXz48HSPYVbRh6aJMxXP5XRkNlpaWmhvb3evynnFJtLLTi0KdCq5Bt++V0k8kW2nUN/AnOWncXz/m8SffKgkcB6atSs8fmjBlNDqVnr+6X/h+70/LN6+Zh0sPone5WfQ+PovcY4ecX2s5pvu5bjPT2/B9eSec6O7C4/fz0BfH4Y/BB8cIfHI3aVjKPOa51+7bGGT3oLXbuhz61m4hJD150Qf+Pbgc71+I8mPfqKoEuhk33PDnX9uczPt7e1lrw0gdHg/nVuvHfE1HQ9/spv4xtZJfQypXO79QqSQ7gtxo/uivEWLFgHuJQu0xk/GrNbWpU2nwoxWubVobkzDwJ/sxn9oP4Fkd9liMNNdPMa1Kud920j/+H8R+PXPCfTGKxpL7jypIweJP/UITt8xOrZthGO9Fa0Vyx3v2t/vyZ00Xby+dPvuh0i/vY+GeBc9p6/Ec+Jy18fq7ztGsjHo+po5jkPKO4feBUtxcIjdvrlsj8HCfz+517fx4NsYQO/ik0sKmwx9bn1nn5sP+vLP9Y7tNMTdn4vJ+vfbmIhmAuxVawl94RKCqy4i8cSOkvO7FW1pTETpf+3VyV//pzWGIiIyi2iqp4xdja1Lm26jLYs/mozNVJf4L1HmXjHSDtH7thG48GJ8S04eOdgdcp5c9U4oXU/nOlU2e3zhcYXjSXUcLTtOurtI+5swmsKujzW0eEq518dpCg87hty/n4pf32GeE7fzljuu7H5jlYi5ZlWdZHzkY6OdkB7d2saxqMUp1iIiIuUo4ydjNuEl+2VURpWxmebMRrl7xXvSKZjBMEbaIfHEDoLRo8NmLwvP42lZgPekUwitacVoaCD0pauKq4hecwsGRtH5Co93rRLaUKZKqGng9dYRSHaTGhgg3PrV0rYMQ57fcq9PXUMDnoVL8C47Zdh/P5VmzMr+Oyxz3hGPm6B/v3UNDa5ZVe+Qx3RjhOeSfOVFQqtbi6ulXrl5zJVh3Ux09VkREZFqpoyfjNlwhRlG08RZxmgUGZvpzmyUq8rZ/fj9hFZ/GaN5PoHwXLqu/4ths1u58+TW6HXdfVNRgZXA2svwNkUwli4n9f5BYhsvGbKm8DRC19xC4pG7XStfxp9xqRK6Zh00+Oj+6xuhro7QmnXEnnmcwKq1GKZJ3bJTie35fmnxlDKvz0AySegLX6b7sftLH6vw389wGbOC13foc5t85UXC6zcS3bF92H+Xk/3vdyCZdM+qJpPUjXBsTyBM4KL1+cDXME3qzlhJfPFy0gXrFMdLjeFFRGQ2UXEXGZdcYYa6ngT9vkBR0QmZXIFkN7EKC1NMVyGewsXXpmEQin5A/8//CSedpmfvC6Ta2zDq6wlceDFmfQOJ539Aqr1txGsJRo/mg8TCa4985UZSXi8sWEz6x3+fmaJJptdeOh7NPM7Sk3GWLIN4N3Pq5nD8F/9cNJ66M86i6c+vJN3ZjqcpwrH33qbHfpRUexvBL3yZxJ5dJY879/rbiC06uei5LPf6RLbenx97roKoYZp4f+t3iYfnk3YcTMOgqbONjm9cgRkMZ/bBANPE+9u/T3dwbslzUlgg5VgwklnT51IMZrjjJvLf73D3Z+NJp4y4KH8yxybVScUaxI3uC3Gj+6K84Yq7KOMn45Jbl9Zy0ilE29snNNOn/lrDG03GphoyG2nHoT8RJ7Z7Z9H23Bq62O6HCKxaS/ypR/Lb3bKXacdhIJFwzSY5c+o5tuw0fP/2M2KF7RZWt5J44WnqTjwF0+eD/uPETjiRXsfBt+Rk4gUVMQN/fAEd37iiKJPoO9/C6O3Bu+xDmMEwqb62osdN9/biS0SLntNyr8/AsWP5safa2/LXG1qxknSTkw/Sj7cdwgyGCZz3uaKsX3jhYswzm4teu5L1oel0RetFR7uudDSGuz8bKzh+MseWo/cYERGZTRT4SVVSq4iRjTaYm4oP0iMpN+XUwckXPCncXq6RN+Fm1/Okm+cTOPIOnfdvK1lbFrjwYvoPvEFizy5Ca9bhn7+Q5Oln0XP6SkLbH8bsOIrH56fjxquKj931AIELLyb21MP5qZ+5zKSnZQG+z3wWI5XC8/5BfI0d9Cw+hbTj5F+f3LmNhkZSwZBrY/eha/ti99xMaHUrvs98tmSdXPS+bTOi3UA1/LFhOHqPERGR2UaBn1Slaa9COUNUQzA3Gj2BMKFrbiH99r7MNEzTxAiFSfzwsXwRFaAoO2RC6Qf0a24htGELsbsLtm3Ygic9gBPvds0G1i0+ke5H78u3aAhceDGB+ScQD8+HQ+8QvedmgqsuKl/VE4qO7Xn57wic/3liux8azMZdeg2+5hNI1PsGT5A9d+E4Q9fcQuz2ze6Z2uzawLRh4j1h8YyunFvN96feY0REZLZR4CfVSa0ialf/cRKF0zDXXp4pmrJhC86SZYRWrCzKDvmT3aUf0G/fTPC2R/LZJKNpLnxwhK5NlzH36m+6Z9TmNOTXD+aCOaejnUbvnKLzl8tI5jh9fXg/dCaRMz9Gxy1XF2fjHrydlhvvgWzg5xpc3H0zc2+4i8jWHQz0HQN/sCgTlsuKGr09pJ202g1MFr3HiIjILKN2DlKV1CpiZss1Hh947V+LWjM0JqL5LB0MTqVsuvomek5bSbIxWNTIGyj/Ab2rI9/4O9cU3XviKRg+H6E164pL9K9ZR//h9/LH57KLRnNLpnpmtmUCjX5Cay8vObZn7wtFx5p1XtKxLveqlYUtMsqMvf+XP6Pr+vV4Oo5imMVvw7m1cZgmiZeeLWlpELliE31B/TsYL73HiIjIbKOMn1QltYoYWbUWpjANA9/+10m/vY/+7HRO3/LT6Dl5xbAtDtJN7mOvqBVF9rzB/7qWrvtuJfCnawhceHEmq2caeE5YTOJ79+WPDa1Zhzl/IcnIAnyv/YJotlKnUV9P6EtXEfriX+KJNEN9A55GH/50GrKtEkJrL+P4gTepP/k090bukWZMwyDtOPmxD63M6dQ35NfrDW1en1sb17h0GU0Ll9CdLXpT2DoiuGgp3U3zRvWaVOO9Mp30HiMiIrONAj+pStVeGGK6VXNhisaeGBx6p3g655p1NC5YVDaIq/P788GSaRg09sTwxmM4x3pIz1tYup5vyAf0/HmP9ZI6cpDEDx/Dd855gAH1jZg+P01/dgVmIAgeL05DI/GmZhriXUTvvaUoA5l4bjeh1ZfQ/dj9mV6BhRU1r9wMvkYS936XYyeeQvjSa4g+eHvBGr+riT76HXznW/ScvjITXFx7Cxx8p2gtYGjt5XhaFmTaWaSdkrVlacch2RgkFAjiO/tcDAycdJroQ3eSam/D6WiHCgO/ar5XppPeY0REZLZR4CdVq5oLQ0w3X8Jl3VuVFKbwxmN0v7gnk6XKVulMvLiHptM+THz+EtdG7tE7biBw0Xp6V5xF49uvwaF36CoMlK69hdD2h3G6O0s+oJuGgWOahK/YhDGnHqO+Pt8mwdOygMD5n6dj67VFQai5aClOaK5rBtJ39rlE77+VwKq1pRU1770l0wswGKbhNz9Jqn+A5utvJx3vBsMg/re76H/j18Tefp3g9p30BsJ4G3x0Zq8ld57YrgcIrFqb6QmYrWjqtrbMDIVd+wYazS0Vvx4qYlKe3mNmL2XBRWQ20ho/kRnGNAzMw++WL0wxzZxUP4FzLyCxZxexpx4mvucJAudegDMwkM+yRLbeT2hNayb4eeFpUkcOErvnZgJdbaTf3pfPjkE2ULltMw5Oyfq/XDYrfu2XiT22A3x+wpd9Lb92y/eZz5aea/dDDOx/g8ZE1H2dl2nmW0u4Vvj0eAmc97nM9T2wnY6t1+D0JOl+5B763/h1fj+6u2hMROn/91+4n8c0Ca1upWfvC2XXlsUj8wmv31i0xi+8fiOJyILKX5DhipiIzEL5942NrcRu3EBsYyu+fa/m1yKLiNQqZfxEZpjGRJTUe/urttqj6QsQHZIpiz25k7lbdwCDDdjdGrk7He2ZdXkVVlsszGal+toY+Pdf0vvqz2i+/nZSHUcx6htdz2UGQtQd6yEV66b5+tvo2vFtUkcOYtTXM+e0DxcFWkOfY+/ik+i668biTODOO4uaz+dfi2gnpJ38eTwtCzJr/UyTOWeeRfdjO0jHo2XXlqXSaZIf/QSRrfdnnpvmFhKRBaTS6Ypfj4rWSIrMIsqCi8hspcBPZKaJdpJ8+TlCq1vzUxGN+nrCV2wiMYWFKcpNlRpIJsoUcEng99ZBtJO6QBDPwiWkjhzM75OZwjgPDrxZeaAyJJuV3Ps8gfM+x/HXXiX+zGMEV13kvqZw0VI6briq6LkjEGTgjdeIvfA04XVfJfbDx12e46/jOO6Baa46Z+EaRB+QeOVFQqtbSbz0LIFzLyh5zVLLTqXHFyo7zSyVTmcKueTW9I0i6AMVMREpoVYeIjJLKfATmWGM8FzS8SiJF57Or6NzTIPUslOnZI1KrviK58AbRO/bVlIwhKZIUbDlaVmA7zOfxXOsl4HXf0Xy5edIx6OE128k9tQj+Uxb6KotJCLzaVx+GqE164qLoWxwD1SGZrNS7W0kXnqWpmtvIbxwMbHdO0uDt/Ub6XrwjuKM3X3bmLt1B8bvfIqGFStJRZoJbLwVJxkjsvV+UskkTlOEtGnCv/zYNZis/9jv0LRiJU7BGsSeQJjARetJPLGDpovX0zWklUX0vm0Et++c1NdtNEVMxrvuSeumZCZQFlxEZivDqZ3/KTuHDx+e7jHMWi0tLbS3t0/3MGaF6azSmHvs9MH9+aqdOUZ9fb6gSW58ZjBM4PzPFwdxq1tJvPA06XiU5m/ex/H+4+AP0psNEjymSaDrKHS2Y/oD9AfD9PiCRev6GhNRjO4uvP4A6Wgnqbf25QPKfABKZkqXp/MDjGO9DBx+D6f/ON4Tl9N12+aSa4ts2k5s+RkjX/+htzA/aCP60J2DweRfXEfyI7/tOgUzN15P22G6tm0s+X3opntILlo2mpdhXMoFZ+O9r0ZzvN4vxM1U3ReqdDuz6P1C3Oi+KG/RokUArouWlfETmWGmswx9bm1McNVFZadKpf1N+fHNOdaTn1KZ2yf25E4iG7Yw8M7bpDs/wPB46VmwdLCVw+u/pGvIBzJOX1k+07i6leQ//IjQn60vmTaZ9DcRAGIbW/NjmLtxm+tf+3OVMkfMWiWTxH74eL633pwVK4kvWV523V2ucmRggfuawanMMgz3gXe86560bkpmCrXyEJHZSlU9RSaRaRj4k934D+0nkOyesKpxuWBiaJXLSVewNqakGmZBEJMbn5NOua/3e/8QDg4D77xFncdDMNGNxzQJRo+6Bg+h6AcE/+NVvIfezQd9+d8/uTPTguG+bZBOlzwXuTVuufHGn3uS8OWDlT8LK2WOVO2vMREldvfNpI4cJP7UI8R276TjW9dSHx+5QubQceSCrt5AuOKnf7zKBWe5QHdc1T9VPXTS5d5PAocP0NTdjr/tvQl9X5lNpu09VERkGinjJzJJanE6UW5tTHLv8yVr59wKhngi89yLqyw+ic47twxOlVz3Vfy+QCYgdAke+n/+T5n1emtaXX+fb73gUpzB9a/7wYhrpUx/coT+iOMoClEVWYZhxj/edU9aNzW5XN9PckWDLlo/o99XRERkaijjJzJJhs2uzFC5rFW+uMyFFxP52rcIbX+46INnLjORinURvnJzcXat9atEH723uMjJQ3cy8N5+vAuXuGYSndw0ymxrhJLf4wwbZAz96/5AtlJmbPkZdDfNG5ymOULWyrXv3yiCm+nOMgw3/vFmJKsho1nLXN9Pstnumf6+IiIiU2NKMn6WZS0FHgNOANLAg7Zt321Z1lzgKWAZcACwbNvuyh6zCbgESAFX2bb94lSMVWTC1FjJ8Pzat0CQyNYdDPQdA3+QRC5rNaSpeuKJHfjOPhejvoHmG+8h1X8cj+khfbwX+vuLzu309WGkHQba3i/NJGaLwQDumcYvXYVzrJfwhhtIY2AaxpgCKtMwqAsECK1phbRDcu/zpNrbigK7iWqNMF3VL4cb/3gzkuWOB/Anu4uuVcagzPvJcNluERGRQlM11XMAuNq27X+xLCsI/NyyrJeBLwF7bdu+1bKs64DrgI2WZZ0JrAY+DCwC/qdlWafZtp2aovGKjFstTX0rO201W5SlUGMiSuKJHSU960Jr1hF7/geZyptr1pF4/gek2tuAbNbONKibfwLxv31isHDKb3ySrr++Mb9frl1D5Cs3ggHG/EWkDx4g+ug945pOm7u+znLT6LKB3URM15zOKcAjjT+XkcwHEKMcz9DjTXC9VueTn5rIy5oVyr2fjJTtFhERyZmSqZ62bR+xbftfst/HgdeAxcAFwPeyu30PWJX9/gLgSdu2+2zb3g+8CXxiKsYqMlHGMvWt0mIw+SIPRw8S6TpKaP/rhLvb8ZiV/5MeTeGZUU1bjXZmpp9lg778/rsfwnfOeYPff+azAPmg0AxF6H78fny/94f0vPISxpKTiTWfQGjtZcXP4YUX0//BEVKLTqLfNIne961xT6ctN42u6eqbSgKy8U7XnO4pwFM53bTctXJUrXdGy/X9ZHUrPa+8pCm1IiJSkSkv7mJZ1jLgN4CfAAts2z4CmeDQsqz52d0WA/9ccNjB7Lah57oUuDR7PC0tLZM4chmO1+vV8+/C+eSnmLf8cVJdHXgizTB/EQ1lgjMnnWbg//4jXX99Qz47Evmrm/D+9h9gFByT26/7e98hcO4FdBQ1J78O7x/8J0yPZ/hxVfhYOQMfHHSdZlbXk6DlpFOKtw/0cWzfr8pOS8t9P+e0j9D8jTtwjh+nv+0wPT98jFR7G7End9Ky7bsYJ32IBtNkYN6CwUb1OJmm7+1tNH/rDMCpeFzDKXd9xvHjzG1urvg843ms0Y55Jih3rU60k5YVK6dpVDNX/v2ksx2z0Ue6v5/mT35q2PeVmUT/HxE3ui/Eje6LsZnSwM+yrADwQ+Artm3HLMsqt6tb6qHkz9K2bT8IPJj7vRo5Th810hyGtwHmZf9u0dlZdjd/spt4NhCDzAfkrr++oaQPWm6/wKq1JVm16I5biSw5kc6mecMOqdLHygkEmgiuacVIZ/vj7X2edDxKvy9AdMjrbtY1EDrjrLLT0nLf90eaIdpJbPvVRcc7fX30xWMks89VoL6RxJ5dmMEwvnPOw3/O+WCa9AdCGOm06+O4jWs4AV9wQs5TbY81kslea1juWo3wXL1fjJW3AeYvKd42zPvKTKL/j4gb3RfiRvdFedkG7q6m7E+ElmXVkQn6dtm2/Ux2c5tlWQuzv18IHM1uPwgsLTh8CaC5QVLbKu2Dlt0vX9RhyP5OR/k3wtz0Tk/b4Yp7rpmGAQcPkHjmcWJPPUx8zxMEzv88oWtucZ1elnYc+ue2EF731eJpaWvW0bP3haIpr5VUyewJhAlt2k5o9ZdJ7NmVGcMzj2EcPEBvMDIhlSQrnZY7EX0Zq6X65Ug9CydCuWtlfvn/KYmIiMjkmKqqngbwMPCabdt3FvzqOeCLwK3Z/z5bsP37lmXdSaa4y6nAT6dirCKToZLMSqXFYAqDJddsSrP71IfCoiLBVRdVXHgm17R86Hq90PaHy2eH2o8Se/KR/BRN6utJYxBa91VSCxYNVpGssEqm1zTpfOjO4jHcnemvNxG98Sop2jJRRVmqop8f5dfflcv6jkW5a62FaYkiIiIzzVRN9fx94GLgV5Zl/Wt229fJBHy2ZVmXAO8CnwewbfvfLcuygV+TqQh6hSp6ylTymCaBrjacjnbM5nnEI/MHe72NUqUBg1sQFL5yM6lEjED294XBUuKJHSWtDcLrN5KILACXsRZ+0K+0ATtQNhPpdHeCP+Qa1BrhuaTjUeJPPZI/xqivHwwqCqpIjhQENSai9L/2atkMZdrfNK5KlDkjVbQcT6Dk9hxNxJjHZYrajYy3UqiIiIhMjCkJ/Gzb/kfc1+0BnFPmmK3A1kkblEgZHtPE/6uf0rVje1FAlfzoJ8YU/FUaMBQGQUZ3F14Doju+TerIwZJgsef0lQQ23orZ2Ubz9beT6urAmFNPqrERp9wH64IP+qn2tkwD9lVr8S4/vSgLN9RwmUjTMPD9x6v5jKBRX09owxZ6Tz+r4n53IwYG0c584/ZpbY0xxkBpOts3DKeW2o2IiIjIyDTfRmSIQFcb0WzQB7miKdsJdLWN7YSVrt1jMAhymiJ0br2W1JGD+f0LS/6nHQcHh+j2r9O+5Uq6/vpGuh++i/S+fyPwzn+4rj8bup4u1d5GYs8uUgsWDVvWf7g1ab5Ed+k00LtvpjEepef0lcy763FCN92Tn5Lp9hgjrZszwnNJvvIiodWtRWMIX7l5QtfFVTKOkdYjupnu9g3lVMtaw4kyEesvRUREatmUt3MQqUaFU/FMjwczGCbVNxjo5YumjFAt082YMiuVZJcK9vG0LCBw3udKp24WBFuVrqcbym06Zm8wQmO8C8/7hwiuuojk3ufzTdadvj7MjqOk/SGME5aQ9DZkL8A96BspG9YTCBO4aH2mKXy2sXvdGSuJL15OeozTb8c6jrE8f1M1pXK0qmWt4USo1qyqiIhINVHgJ7Oe64fGNetIPP+DfDAzXNGUkYwlYKgkWCzcx3fOeaUN04dMJx3PB/3C6ZimYeB7/ZfF17O6lcQLT5Nqb8s8V42NFT03lUyDzY3bv3FbftyxQHjCgr7RjmO0z181T6mslfV3U1GoRkREZKbTVE+Z9Vw/NO5+CN9nPgtQXDRlDAoDhpGmPeZUMg2vcB8DAzMYJviFLxP6wiWEvnAJZjBcMp0090E/uWjZsNM7h+P6fD25E9855+WD5oFAqLKTVTgNdiLGPVnjGGmKYa1NqaxKo5hOLSIiMlsp4ydS5kNj3SkriGzajtHcQiKyYMxVPWH0mZV8sHjbI3gTMZzeHtLNC9z32b4Tb18fgcZGYrsfKspaOpHmMY95qNx02HI9AL0nLCZw4cWw+CR6faGKskfVkg0b6zgqmWJYS1Mqq1W13EciIiLVTBk/mfXKFe0YaFlAbPkZdDfNG1fQNxqF2SNfohuj7TBdN32Frm9eTWzjJUUNtotaBHjMfNAHg1lLIz0xwUVhs+/U2//h+nwZJyzB+J1P0XPyioqLuFRLNmys46i0cMukZyxnuWq5j6R6qfiPiIgyfiJjL9oxwVxbI6xZly80U7huqTcQLso0hda0DttrL3f+kZrIF46lcF/HNIt7AK69nNiuB4rGmUrE6J23KD/90ZfoZuDgWzTVN5AKNEHbIWK3by7JjFVDNmzMWbkqLdwy2yirKsNR8R8RkQwFfjLrVcuHRtfWCLsfIrBqbb4Rei6oaISiTNNIfe5G88HHbd/wFZvyAWiqvY10Ok3gwosx0pm2Eonnf0A6Hh0MSl0CWBp8rkFscoIasI/XWAqdaIph9aiVQjUy8VT8R0QkQ1M9RaiOqXhmR5tr9shgcEpSPqgYkmlK7n2+pM9d4VS30fSSc9s3et+2fLEbAGOgn/juncSeepj4U4+Qas+M3ejuyhzvEsCmuzvxnXNe0bVNRvGNqZzSpSmGIjOAiv+IiADK+IlUDaPR55o9ckwj/31uCqov97vsvqn2NhIvPUtk6/0MJJOlWcvRTEkss6936fL8Y3qXnOQ6Vo/fz0CZ4zPrDV2C2AmUy1YmntiB7+xzwTQJnXEW8cUnT8o6zWrJFotIecrMi4hkKOMn006L7jNSgRChNeuKs0drL6fulNMJrWklsvX+/NRMt0xT4KL1xMPzXbOW5QrYuH3wKbdvetGJ+ZYUjs9fmmFc3cpAX1/Z4x3TKAliJzoz1piIZhq9n3sBiT27iO3eSefWa/C/9otJu6+qIVssIuUpMy8ikmE4tfMhxTl8+PB0j2HWamlpob29fdTHadH9INMw8O1/nfTb+zJr50wDMxQh8cPHSLW3EbrpHpKLluX395gmga42nI52jOZ5JCLzy2a1XAvHbNhCz2mVrfEb+poEkt0ktm/Cd/a5GBg4OPS88hL+jdvKr/FbchLO/EXQ1QFNEXrHmBkbrkiN/9B+nNd+SWLPrpK/7ms9T/UY6/uF1LbJvC/y7xvZzPxY339k6un9Qtzovihv0aJFUDjFqoCmesq00qL7QWnHoefkFYQic+n/+Y8hnc4HfUOzc6Zh0Pj6L+kaTcBcN4fg6nV4wmGMBj9GUxjDMEqKYFQyfbEnECZw0Xr3SqiAs2QZkRvvxuhJQqOP/kCIXl8oc47GYOYkYwz6hgtKjfBcME1V2hSRIir+IyKiwE+m2ywrh18uW5XbbnR3gT+A55QVpN7aBxSv7ct9WBltwNyYiNLzzBME/ui/EH3gtsFqnVduJrHiYyXB4kgfksoFh4BrYNY7f8mE/HV9pOvuCYQJnXGW1vOIiIiIDKE1fjKtRrP2bKYrbIIeu3EDsY2t+Pa9isc089u7b7iKzuvXk35vP8l/+BGhP1tP8LZHSjN5o61Sl4gRWvXfie68s7ha5723uFb2rITb2rbRVA8dkxGuO+04xBefTPjKzVrPIyIiIlJAgZ9Mq9m06L5cUBToaivd/uROfGefS/S+bXiPH6Px4NtFhW9GCpiHFsyp8/npP/DG5Jc0n+Sy6ZX8oSCVTpNY8bF8IZrg9p2zcs2oiIiISCFN9ZRpNavK4ZcJipyOdvf2CScsIrjqImg/SnLnXaTj0fx6tlzA7LbGzqR0umX4ik1Q794uYiKzq5NdNn246y6cjjpV63mGKzQzWabjMUVERGTmU+An0262LLovFxSZzS2u2weOvk/8qYfzrRISLzxdvJ6tTMDsT3a7NmAPffEvCbd+NT/dM7/Gb0jQNB6VBmZjVU1/KJiOirSqgisiIiJjpXYOMiFUVndk5T609644i8bXf1m8fc06Es//gFR7G5BtR/CFS3D6juFdfjrpBYtKAp5cJsjTdpiubRtLHr9503b62w7hbTmBgfcPMmf5acSXfoiBERqbj1SQpuz27i4a5p9AV13juIOSasxy+ZPdxDe2TmnbiOl4zMmg9wtxo/tC3Oi+EDe6L8pTOweRcZqIwKNstiqdzm83uruo83jpuuumfNAHmaydp3ke0ftvdc30FAaVwVUXuWYQjx94g/junfkMYtd3b8e/cRsDZQIG0zBo7InhOfAG0fu2jRysFownl8FtbGkhPc435qrNck1HRdpZVgVXREREJo6Ku4iMoFw1zlyhldFwq4RZyHEcnIYG0vFo0XbPwiUYcxoIrrqI0BcuwQyGi6plFhaOSe59ntDq1uKCOWvW0fPy32Ueo6B4TLmiK7lrdn78v/JBX/7YcgVp7rmZYPRovqDMWJ4fN5NeKXSMpqMi7WyqgisiIiITS4GfyAgmO/AYGlhG79hCeP3G/Ad8z8IlhKwv03XXjcSeepj4nicInPc5zGB4MHAryASl2ttIvPA0gVVriWzaTmTLXUXTRnPXYJhm2YAhd81G2hlVQZqBn/+4KDh2RphGWpFJrhQ6VtNRkXY2VcEVERGRiaWpniIjKQg8PC0L8J1zHgYG3p4k5gSsNRsaWKaOHCT21CNEtt7PQDKJ1++n6/q/KGn3ELjw4nzgNrRwTKq9jcSeXZnpo1CSQTTq66k7YyWxckVXCq55NAVpcoFeLjiet/xx8DaM6/mZ7EqhYzUdhWaqqbiNiIiIzCzK+ImMIBd4eFoWEDjvcyT27CL21MN03bRhzFM+C/vseXsSmexdgdSRgwwkkyQXLWMgkXBv97B0eT7TM1wmyO134Ss3E1+8vGzAkLtm12mjV20hHllQ+nirW+nZ+0LRGFNdHaN+boaq5izXSFN3a+UxRUREZOZTVU+ZELVcXSk3FTN9cD+JZx4fd0VF12IlblU8s+cNJLuJuVRyDG1/mIQ/VHTeXDVNmiL0FmSChvvdSGM0g2F8n/ks3qXLSS86kZ5AU0n1zjq/n+gdN5A6crBojPPuepyOcWb8xjJ+qW61/H4hY6f7QtzovhA3ui/KG66qpzJ+IiPITa/zfuiMUa81K8zs5QqeuK4Z3P0Qvs98FijNaJXLePUEioPN4TJBo80SFU4p9F95PcbvfIrEaR8l4Q/ljy08Zyw8n8BF60vGyPxFIz29FVGWS0RERGR8tMZPpAJpxyHdPH9Ua83KtSEgEHSfuvmhMwnddE/Juq3pWtdV2JYhM8jyj1dujA2m/rYkIiIiUg30qUxmPLes2mQYmnnzLFzC3Otvw+jucn3cctVAPf6Aa0n+VPO8shmtmZDxmgljFBEREZmtlPGTGW2imntX0qC9KKuVjOPp6qBz67XlH7dMG4KBvmOErtpSOuZyFTZFRERERMZJgZ/MaOWyaqMpuDKa4DGX1fID0Zv/atjHLduGwB+kZ8FSleQXERERkSmjqZ4y4xRO7fR0HB13c+8xNWivoKn4cG0INC1SRERERKaSMn4yowzNzoXWrBt/c+/hgrgyWcNKmoqr2fbwKpleKyIiIiITQxk/mVGGZueSLz+XD/5gbM29c0Fc0bYRgsdKm4ors+cuF8DHN7YSu3EDsY2t+Pa9OmmFeURERERmO2X8ZGYZkp1LtbeReP4HRG64i4H+/jFl1XJB3GiKrSibNz4TsTZTRERERCqnwE9mFLcplul4lAFfYDBgGGXwNdYgbjR97mSIMUyvFREREZGx01RPmVEqnWI5WpqSObXGMr1WRERERMZOGT+ZUTTFsjaMZXqtiIiIiIydAj+ZcTTFslQlFTKrqYqmAngRERGRqaXAT2SGq6QB/Wia1E8VBfAiIiIiU0dr/ERmuEoa0I+pSb2IiIiI1AwFfiIz3XAVMkezj4iIiIjULAV+IjNcJRUyVUVTREREZHZT4CdTzjQM/Mlu/If2E0h2YxrGdA9pRqukxcVktcEQERERkZlBxV1kSlVjkZGZrpIKmaqiKSIiIjK7KeMnU0pFRiZHJQ3o1aReREREZPZS4CdTS0VGRERERESmnAI/mVIqMiIiIiIiMvUU+MmUUpEREREREZGpp+IuMqXGU2TENIzMWsBoJ0Z4roqTyLTTPSkiIiIzhQI/mXK5IiP4mzIbKgz6proaqD7Uy3BUoVZERERmEk31lBlhsquBDu0t6DFNfPteJb6xldiNG4htbMW379Vp7zmoHojVQxVqRUREZCZRxk9mhuGqgeYyh2PklrkJX7mZ2K7vlnyoD27fmclWTgNlmKrMJN6TIiIiIhNNGT+ZESazGqhb5iZ67y34zj63aL/pbjuhDFN1UYVaERERmUkU+MmMMKnVQMtkbgyz+J/HtH+oVw/EqqIKtSIiIjKTaKqnzAjjqQY6klzmpjCoMurrqTtjZX57flplIFxRMZrJUG6cyjBNj8m8J0VEREQmmgI/mTHGUg20ErnMzdC1c/HFy6vqQ325cU5nMDrbTdY9KSIiIjLRFPjJrFc2c5NOV9WHemWYRERERGSsFPiJMHMyNzNlnCIiIiJSXRT4yYyhhuq1Ra+niIiIyNRR4CczgnrY1Ra9niIiIiJTS+0cZEZQD7vaotdTREREZGpNScbPsqxHgPOBo7ZtfyS7bS7wFLAMOABYtm13ZX+3CbgESAFX2bb94lSMU6rYcD3scuvdZObQ6ykiIiIypaYq4/co8CdDtl0H7LVt+1Rgb/ZnLMs6E1gNfDh7zP2WZXmmaJxSpXI97Iq2jaGHnWkY+JPd+A/tJ5DsxjSMiRymVMgIN0/I6ykiIiIilZmSwM+27VeAziGbLwC+l/3+e8Cqgu1P2rbdZ9v2fuBN4BNTMU6pXrkedrlgIbcmrDcQrvgcuXVl8Y2txG7cQGxjK759ryr4m2KmYcAHRwitWVf8em4Y3espIiIiIpWbzuIuC2zbPgJg2/YRy7LmZ7cvBv65YL+D2W0yi01ED7ty68qC23dmWiTIlGhMRIndvhkzGCawai0GBo5p4CxZpsIuIiIiIpOkGqt6uqVfXD8NWpZ1KXApgG3btLS0TOa4ZBher3dqnv/m5vy3jaM8dOCDg67ryup6ErScdMoEDE6Gcrsvcq9Dqq+N+FOP5Lc3f/S3aFl68lQPUabBlL1fyIyi+0Lc6L4QN7ovxmY6A782y7IWZrN9C4Gj2e0HgaUF+y0BDrudwLbtB4EHsz867e3tkzZYGV5LSwvV/vwHfEGM+vqi4M+or6ffFyBa5WOfqdzuC70OMhPeL2Tq6b4QN7ovxI3ui/IWLVpU9nfT2c7hOeCL2e+/CDxbsH21ZVn1lmWdDJwK/HQaxic1ZiLWCcr46XUQERERmXqGMwVraizL2g18GmgB2oAbgD2ADZwIvAt83rbtzuz+1wNfBgaAr9i2/aMKHsY5fNg1MShTYKb85cU0jEyvuOw6wd5RrhOU0Sl3X+h1mN1myvuFTC3dF+JG94W40X1RXjbj51q5cEoCvymiwG8a6R+guNF9IW50X4gb3RfiRveFuNF9Ud5wgd90TvUUERERERGRKaDAT0REREREpMYp8BMREREREalxCvxERERERERqnAI/ERERERGRGqfAT0REREREpMYp8BMREREREalxCvxERERERERqnAI/ERERERGRGqfAT0REREREpMYp8BMREREREalxCvxERERERERqnAI/ERERERGRGqfAT0REREREpMYp8BMREREREalxCvxERERERERqnAI/ERERERGRGqfAT0REREREpMYp8BMREREREalxCvxERERERERqnAI/ERERERGRGqfAT0REREREpMYp8BMREREREalx3ukewGxhGgaNiShEOzHCc+kJhEk7znQPS0REREREZgEFfpOkONBrhg+OELt9M05fH0Z9PaGrttBz+koFfyIiIiIiMukU+E0C0zDw7XuV2D03DwZ6a9ZhBsOk+tpw+vqI3XMzwe07Sfqbpnu4IiIiIiJS47TGbxI0JqL5oA/IBHq7H8J3znn5fZy+Pujumq4hioiIiIjILKLAbzJEO/NBX47T14eBkf/ZqK+HpshUj0xERERERGYhBX6TwAjPzQR2hdvq63FMI/996Kot9AbC0zA6ERERERGZbbTGbxL0BMKErtpSvMZvwxacJcsIrVgJTRFV9RQRERERkSmjwG8SpB2HntNXEty+M7OOrzDQawxmdlLQJyIiIiIiU0SB3yRJO06mYmeuaqcCPRERERERmSZa4yciIiIiIlLjFPiJiIiIiIjUOAV+IiIiIiIiNU6Bn4iIiIiISI1T4CciIiIiIlLjFPiJiIiIiIjUOAV+IiIiIiIiNU6Bn4iIiIiISI1T4CciIiIiIlLjFPiJiIiIiIjUOAV+IiIiIiIiNU6Bn4iIiIiISI1T4CciIiIiIlLjFPiJiIiIiIjUOAV+IiIiIiIiNc5wHGe6xzBRauZCRERERERExshw21hLGT9DX9P3ZVnWz6d7DPqqvi/dF/py+9J9oS+3L90X+nL70n2hL7cv3RcjfrmqpcBPREREREREXCjwExERERERqXEK/GSiPDjdA5CqpPtC3Oi+EDe6L8SN7gtxo/tiDGqpuIuIiIiIiIi4UMZPRERERESkxnmnewAyM1iW9QhwPnDUtu2PZLfNBZ4ClgEHAMu27a7s7zYBlwAp4Crbtl+chmHLJLIsaynwGHACkAYetG37bt0Xs5tlWQ3AK0A9mf/HPG3b9g26LwTAsiwP8DPgkG3b5+u+EMuyDgBxMq/zgG3bH9d9IZZlhYGdwEfItGz7MrAP3RfjooyfVOpR4E+GbLsO2Gvb9qnA3uzPWJZ1JrAa+HD2mPuz/7OX2jIAXG3b9hnAJ4Ersq+97ovZrQ/4I9u2zwI+BvyJZVmfRPeFZGwAXiv4WfeFAPyhbdsfs23749mfdV/I3cD/sG17BXAWmfcN3RfjpMBPKmLb9itA55DNFwDfy37/PWBVwfYnbdvus217P/Am8ImpGKdMHdu2j9i2/S/Z7+Nk3pQXo/tiVrNt27FtO5H9sS775aD7YtazLGsJcB6Zv+Ln6L4QN7ovZjHLskLA2cDDALZtH7dtO4rui3FT4CfjscC27SOQCQKA+dnti4H3CvY7mN0mNcqyrGXAbwA/QffFrGdZlseyrH8FjgIv27at+0IA7gK+RmZqeI7uC3GAlyzL+rllWZdmt+m+mN2WAx8Af2NZ1i8sy9ppWZYf3RfjpsBPJoPhsk3lY2uUZVkB4IfAV2zbjg2zq+6LWcK27ZRt2x8DlgCfsCzrI8PsrvtiFrAsK7dG/OcVHqL7Yvb4fdu2fxP4z2SWDJw9zL66L2YHL/CbwA7btn8DSJKd1lmG7osKKfCT8WizLGshQPa/R7PbDwJLC/ZbAhye4rHJFLAsq45M0LfLtu1nspt1XwgA2ak5/0BmzYXui9nt94E/zRbyeBL4I8uynkD3xaxn2/bh7H+PAn9LZoqe7ovZ7SBwMDtbBOBpMoGg7otxUuAn4/Ec8MXs918Eni3YvtqyrHrLsk4GTgV+Og3jk0lkWZZBZv79a7Zt31nwK90Xs5hlWfOy1diwLKsR+E/A6+i+mNVs295k2/YS27aXkSnC8Pe2bV+E7otZzbIsv2VZwdz3wLnAv6H7Ylazbft94D3Lsk7PbjoH+DW6L8ZNDdylIpZl7QY+DbQAbcANwB7ABk4E3gU+b9t2Z3b/68mU3h0gMwXwR1M/aplMlmX9AfB/gF8xuGbn62TW+em+mKUsy1pJZtG9h8wfF23btm+2LKsZ3RcCWJb1aeCabDsH3RezmGVZy8lk+SAzve/7tm1v1X0hlmV9jEwhqDnA28Cfk/1/CrovxkyBn4iIiIiISI3TVE8REREREZEap8BPRERERESkxinwExERERERqXEK/ERERERERGqcAj8REREREZEap8BPRERqimVZiWyZeBEREclSOwcREZm1LMtygFNt235zgs/7l8CXgI8Cu23b/lIFx3wS+CbwW0AK+AfgKtu2j2R/bwC3Aq3ZQx4GNtq27ViWNR+4G/gU4CfTBPurtm3/JHvsQuC7wMeBhcDJtm0fmIBLFRGRGUIZPxERkQliWZY3++1h4BbgkVEcHgEeBJYBJwFx4G8Kfn8psAo4C1gJnA9clv1dAPi/ZILGucD3gBcsywpkf58G/gfw30ZzPSIiUjuU8RMRkapkWdYBMlmqi8lkqfYA623bPmZZ1jpgI5kg5x+By23bPpw9Lp/FsyzrUSBJJpg6G/g18N9t237LsqxXgP8H6AEc4BJgL/Ao8AdkgqV/Bz5l23Z6hHHuANYCpwN+27YHsr+7BVhSScbP5by/Cfxv27aD2Z//CXjUtu0Hsz9fAqyzbfuTZY6PAX9o2/bPC7Z5gX6U8RMRmXWU8RMRkWq2Fvhj4BTgNGCzZVl/BGwDLDIB4TvAk8OcYw1wE5mM2pvAVgDbts/O/v4s27YDtm0/BVwNHATmAQuAr5MJCkeyBjgPCOeCvglwNpnAM+fDwC8Lfv5ldlsJy7I+Bswhc70iIiJ4R95FRERk2nzHtu33ACzL2grcSybYe8S27X/Jbt8EdFmWtaxMFusZ27Z/mt13F3DnMI/Xnz3/Sdl1f/+nwnHekxvnRLAsayWwBbigYHMA6C74uRsIWJZl2LbtFBwbAh4HbrJtu3B/ERGZxZTxExGRalYYTL0DLMp+vZPbaNt2AugAFpc5x/sF3/eQCaDKuY1Mluwly7LetizrujGMc1wsy/oQ8CNgg23bhYFnAggV/BwCEkOCvkbg74B/tm1720SNSUREZj4FfiIiUs2WFnx/IpmiKYfJFD8BwLIsP9AMHBrvg9m2Hbdt+2rbtpcDnwW+alnWORUcOiEL5i3LOgn4n8A3bdt+fMiv/51MYZecsyiYCmpZVj2ZdZCHGCz6IiIiAmiqp4iIVLcrLMt6nkym7uvAU2QKsDxpWdb3gdeAbwE/GWOxkjZgOdm1cJZlnQ+8DrwFxMi0VUiN9qTZIipewAN4LMtqAAaGW/9nWdZi4O+B+2zbfsBll8fIBKL/L5lA82oyU1+xLKsOeBroBf7MrRhNdgye7I/1lmU12LZ9bLTXJiIiM5MCPxERqWbfB14iM73zWeAW27Z7LMv6BvBDMgVb/glYPcbz3wh8LztF8lIy00W/Q6a4Sxdwv23b/zCG824Gbij4+SIyBWZuHOaYVjJB6A2WZeWPtW07NzX1u9nf/yr7887sNoDfI9PeoReIWpaVO/w/F0wX7S14rNez/zUquxwREZnp1M5BRESqUrZNQqtt2/9zusciIiIy02mNn4iIiIiISI3TVE8REZFhWJZ1IpnG727OtG373VGc6+tk1ioO9X9s2/7PYxmfiIhIJTTVU0REREREpMZpqqeIiIiIiEiNU+AnIiIiIiJS4xT4iYiIiIiI1DgFfiIiIiIiIjVOgZ+IiIiIiEiNU+AnIiIiIiJS4/5/6DEqla4tqw4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating an figure\n",
    "fig,ax = plt.subplots(figsize=(15,8))\n",
    "\n",
    "# Generating a scatterplot\n",
    "sns.scatterplot(x=top_2021['points_r1_2021'], y=top_2020['points_r1_2020'])\n",
    "\n",
    "# Adding a title\n",
    "plt.title(\"Average Points 2020 vs 2021\")\n",
    "\n",
    "# Adding lines to show average for 2019 and 2020\n",
    "ax.axhline(top_2020['points_r1_2020'].mean(), linestyle=\"-\", color=\"black\", label='2020') \n",
    "ax.axhline(top_2021['points_r1_2021'].mean(), linestyle=\"-\", color=\"blue\", label='2021') \n",
    "\n",
    "# Adding a legend\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec3470e8-b348-4bf3-9e61-4305d09b81a4",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "799a2f85-a50a-4614-92aa-f9e32587c818",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11d2b26f-68e9-4768-91fc-d6fe557905b8",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3928d669-5d1c-403c-a9a8-a07bd2f8bbc7",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7858649f-8a6f-4b6b-9ebb-8c4f04c545f3",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2af2dfa-5524-4eca-8ecb-0b49ed487fa7",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "d3b78c5e-5c47-4c0f-ab25-85a86c8e4679",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "An average increase of: 7.496508583937766 between 2019 and 2021\n"
     ]
    }
   ],
   "source": [
    "# Getting the increase\n",
    "avg_increase = average_2021 - average_2019\n",
    "\n",
    "# Getting the %\n",
    "increase_percent = (avg_increase/average_2019)*100\n",
    "\n",
    "print('An average increase of: {} between 2019 and 2021'.format(increase_percent))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d825c0fc-00fb-49bd-8046-3c841b32858d",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73945118-ed75-46b2-b70c-238cd46d92f5",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "02f620b4-ff24-4c82-99eb-923b4882bb6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f082c92730>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating an figure\n",
    "fig,ax = plt.subplots(figsize=(15,8))\n",
    "\n",
    "# Generating a scatterplot\n",
    "sns.scatterplot(x=top_2021['points_r1_2021'],y=top_2019['points_r1_2019'])\n",
    "\n",
    "# Adding a title\n",
    "plt.title(\"Average Points 2019 vs 2021\")\n",
    "\n",
    "# Adding lines to show average for 2019 and 2020\n",
    "ax.axhline(top_2019['points_r1_2019'].mean(), linestyle=\"-\", color=\"black\", label='2019') \n",
    "ax.axhline(top_2021['points_r1_2021'].mean(), linestyle=\"-\", color=\"blue\", label='2021') \n",
    "\n",
    "# Adding a legend\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74df6047-b38f-4e60-bf9c-4b42b25c3e49",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f156c19-6190-4e21-997e-a33fe567935d",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cebe3e86-fe9a-4068-9d12-015c324db5b9",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d975fb3-dec8-4b56-a79c-feb01dbb5794",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66fbfbed-51ef-449d-bf61-5af08fa52e90",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7d691e5-a7dc-47d3-874a-b7aace6773a5",
   "metadata": {},
   "source": [
    "### Heatmap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48185e68-f2ca-49d3-93ae-53db2450dfd8",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "6a05c024-cf69-4e50-8ac9-28613c280cbe",
   "metadata": {},
   "outputs": [
    {
     "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>points_r1_2021</th>\n",
       "      <th>points_r1_2020</th>\n",
       "      <th>points_r1_2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>points_r1_2021</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.960016</td>\n",
       "      <td>0.910599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>points_r1_2020</th>\n",
       "      <td>0.960016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.955069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>points_r1_2019</th>\n",
       "      <td>0.910599</td>\n",
       "      <td>0.955069</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                points_r1_2021  points_r1_2020  points_r1_2019\n",
       "points_r1_2021        1.000000        0.960016        0.910599\n",
       "points_r1_2020        0.960016        1.000000        0.955069\n",
       "points_r1_2019        0.910599        0.955069        1.000000"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = allcourses.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3153a84-525f-4eae-be90-67df1b8fe04f",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d49d373-b113-40de-97b3-b4b710255afa",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "5a778c6e-b65d-4338-9198-33c5f4f0ca62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr, annot=True, linewidth=1, cmap='coolwarm', square=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbbc526c-89ec-468f-93e2-776d035a73f3",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f16b63a-8eca-433e-859f-38a5f5335152",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4468a40-107a-47d0-baa5-f849cd59a93d",
   "metadata": {},
   "source": [
    "The heatmap is as expected in that sequent years are more related to each other that 2019 and 2021. It is interesting that 2020 is equal 0.96 in relation to both 2019 and 2021. The colour of the squares would suggest it has slightly more correlation with 2021's points. The output from the `.corr()` method above confirms this."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66a9226a-9a68-4d40-b697-55cd2c7981b2",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c6a0ba9-1289-4090-91f7-b0d0732e1fb3",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33caa9fe-707b-4ec6-9d14-c78fccb734d0",
   "metadata": {},
   "source": [
    "### Points Increase"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c1b4f01-7d58-412e-b0a6-b91d7f440971",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce132d34-51c1-4af9-901b-faae6915fe66",
   "metadata": {},
   "source": [
    "So now we are certain that in the majority of cases, course points have risen over the past three years, let's try and work out by how much. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae9b8cf2-626b-4e37-8c79-5e29b580f811",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b531d730-3799-492f-8aea-e7f2b70f3496",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "b902e622-10b6-41b0-8a13-0aefcc239c8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting sum points for each\n",
    "sum_2019 = allcourses['points_r1_2019'].sum()\n",
    "sum_2020 = allcourses['points_r1_2020'].sum() \n",
    "sum_2021 = allcourses['points_r1_2021'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b0c8e26-5d97-48b0-b9da-a47c33f31df7",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "99912e03-1189-4d21-8b79-8ace10521f61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "An increase of: 16.311328209180374 between 2019 and 2020\n"
     ]
    }
   ],
   "source": [
    "# Getting the increase\n",
    "sum_increase =  sum_2020 - sum_2019\n",
    "\n",
    "# Getting the %\n",
    "sum_percent = (sum_increase/sum_2019)*100\n",
    "\n",
    "print('An increase of: {} between 2019 and 2020'.format(sum_percent))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b82f839-9b76-4b1c-bf5f-6edbf8aea42b",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b0ae9b8-cce7-42c5-8d71-73e6fdcaee6f",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "bf7a2ed1-023d-45d1-aa3a-81e2d5fcdc98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "An increase of: 14.051433086703542 between 2019 and 2021\n"
     ]
    }
   ],
   "source": [
    "# Getting the increase\n",
    "sum_increase =  sum_2021 - sum_2019\n",
    "\n",
    "# Getting the %\n",
    "sum_percent = (sum_increase/sum_2019)*100\n",
    "\n",
    "print('An increase of: {} between 2019 and 2021'.format(sum_percent))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd187737-35fd-423b-b76b-88cdf8c73e29",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21490cc8-9234-4ac9-9412-d76b74f0d93d",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "0fd68df7-3b84-418a-a812-703a7acf1b8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "An increase of: -1.9429707813262351 between 2020 and 2021\n"
     ]
    }
   ],
   "source": [
    "# Getting the increase\n",
    "sum_increase =  sum_2021 - sum_2020\n",
    "\n",
    "# Getting the %\n",
    "sum_percent = (sum_increase/sum_2020)*100\n",
    "\n",
    "print('An increase of: {} between 2020 and 2021'.format(sum_percent))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b66e61e-2c94-405e-92ab-9b54701eb1ef",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aa3b4a0-4fd3-44b5-880e-9ebf38234cb7",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcf7d642-286d-45b6-a386-07d9ed6bff74",
   "metadata": {},
   "source": [
    "I'm not totally confident in these numbers, as I think we have to take into account that 2020 offered more courses than both 2019 and 2021 and this would make the points sum greater. I think this needs to be taken into consideration. I'm going to try another method of checking if points have risen between courses. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0740848-7b89-4fbc-8d04-ed371fba55cc",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d64454a-aefc-44c6-b211-eeef1bfc6960",
   "metadata": {},
   "source": [
    "Using the `.gt()` function we can return whether a value in one column is greater than a value in another column [22].  We have to specify which columns we want to compare using `.iloc[]`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "743e64e2-1ef4-44da-903a-d5754330bd8c",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "69cee5ec-e112-4b2f-87f3-392d9df9bf90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "code\n",
       "AL801    False\n",
       "AL802    False\n",
       "AL803     True\n",
       "AL805    False\n",
       "AL810     True\n",
       "         ...  \n",
       "SG403    False\n",
       "SG432    False\n",
       "TL712    False\n",
       "TL719    False\n",
       "TL746    False\n",
       "Length: 1766, dtype: bool"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking  2020 against 2021\n",
    "course_diff = allcourses.iloc[:,2].gt(allcourses.iloc[:,4],axis = 0)\n",
    "course_diff"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "def766ea-803c-42a1-92a7-fea5c8de214c",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9089d3a8-b859-4e28-bbdd-94a14adc84ea",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "001c8bf4-5aa8-4d0b-866c-80d790f00df6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking a random row\n",
    "course_diff.iloc[200]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ab3618f-2f72-431d-b75d-8e9385a59147",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06ea44dd-f019-42f8-a4bb-659cbaf619c5",
   "metadata": {},
   "source": [
    "Let's check the total "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "218b2c8e-2506-437d-ae59-afb30e32336c",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "4971bf26-141c-4da1-bd89-8f370172ab4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    921\n",
       "True     845\n",
       "dtype: int64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many courses increased points (True) Vs didn't (False)\n",
    "diff_count = course_diff.value_counts()\n",
    "diff_count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07fdbb39-13a1-4bfc-af5f-8f17799ba6d3",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68cd9c46-3b5a-46ca-9c92-f2b9c543cd11",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "1cf7f8ae-ffe2-4b47-b948-ec248b075deb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Circle at 0x1f0831346d0>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setting Labels\n",
    "diff_count_labels = ['# of courses WITH a points increase', '# of courses with NO points increase']\n",
    "\n",
    "# Generating a piechart\n",
    "plt.pie(diff_count, labels=diff_count_labels, autopct='%1.1f%%') # Data, lables, colours, %\n",
    "\n",
    "# Adding a title\n",
    "plt.title(\"% Of Courses With A Points in 2021 Vs 2020\")\n",
    "\n",
    "# Adding a circle at the center to make it a donut chart\n",
    "my_circle=plt.Circle( (0,0), 0.8, color='white')\n",
    "\n",
    "# Current figure\n",
    "p=plt.gcf()\n",
    "p.gca().add_artist(my_circle)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcaf1e44-1258-4ff7-ab07-1ab9b2a9f958",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a7a39df-6505-46da-a33b-f1135ea5b0f9",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "072c2126-19c5-4e2c-8981-089bb3949f44",
   "metadata": {},
   "source": [
    "We can see that 52.2% of courses offered in 2021 increased in points over courses in 2020."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbda16d4-3dbf-4520-8347-23fc524ddfe5",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54ab3fc6-aa69-4cf4-9c27-b3024e6a1e60",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7459a9a5-4496-4394-a7ff-4b12bbea846e",
   "metadata": {},
   "source": [
    "***\n",
    "## Conculsion\n",
    "***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "975ea71c-b43d-4f40-bfe0-8d5e06283020",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "607e4ee2-3f45-4f63-8f4f-696b13fae8a9",
   "metadata": {},
   "source": [
    "- `Pandas` is an extremely robust and powerful software for analysing and manipulating data. \n",
    "- We can say that the introduction of accredited grades has led to an inflation in CAO entry point requirements. \n",
    "- The big rise in 2020 seems to have pushed points up across the board with courses now compensating for the inflation by increasing their points entry requirements.\n",
    "- 52.2% of 2021's course point requirements were increased from 2020.\n",
    "- Out of the three years, 2020 had the most courses on offer for candidates.\n",
    "- For all years, the majority of courses required within the region of 300-375 points.\n",
    "- Three of the top 15 courses in 2019, were not offered in 2021.\n",
    "- The data for all years can be skewed by courses requiring additionally entry requirements e.g., Medicine being 50 points but outside of the CAO needed a GAMSAT or other courses needed an interview, audition or portfolio which added to their totals and pushed points beyond 625 - which is the highest a student can achieve from a leaving cert exam alone.\n",
    "- The top courses in 2020 ran over multiple years.\n",
    "- The top 4 courses in 2020 and 2021 stayed the same.\n",
    "- There was a point rise across the board in 2020. \n",
    "- The “mid-level” courses had a substantial increase, just under double the amount of courses requiring 350+ points.\n",
    "- In 2021, the number of courses requiring 500+ points is over double from 2029."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d93e2814-10c7-460b-919b-6896de7c3b6a",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94212084-635c-45a3-a1a7-0b27f7720e90",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "004236b0-90a7-4b88-9d92-0dbe3ea91072",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## References\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff9cc6e1-9f4d-4ec1-9da6-29a1af9bd616",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7623a120-5e8c-48e6-921d-862fea1fcffe",
   "metadata": {},
   "source": [
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    "[17]\tIndependent. ie Newsdesk. Leaving Cert Results: How 2021 grades compare to previous years – and a reminder of the 2020 CAO points. Irish independent [Internet]. 2021 Sep 3 [cited 2021 Dec 8]; Available from: https://www.independent.ie/irish-news/education/leaving-cert-how-2021-grades-compare-to-previous-years-and-a-reminder-of-the-2020-cao-points-40814264.html\n",
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   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "#### Other References Used (Code Solution Help)\n"
   ]
  },
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    "\n",
    "[25] \tFix OSError: Unable to locate Ghostscript on paths for Python Beginners - Python Tutorial [Internet]. Tutorial Example. 2019 [cited 2021 Dec 16]. Available from: https://www.tutorialexample.com/fix-oserror-unable-to-locate-ghostscript-on-paths-for-python-beginners-python-tutorial/\n",
    " \t \n",
    "[26] \tDu F. Extract tabular data from PDF with Camelot Using Python [Internet]. Youtube; 2019 [cited 2021 Dec 16]. Available from: https://www.youtube.com/watch?v=LoiHI-IB3lY\n",
    " \t \n",
    "[27] \tHow to move a column to first position in pandas DataFrame? [Internet]. GeeksforGeeks. 2021 [cited 2021 Dec 16]. Available from: https://www.geeksforgeeks.org/how-to-move-a-column-to-first-position-in-pandas-dataframe/\n",
    " \t \n",
    "[28]\tHow to remove special characers from a column of dataframe using module re? [Internet]. Stack Overflow. [cited 2021 Dec 16]. Available from: https://stackoverflow.com/questions/33257344/how-to-remove-special-characers-from-a-column-of-dataframe-using-module-re\n",
    " \t \n",
    "[29]\tPandas: To_numeric for multiple columns [Internet]. Stack Overflow. [cited 2021 Dec 16]. Available from: https://stackoverflow.com/questions/36814100/pandas-to-numeric-for-multiple-columns\n",
    " \t \n",
    "[30]\tPandas: change data type of Series to String [Internet]. Stack Overflow. [cited 2021 Dec 16]. Available from: https://stackoverflow.com/questions/22231592/pandas-change-data-type-of-series-to-string\n",
    " \t \n",
    "[31]\tRegex symbol list and regex examples [Internet]. Codexpedia.com. [cited 2021 Dec 16]. Available from: https://www.codexpedia.com/regex/regex-symbol-list-and-regex-examples/\n",
    " \t \n",
    "[32]\tJupyterLab Notebook cells going missing [Internet]. Stack Overflow. [cited 2021 Dec 16]. Available from: https://stackoverflow.com/questions/68994024/jupyterlab-notebook-cells-going-missing\n",
    " \t \n",
    "[33]\tHow to select rows in Pandas dataframe based on string matching in multiple columns [Internet]. Stack Overflow. [cited 2021 Dec 16]. Available from: https://stackoverflow.com/questions/58559581/how-to-select-rows-in-pandas-dataframe-based-on-string-matching-in-multiple-colu\n",
    " \t \n"
   ]
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   "source": [
    "### Images\n",
    "\n",
    "- [README Header](https://agile.com.np/wp-content/uploads/2020/12/data-analytics-image-big-data.jpg)\n",
    "- [CAO Logo](https://www.icareer.ie/wp-content/uploads/2016/12/Untitled-1.jpg)\n",
    "- [Points](https://www.irishtimes.com/polopoly_fs/1.3596890!/image/image.jpg)\n",
    "- [Concat](https://pandas.pydata.org/pandas-docs/stable/_images/merging_concat_basic.png)\n",
    "- [Join](https://www.datasciencemadesimple.com/wp-content/uploads/2017/09/join-or-merge-in-python-pandas-1.png?ezimgfmt=ng:webp/ngcb1)\n",
    "- [Covid](https://www.bswrehab.com/-/media/project/baylorrehab/dotcom/usa/images/about-us/in-the-news/covid19visitationpolicy.jpg?h=256&iar=0&w=256&t=20200926000839&hash=B45A283413C03E8AFECD850719453F0F)"
   ]
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   "cell_type": "markdown",
   "id": "eef4ba22-2751-46b1-895c-51a803c3d05c",
   "metadata": {},
   "source": [
    "***"
   ]
  },
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   "id": "04e20723-e2aa-4bdb-b20f-35438621d47b",
   "metadata": {
    "tags": []
   },
   "source": [
    "# End"
   ]
  }
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