analysis.ipynb

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   "cell_type": "code",
   "execution_count": 138,
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   "outputs": [],
   "source": [
    "import re\n",
    "import operator\n",
    "import itertools\n",
    "from functools import reduce, partial\n",
    "\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import scipy\n",
    "import pandas as pd\n",
    "import plotly\n",
    "import plotly.graph_objs as go\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.express as px\n",
    "import plotly.io as pio\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "# Config\n",
    "pio.templates.default = \"plotly_dark\"\n",
    "\n",
    "pd.set_option(\"display.max_rows\", 500)\n",
    "pd.set_option(\"display.max_columns\", 50)\n",
    "pd.set_option(\"plotting.backend\", \"plotly\")\n",
    "\n",
    "# TODO: Backtesting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"../data.csv\")\n",
    "data_male = data.groupby(\"College branch\").get_group(\"Males\")\n",
    "data_female = data.groupby(\"College branch\").get_group(\"Females\")\n",
    "\n",
    "data_filtered = data.drop(columns=data.columns[3:5])\n",
    "\n",
    "courses_raw = [\"ENGLISH100\", \"ARB100\", \"MATH101\", \"CHEM101\", \"STAT101\", \"TECH101\", \"ENTREPRENEUR101\", \"FAJAB101\", \"NAHAJ101\", \"ENGLISH110\",\n",
    "            \"SALAM107\", \"PHYS104\", \"MATH106\", \"CSC111\", \"MATH151\", \"SALAM108\", \"CSC113\", \"CSC220\", \"MATH244\",\n",
    "            \"CSC212\", \"CSC215\", \"MATH281\", \"CSC304\", \"CSC380\", \"CSC227\", \"CSC311\", \"CSC339\", \"CSC343\", \"CSC361\",\n",
    "            \"CSC329\", \"CSC340\", \"CSC453\", \"CSC496\", \"PHYS210\", \"PHYS103\", \"CHEM103\", \"CSC443\", \"CSC462\", \"CSC476\", \"CSC478\", \"CSC484\", \"CSC489\"]\n",
    "criteria = [\"Application\", \"Relevance\", \"Insight\", \"Understanding\", \"Ease\"]\n",
    "ratings = list(range(1, 6))\n",
    "\n",
    "filtered_comments = [\"I do not take this course. \"]\n",
    "filtered_courses = [\"PHYS210\", \"CHEM103\", \"CSC484\", \"CSC476\", \"CSC478\"]\n",
    "bayes_weight = 2\n",
    "name_to_figure = {}\n",
    "\n",
    "ccis_df = pd.read_html(\"https://ccis.ksu.edu.sa/en/cs/Course-Catalog\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Utility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "@np.vectorize\n",
    "def course_to_category(course):\n",
    "    prefix_to_category = {\"ENGLISH\":\"Humanities\", \"NAHAJ\": \"Humanities\", \"ARB\": \"Humanities\", \"ENTREPRENEUR\": \"Humanities\", \"FAJAB\": \"Humanities\", \"TECH\": \"Humanities\",\n",
    "                    \"PHYS\": \"Physics\", \"CHEM\": \"Chemistry\", \"SALAM\": \"Islamics\", \"MATH\": \"Mathematics\", \"STAT\": \"Mathematics\", \"CSC\": \"Computer science\"}\n",
    "    return prefix_to_category[re.findall(\"[a-zA-Z]+\", course)[0]]\n",
    "\n",
    "def df_by_regex(df, regex):\n",
    "    return df.filter(regex=regex, axis=1)\n",
    "\n",
    "def get_subject_df(df, subject):\n",
    "    return df_by_regex(df, f\"{subject}.*\\[.*\\].*\")\n",
    "\n",
    "def remove_double_naming_in_legend(fig):\n",
    "    for trace in fig.select_traces():\n",
    "        trace.name = trace.name.split(\",\")[0]\n",
    "\n",
    "def get_course_to_num_ranking_series(df):\n",
    "    # a bit of a primitive way to count every filled in ranking (some rankings are NaN). Maybe it could be improved?\n",
    "    i = 0\n",
    "    course_to_num_ranking = {}\n",
    "    while i < len(df):\n",
    "        course_to_num_ranking.update({df.iloc[i].name[0]:len(df.iloc[i].dropna())})\n",
    "        i += 5\n",
    "    return pd.Series(course_to_num_ranking)\n",
    "\n",
    "@np.vectorize\n",
    "def build_df(cc):\n",
    "    name = cc.iloc[0][0].replace(\" \", \"\").split(\"–\")[0].split(\"-\")[0]\n",
    "    lv1_indices = cc[0].copy()\n",
    "    lv1_indices[0] = \"Title\"\n",
    "    return pd.DataFrame(cc[1].values, index=pd.MultiIndex.from_tuples(zip(itertools.repeat(name), lv1_indices)))\n",
    "\n",
    "def get_scores_df(df, criteria, categories):\n",
    "    return pd.DataFrame(((bayesian_mean(df.loc[[(subject, criterion) for subject in courses]].T, categories)) for criterion in criteria), index=criteria)\n",
    "\n",
    "def bayesian_mean(course_to_criterion, categories):\n",
    "    course_to_criterion_mean = course_to_criterion.mean().droplevel(1)\n",
    "\n",
    "    temp = course_to_criterion_mean.reset_index()\n",
    "    temp[\"index\"] = categories\n",
    "    category_to_criterion_mean = temp.groupby(\"index\").mean()[0]\n",
    "\n",
    "    return course_to_criterion.apply(lambda x: scores_to_bayesian_mean(x, category_to_criterion_mean)).droplevel(1)\n",
    "\n",
    "def scores_to_bayesian_mean(scores, category_to_criterion_mean, category_weight_portion=bayes_weight):\n",
    "    \"\"\"\n",
    "    scores: Series of scores with Name formatted like this (Course, _)\n",
    "    category_to_criterion_mean: category_to_criterion_mean\n",
    "    category_weight_portion: the portion of scores category average is weighted at. e.g. 1 means its just as weighty as scores, 2 means half as weighty\n",
    "    returns bayesian estimation of mean of scores, given its category\n",
    "    \"\"\"\n",
    "    scores = scores.dropna()\n",
    "    category_mean_series = pd.Series({\"\": category_to_criterion_mean[course_to_category(scores.name[0]).item(0)]})\n",
    "    weights = [max(len(scores) // category_weight_portion, 1)] + [1] * len(scores)\n",
    "    normalized_weights = weights/np.linalg.norm(weights)\n",
    "    return np.average(scores.append(category_mean_series), weights=normalized_weights)\n",
    "\n",
    "def get_course_info_df(ccis_df, courses):\n",
    "    course_info = pd.concat(build_df(ccis_df[:-14]))\n",
    "    course_info.columns = [\"Values\"]\n",
    "    course_info.loc[((\"ENGLISH100\", \"Title\"), \"Values\")] = \"English 100\"\n",
    "    course_info.loc[((\"ARB100\", \"Title\"), \"Values\")] = \"Arabic 100\"\n",
    "    course_info.loc[((\"MATH101\", \"Title\"), \"Values\")] = \"Math 101 - Derivative Math\"\n",
    "    course_info.loc[((\"CHEM101\", \"Title\"), \"Values\")] = \"Chemistry 101 - Intro To Chemistry\"\n",
    "    course_info.loc[((\"STAT101\", \"Title\"), \"Values\")] = \"Statistics 101\"\n",
    "    course_info.loc[((\"TECH101\", \"Title\"), \"Values\")] = \"Tech 140 - Computer Skills\"\n",
    "    course_info.loc[((\"ENTREPRENEUR101\", \"Title\"), \"Values\")] = \"Entrepreneurship 101\"\n",
    "    course_info.loc[((\"FAJAB101\", \"Title\"), \"Values\")] = \"Fajab 101 - Health and Stuff\"\n",
    "    course_info.loc[((\"NAHAJ101\", \"Title\"), \"Values\")] = \"Nahaj 101 - University Skills\"\n",
    "    course_info.loc[((\"ENGLISH110\", \"Title\"), \"Values\")] = \"English 110\"\n",
    "    course_info.loc[((\"SALAM107\", \"Title\"), \"Values\")] = \"Salam 107 - Job Ethics\"\n",
    "    course_info.loc[((\"PHYS104\", \"Title\"), \"Values\")] = \"Physics 104 - Electrical Physics\"\n",
    "    course_info.loc[((\"MATH106\", \"Title\"), \"Values\")] = \"Math 106 - Integral Math\"\n",
    "    course_info.loc[((\"MATH151\", \"Title\"), \"Values\")] = \"Math 151 - Discrete Math\"\n",
    "    course_info.loc[((\"SALAM108\", \"Title\"), \"Values\")] = \"Salam 108 - Modern Issues\"\n",
    "    course_info.loc[((\"MATH244\", \"Title\"), \"Values\")] = \"MATH 244 - Linear Algebra\"\n",
    "    course_info.loc[((\"MATH281\", \"Title\"), \"Values\")] = \"Math 281 - Combinatorial Math\"\n",
    "    course_info.loc[((\"CSC343\", \"Title\"), \"Values\")] = \"CSC 343 - Intro To Software Engineering\"\n",
    "    course_info.loc[((\"PHYS103\", \"Title\"), \"Values\")] = \"Physics 103 - Newtonian Physics\"\n",
    "    course_info.loc[((\"CSC489\", \"Title\"), \"Values\")] = \"CSC 489 - Selected Topics in Computer Science\"\n",
    "\n",
    "    course_to_info = {}\n",
    "    for course in courses:\n",
    "        course_to_info[course] = course_info.loc[course].values[0][0]\n",
    "    return pd.DataFrame(course_to_info.values(), course_to_info.keys(), columns=[\"Description\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Softwarez\\Anaconda\\lib\\site-packages\\pandas\\core\\generic.py:4153: PerformanceWarning:\n",
      "\n",
      "dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n",
      "\n",
      "D:\\Softwarez\\Anaconda\\lib\\site-packages\\numpy\\core\\_asarray.py:102: VisibleDeprecationWarning:\n",
      "\n",
      "Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Dataframes\n",
    "index = list((course, criterion) for criterion in criteria for course in courses_raw)\n",
    "sorted_index = sorted(index, key=lambda x: courses_raw.index(x[0]))\n",
    "df = get_subject_df(data_filtered, \"\").T.set_index(pd.Index(sorted_index)).drop(filtered_courses)\n",
    "\n",
    "# Useful structures\n",
    "courses = courses_raw.copy()\n",
    "[courses.remove(course) for course in filtered_courses]\n",
    "categories = pd.Series(course_to_category(courses))\n",
    "assert sum(categories.isna()) == 0, \"prefix_to_category must cover each course prefix\"\n",
    "\n",
    "# Comments\n",
    "course_to_comments = {}\n",
    "other_details_df = df_by_regex(data_filtered, \"Other details you want to add.*\")\n",
    "for course, other_details_col in zip(courses, other_details_df):\n",
    "    filt = (other_details_df[other_details_col].isna()) | reduce(operator.or_, (comment == other_details_df[other_details_col] for comment in filtered_comments))\n",
    "    comments = other_details_df[other_details_col][~filt]\n",
    "    if comments.all():\n",
    "        course_to_comments[course] = comments\n",
    "\n",
    "# Course info\n",
    "course_info_df = get_course_info_df(ccis_df, courses)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ranking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Course ranking by criteria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
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   "source": [
    "functions = [\n",
    "            lambda x: bayesian_mean(x, categories), \n",
    "            lambda x: pd.Series(np.nanmedian(x, axis=0), index=courses), \n",
    "            lambda x: pd.Series(stats.mode(x, nan_policy=\"omit\")[0].data[0], index=courses),\n",
    "            lambda x: x.std().droplevel(1),\n",
    "]\n",
    "function_to_name = {function: function_name for function, function_name in zip(functions, [\"Bayesian estimated mean\", \"Median\", \"Mode\", \"Standard deviation\"])}\n",
    "rows = len(functions) * 3\n",
    "cols = 2\n",
    "subtitles = np.empty((rows, cols), dtype=\"object\")\n",
    "for f_i, function in enumerate(functions):\n",
    "    for c_i, criterion in enumerate(criteria):\n",
    "        row = f_i * 3 + int(c_i / 2)\n",
    "        col = c_i % 2\n",
    "        subtitles[row, col] = (f\"{function_to_name.get(function)} {criterion} score for each course\")\n",
    "\n",
    "\n",
    "fig_ranking = make_subplots(rows=len(functions) * 3, cols=2, \n",
    "        subplot_titles=subtitles.reshape(-1))\n",
    "        \n",
    "for f_i, function in enumerate(functions):\n",
    "    for c_i, criterion in enumerate(criteria):\n",
    "        row = f_i * 3 + int(c_i / 2 + 1)\n",
    "        col = c_i % 2 + 1\n",
    "        course_to_criterion = function(df.loc[[(subject, criterion) for subject in courses]].T)\n",
    "\n",
    "        scatter = px.scatter(course_to_criterion, color=categories, symbol=categories, hover_name=course_info_df.Description)\n",
    "        for trace in scatter.select_traces():\n",
    "            # Only show legend once\n",
    "            if f_i == c_i == 0:\n",
    "                trace.showlegend = True\n",
    "            else:\n",
    "                trace.showlegend = False\n",
    "\n",
    "            fig_ranking.add_trace(\n",
    "                trace,\n",
    "                row=row, col=col\n",
    "            )\n",
    "        fig_ranking.update_yaxes(\n",
    "            title=f\"{function_to_name.get(function)} {criterion}\",\n",
    "            row=row, col=col\n",
    "        )\n",
    "    remove_double_naming_in_legend(fig_ranking)\n",
    "    fig_ranking.update_layout(height=5000, width=1920)\n",
    "\n",
    "name = \"ranking_of_criteria\"\n",
    "fig_ranking.write_image(f\"images/{name}.png\")\n",
    "name_to_figure.update({name: fig_ranking})\n",
    "fig_ranking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scores "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores_df = get_scores_df(df, criteria, categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 0\n",
    "course_to_num_ranking = {}\n",
    "while i < len(df):\n",
    "    course_to_num_ranking.update({df.iloc[i].name[0]:len(df.iloc[i].dropna())})\n",
    "    i += 5\n",
    "course_to_num_ranking_series = pd.Series(course_to_num_ranking)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
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    "general_scores = scores_df.sum().sort_values() / 25\n",
    "sorted_categories = course_to_category(general_scores.index)\n",
    "fig_scores = px.scatter(general_scores, color=sorted_categories, symbol=sorted_categories, title=\"Course ranking (General)\", \n",
    "                        hover_name=get_course_info_df(ccis_df, general_scores.index)[\"Description\"].values)\n",
    "fig_scores.update_xaxes(title=\"Course\")\n",
    "fig_scores.update_yaxes(title=\"Ranking\")\n",
    "\n",
    "remove_double_naming_in_legend(fig_scores)\n",
    "\n",
    "fig_scores.add_trace(\n",
    "    go.Bar(x=course_to_num_ranking_series.index, y=course_to_num_ranking_series / np.linalg.norm(course_to_num_ranking_series), marker={\"color\": \"rgba(100, 100, 100, 0.3)\"}, name=\"Percentage of data points\"),\n",
    "    # row=2, col=1\n",
    ")\n",
    "fig_scores.update_layout(height=980, width=1920)\n",
    "\n",
    "name = \"Course ranking (General)\"\n",
    "fig_scores.write_image(f\"images/{name}.png\")\n",
    "name_to_figure.update({name: fig_scores})\n",
    "fig_scores"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weighted scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights_real_world = {\"Application\": 1.5, \"Relevance\": 1, \"Understanding\": 0.2, \"Ease\": 0.5}\n",
    "weights_academic = {\"Insight\": 1.5, \"Understanding\": 1.5}\n",
    "\n",
    "scores_df_real_world = scores_df.apply(lambda x: weights_real_world.get(x.name, 0) * x, axis=1)\n",
    "scores_df_academic = scores_df.apply(lambda x: weights_academic.get(x.name, 0) * x, axis=1)\n",
    "\n",
    "standardized_scores_df_real_world = scores_df_real_world.sum() / max(scores_df_real_world.sum())\n",
    "standardized_scores_df_academic = scores_df_academic.sum() / max(scores_df_academic.sum())"
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  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
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   "source": [
    "weighted_scores = pd.DataFrame([standardized_scores_df_real_world, standardized_scores_df_academic]).T\n",
    "weighted_scores.columns = (\"Real-world score\", \"Academic score\")"
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   "source": [
    "sorted_categories = course_to_category(weighted_scores.index)\n",
    "fig_weighted_scores = px.scatter(weighted_scores, x=\"Real-world score\", y=\"Academic score\", color=sorted_categories, symbol=sorted_categories, title=\"Course ranking (Real-world Vs. Academic)\",\n",
    "                hover_name=course_info_df.Description, text=weighted_scores.index)\n",
    "\n",
    "remove_double_naming_in_legend(fig_weighted_scores)\n",
    "\n",
    "fig_weighted_scores.update_traces(textposition=\"top center\")\n",
    "fig_weighted_scores.update_layout(height=980, width=1920)\n",
    "fig_weighted_scores.add_annotation(\n",
    "    x=.4, y=.98,\n",
    "    text=\"Academic_score = (Insight: 1.5 Understanding * 1.5) / max_score\",\n",
    "    showarrow=False\n",
    ")\n",
    "fig_weighted_scores.add_annotation(\n",
    "    x=.462, y=1,\n",
    "    text=\"Real_world_score = (Application * 1.5 + Relevance + Understanding * 0.2 + Ease * 0.5) / max_score\",\n",
    "    showarrow=False\n",
    ")\n",
    "\n",
    "name = \"Course ranking (Real-world Vs. Academic)\"\n",
    "fig_weighted_scores.write_image(f\"images/{name}.png\")\n",
    "name_to_figure.update({name: fig_weighted_scores})\n",
    "fig_weighted_scores"
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    "scores_df_matrix[\"categories\"] = categories.values\n",
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    "all_scores = weighted_scores.copy()\n",
    "all_scores[\"General score\"] = general_scores\n",
    "all_scores[\"categories\"] = categories.values\n",
    "fig_scatter_3d = px.scatter_3d(all_scores, x=\"General score\", y=\"Real-world score\", z=\"Academic score\", symbol=\"categories\", color=\"categories\", hover_name=course_info_df.Description, title=\"3D-Scatter plot of all scores\")\n",
    "fig_scatter_3d.update_layout(height=980, width=1920)\n",
    "name = \"3d_scatter_scores\"\n",
    "fig_scatter_3d.write_image(f\"images/{name}.png\")\n",
    "name_to_figure.update({name: fig_scatter_3d})\n",
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        },
        "width": 1920,
        "xaxis": {
         "anchor": "y",
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         "scaleanchor": "y"
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         "anchor": "x",
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     },
     "metadata": {},
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   ],
   "source": [
    "fig_corr = px.imshow(all_scores.corr(), title=\"Correlation of score types\")\n",
    "fig_corr.update_layout(height=980, width=1920)\n",
    "name = \"Correlation of score types\"\n",
    "fig_corr.write_image(f\"images/{name}.png\")\n",
    "name_to_figure.update({name: fig_corr})\n",
    "fig_corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate site"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"index_empty.html.test\") as file:\n",
    "    html = file.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"index.html\", \"w\") as file:\n",
    "    file.write(html)\n",
    "    for figure in name_to_figure.values():\n",
    "        file.write(figure.to_html(full_html=False, include_plotlyjs=False))\n",
    "    file.write(\"</div></body></html>\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Meta stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    37.000000\n",
       "mean     10.000000\n",
       "std       3.109126\n",
       "min       2.000000\n",
       "25%       8.000000\n",
       "50%      11.000000\n",
       "75%      13.000000\n",
       "max      14.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_course_to_num_ranking_series(df).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrl}\n",
      "\\toprule\n",
      "{} &  Real-world score &  Academic score &  General score &        categories \\\\\n",
      "\\midrule\n",
      "ENGLISH100      &          0.977969 &        0.587055 &       0.749899 &        Humanities \\\\\n",
      "ARB100          &          0.644273 &        0.561107 &       0.559423 &        Humanities \\\\\n",
      "MATH101         &          0.899391 &        0.883593 &       0.772007 &       Mathematics \\\\\n",
      "CHEM101         &          0.520728 &        0.438133 &       0.445344 &         Chemistry \\\\\n",
      "STAT101         &          0.927789 &        0.804526 &       0.756429 &       Mathematics \\\\\n",
      "TECH101         &          0.765262 &        0.545605 &       0.626730 &        Humanities \\\\\n",
      "ENTREPRENEUR101 &          0.601895 &        0.521720 &       0.530546 &        Humanities \\\\\n",
      "FAJAB101        &          0.600165 &        0.538924 &       0.546742 &        Humanities \\\\\n",
      "NAHAJ101        &          0.638404 &        0.661528 &       0.594242 &        Humanities \\\\\n",
      "ENGLISH110      &          0.918583 &        0.582118 &       0.713858 &        Humanities \\\\\n",
      "SALAM107        &          0.754027 &        0.638643 &       0.659744 &          Islamics \\\\\n",
      "PHYS104         &          0.624921 &        0.534890 &       0.519392 &           Physics \\\\\n",
      "MATH106         &          0.794863 &        0.735696 &       0.674323 &       Mathematics \\\\\n",
      "CSC111          &          0.984843 &        0.934987 &       0.849246 &  Computer science \\\\\n",
      "MATH151         &          0.954951 &        0.868024 &       0.817721 &       Mathematics \\\\\n",
      "SALAM108        &          0.634832 &        0.415596 &       0.526326 &          Islamics \\\\\n",
      "CSC113          &          0.840633 &        0.775292 &       0.730219 &  Computer science \\\\\n",
      "CSC220          &          0.862635 &        0.832111 &       0.739635 &  Computer science \\\\\n",
      "MATH244         &          0.831908 &        0.716022 &       0.674008 &       Mathematics \\\\\n",
      "CSC212          &          0.943643 &        1.000000 &       0.839300 &  Computer science \\\\\n",
      "CSC215          &          0.801212 &        0.781024 &       0.676474 &  Computer science \\\\\n",
      "MATH281         &          0.848302 &        0.846393 &       0.748259 &       Mathematics \\\\\n",
      "CSC304          &          0.676360 &        0.582436 &       0.612944 &  Computer science \\\\\n",
      "CSC380          &          0.795881 &        0.727745 &       0.676944 &  Computer science \\\\\n",
      "CSC227          &          0.740514 &        0.637227 &       0.619513 &  Computer science \\\\\n",
      "CSC311          &          0.993665 &        0.991418 &       0.866180 &  Computer science \\\\\n",
      "CSC339          &          0.671742 &        0.846109 &       0.679513 &  Computer science \\\\\n",
      "CSC343          &          0.659292 &        0.501000 &       0.529513 &  Computer science \\\\\n",
      "CSC361          &          0.801822 &        0.819164 &       0.703416 &  Computer science \\\\\n",
      "CSC329          &          0.971137 &        0.964173 &       0.849513 &  Computer science \\\\\n",
      "CSC340          &          0.609492 &        0.737127 &       0.586180 &  Computer science \\\\\n",
      "CSC453          &          0.658107 &        0.646309 &       0.582847 &  Computer science \\\\\n",
      "CSC496          &          0.898492 &        0.796582 &       0.736309 &  Computer science \\\\\n",
      "PHYS103         &          0.611916 &        0.635857 &       0.542905 &           Physics \\\\\n",
      "CSC443          &          0.683071 &        0.549409 &       0.578540 &  Computer science \\\\\n",
      "CSC462          &          1.000000 &        0.922291 &       0.843796 &  Computer science \\\\\n",
      "CSC489          &          0.792190 &        0.732545 &       0.691387 &  Computer science \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(all_scores.to_latex())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|                 |   Real-world score |   Academic score |   General score | categories       |\n",
      "|:----------------|-------------------:|-----------------:|----------------:|:-----------------|\n",
      "| ENGLISH100      |           0.977969 |         0.587055 |        0.749899 | Humanities       |\n",
      "| ARB100          |           0.644273 |         0.561107 |        0.559423 | Humanities       |\n",
      "| MATH101         |           0.899391 |         0.883593 |        0.772007 | Mathematics      |\n",
      "| CHEM101         |           0.520728 |         0.438133 |        0.445344 | Chemistry        |\n",
      "| STAT101         |           0.927789 |         0.804526 |        0.756429 | Mathematics      |\n",
      "| TECH101         |           0.765262 |         0.545605 |        0.62673  | Humanities       |\n",
      "| ENTREPRENEUR101 |           0.601895 |         0.52172  |        0.530546 | Humanities       |\n",
      "| FAJAB101        |           0.600165 |         0.538924 |        0.546742 | Humanities       |\n",
      "| NAHAJ101        |           0.638404 |         0.661528 |        0.594242 | Humanities       |\n",
      "| ENGLISH110      |           0.918583 |         0.582118 |        0.713858 | Humanities       |\n",
      "| SALAM107        |           0.754027 |         0.638643 |        0.659744 | Islamics         |\n",
      "| PHYS104         |           0.624921 |         0.53489  |        0.519392 | Physics          |\n",
      "| MATH106         |           0.794863 |         0.735696 |        0.674323 | Mathematics      |\n",
      "| CSC111          |           0.984843 |         0.934987 |        0.849246 | Computer science |\n",
      "| MATH151         |           0.954951 |         0.868024 |        0.817721 | Mathematics      |\n",
      "| SALAM108        |           0.634832 |         0.415596 |        0.526326 | Islamics         |\n",
      "| CSC113          |           0.840633 |         0.775292 |        0.730219 | Computer science |\n",
      "| CSC220          |           0.862635 |         0.832111 |        0.739635 | Computer science |\n",
      "| MATH244         |           0.831908 |         0.716022 |        0.674008 | Mathematics      |\n",
      "| CSC212          |           0.943643 |         1        |        0.8393   | Computer science |\n",
      "| CSC215          |           0.801212 |         0.781024 |        0.676474 | Computer science |\n",
      "| MATH281         |           0.848302 |         0.846393 |        0.748259 | Mathematics      |\n",
      "| CSC304          |           0.67636  |         0.582436 |        0.612944 | Computer science |\n",
      "| CSC380          |           0.795881 |         0.727745 |        0.676944 | Computer science |\n",
      "| CSC227          |           0.740514 |         0.637227 |        0.619513 | Computer science |\n",
      "| CSC311          |           0.993665 |         0.991418 |        0.86618  | Computer science |\n",
      "| CSC339          |           0.671742 |         0.846109 |        0.679513 | Computer science |\n",
      "| CSC343          |           0.659292 |         0.501    |        0.529513 | Computer science |\n",
      "| CSC361          |           0.801822 |         0.819164 |        0.703416 | Computer science |\n",
      "| CSC329          |           0.971137 |         0.964173 |        0.849513 | Computer science |\n",
      "| CSC340          |           0.609492 |         0.737127 |        0.58618  | Computer science |\n",
      "| CSC453          |           0.658107 |         0.646309 |        0.582847 | Computer science |\n",
      "| CSC496          |           0.898492 |         0.796582 |        0.736309 | Computer science |\n",
      "| PHYS103         |           0.611916 |         0.635857 |        0.542905 | Physics          |\n",
      "| CSC443          |           0.683071 |         0.549409 |        0.57854  | Computer science |\n",
      "| CSC462          |           1        |         0.922291 |        0.843796 | Computer science |\n",
      "| CSC489          |           0.79219  |         0.732545 |        0.691387 | Computer science |\n"
     ]
    }
   ],
   "source": [
    "print(all_scores.to_markdown())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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