diff --git "a/make_data.ipynb" "b/make_data.ipynb"
new file mode 100644--- /dev/null
+++ "b/make_data.ipynb"
@@ -0,0 +1,1530 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import random\n",
+    "\n",
+    "# Sample data\n",
+    "data = {\n",
+    "    \"Question No\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],\n",
+    "    \"Question Full Marks\": [5, 4, 3, 6, 5, 4, 3, 6, 5, 4, 3, 6, 5, 4, 3],\n",
+    "    \"Learning Indicator ID\": [\"LI001\", \"LI002\", \"LI003\", \"LI004\", \"LI005\", \"LI006\", \"LI007\", \"LI008\", \"LI001\", \"LI002\", \"LI003\", \"LI004\", \"LI005\", \"LI006\", \"LI007\"],\n",
+    "    \"Learning Indicator Text\": [\n",
+    "        \"Understand basic algebraic concepts\", \"Apply geometric principles\", \"Solve linear equations\", \"Interpret statistical data\",\n",
+    "        \"Analyze historical events\", \"Understand economic theories\", \"Apply scientific method\", \"Comprehend literary texts\",\n",
+    "        \"Understand basic algebraic concepts\", \"Apply geometric principles\", \"Solve linear equations\", \"Interpret statistical data\",\n",
+    "        \"Analyze historical events\", \"Understand economic theories\", \"Apply scientific method\"\n",
+    "    ]\n",
+    "}\n",
+    "\n",
+    "# Create question DataFrame\n",
+    "question_df = pd.DataFrame(data)\n",
+    "\n",
+    "def generate_student_data(question_df, no_of_students):\n",
+    "    student_data = []\n",
+    "    for student_id in range(1, no_of_students + 1):\n",
+    "        for _, row in question_df.iterrows():\n",
+    "            question_no = row[\"Question No\"]\n",
+    "            max_marks = row[\"Question Full Marks\"]\n",
+    "            marks_obtained = random.randint(0, max_marks)\n",
+    "            student_data.append([question_no, student_id, marks_obtained, max_marks])\n",
+    "    \n",
+    "    student_df = pd.DataFrame(student_data, columns=[\"Question No\", \"Student ID\", \"Marks Obtained\", \"Max Marks\"])\n",
+    "    return student_df\n",
+    "\n",
+    "# Example usage\n",
+    "no_of_students = 35\n",
+    "student_df = generate_student_data(question_df, no_of_students)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "student_df.to_csv(\"students_df.csv\",index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "question_df.columns = [\"question_number\",\"full_marks\",\"learning_indicator_id\",\"learning_indicator_text\"]\n",
+    "student_df.columns = [\"question_number\",\"student_id\",\"marks_obtained\",\"maximum_marks\"]\n",
+    "student_df.to_csv(\"students_df.csv\",index=False)\n",
+    "question_df.to_csv(\"question_paper.csv\",index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def plot_violin_by_li(question_data, student_performance_data):\n",
+    "    # Merge the dataframes on question number\n",
+    "    merged_data = pd.merge(student_performance_data, question_data, on='question_number')\n",
+    "    \n",
+    "    merged_data['normalized_marks'] = merged_data['marks_obtained'] / merged_data['maximum_marks']\n",
+    "    \n",
+    "    # Create the violin plot\n",
+    "    plt.figure(figsize=(12, 6))\n",
+    "    sns.violinplot(x='learning_indicator_id', y='normalized_marks', data=merged_data)\n",
+    "    plt.xlabel('Learning Indicator ID')\n",
+    "    plt.ylabel('Normalized Marks Obtained')\n",
+    "    plt.title('Violin Plot of Normalized Marks Obtained by Learning Indicator ID')\n",
+    "    plt.xticks(rotation=45)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "\n",
+    "plot_violin_by_li(question_df, student_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def categorize_marks(normalized_marks):\n",
+    "    if normalized_marks >= 0.8:\n",
+    "        return '80-100%'\n",
+    "    elif normalized_marks >= 0.6:\n",
+    "        return '60-80%'\n",
+    "    else:\n",
+    "        return '<60%'\n",
+    "\n",
+    "def analyze_student_performance_by_li(question_data, student_performance_data):\n",
+    "    # Merge the dataframes on question number\n",
+    "    merged_data = pd.merge(student_performance_data, question_data, on='question_number')\n",
+    "    \n",
+    "    merged_data = merged_data.groupby([\"student_id\",\"learning_indicator_id\"])[[\"marks_obtained\",\"maximum_marks\"]].sum().reset_index()\n",
+    "    merged_data['normalized_marks'] = merged_data['marks_obtained'] / merged_data['maximum_marks']\n",
+    "    # print(merged_data)\n",
+    "    # Categorize the normalized marks\n",
+    "    merged_data['category'] = merged_data['normalized_marks'].apply(categorize_marks)\n",
+    "    \n",
+    "    # Group by learning indicator ID and category, and count the number of students in each category\n",
+    "    merged_data = merged_data.groupby(['learning_indicator_id', 'category']).size().unstack(fill_value=0)\n",
+    "    \n",
+    "    # Rename the columns for better readability\n",
+    "    merged_data.columns = ['<60%', '60-80%', '80-100%']\n",
+    "    merged_data = merged_data.sort_values(['<60%', '60-80%', '80-100%'],ascending=[False,False,False]).reset_index()\n",
+    "    \n",
+    "    # Display the results\n",
+    "    return merged_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "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>learning_indicator_id</th>\n",
+       "      <th>&lt;60%</th>\n",
+       "      <th>60-80%</th>\n",
+       "      <th>80-100%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>LI002</td>\n",
+       "      <td>12</td>\n",
+       "      <td>4</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>LI001</td>\n",
+       "      <td>11</td>\n",
+       "      <td>6</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>LI004</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>LI006</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>LI005</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>LI008</td>\n",
+       "      <td>5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>LI007</td>\n",
+       "      <td>4</td>\n",
+       "      <td>12</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>LI003</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  learning_indicator_id  <60%  60-80%  80-100%\n",
+       "0                 LI002    12       4       19\n",
+       "1                 LI001    11       6       18\n",
+       "2                 LI004    10       3       22\n",
+       "3                 LI006    10       2       23\n",
+       "4                 LI005     9       2       24\n",
+       "5                 LI008     5      14       16\n",
+       "6                 LI007     4      12       19\n",
+       "7                 LI003     4       6       25"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "category_counts=analyze_student_performance_by_li(question_df,student_df)\n",
+    "category_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "28.0"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "35*0.8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  learning_indicator_id  <60%  60-80%  80-100%  Rank Priority\n",
+      "0                 LI002    12       4       19     1     High\n",
+      "1                 LI001    11       6       18     2     High\n",
+      "2                 LI004    10       3       22     3   Medium\n",
+      "3                 LI006    10       2       23     4   Medium\n",
+      "4                 LI005     9       2       24     5      Low\n",
+      "5                 LI008     5      14       16     6      Low\n",
+      "6                 LI007     4      12       19     7      Low\n",
+      "7                 LI003     4       6       25     8      Low\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "def prioritize_lis(category_counts):\n",
+    "    # Add a rank column based on the order of rows\n",
+    "    category_counts['Rank'] = category_counts.index + 1\n",
+    "    \n",
+    "    # Determine the number of LIs\n",
+    "    total_lis = len(category_counts)\n",
+    "    \n",
+    "    # Determine the cutoff points for high, medium, and low priority\n",
+    "    high_priority_cutoff = int(total_lis * 0.3)\n",
+    "    medium_priority_cutoff = int(total_lis * 0.6)\n",
+    "    \n",
+    "    # Classify the LIs based on their rank\n",
+    "    category_counts['Priority'] = 'Low'\n",
+    "    category_counts.loc[category_counts['Rank'] <= high_priority_cutoff, 'Priority'] = 'High'\n",
+    "    category_counts.loc[(category_counts['Rank'] > high_priority_cutoff) & (category_counts['Rank'] <= medium_priority_cutoff), 'Priority'] = 'Medium'\n",
+    "    \n",
+    "    return category_counts\n",
+    "\n",
+    "prioritized_lis = prioritize_lis(category_counts)\n",
+    "print(prioritized_lis)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def student_level_analysis(student_data, question_data, prioritized_lis):\n",
+    "    # Merge the student data with question data\n",
+    "    merged_data = pd.merge(student_data, question_data, on='question_number')\n",
+    "    \n",
+    "    # Normalize the marks obtained for each learning indicator by each student\n",
+    "    merged_data = merged_data.groupby(['student_id', 'learning_indicator_id'])[['marks_obtained', 'maximum_marks']].sum().reset_index()\n",
+    "    merged_data['normalized_marks'] = merged_data['marks_obtained'] / merged_data['maximum_marks']\n",
+    "    \n",
+    "    # Merge with prioritized_lis to get the priority and rank\n",
+    "    merged_data = pd.merge(merged_data, prioritized_lis[['learning_indicator_id', 'Rank']], on='learning_indicator_id', how='left')\n",
+    "    \n",
+    "    # Rank the LIs for each student based on normalized marks and class-level LI priority\n",
+    "    merged_data['student_rank'] = merged_data.groupby('student_id')['normalized_marks'].rank(method='dense', ascending=False)\n",
+    "    merged_data = merged_data.sort_values(by=['student_id', 'student_rank', 'Rank'])\n",
+    "    \n",
+    "    # Ensure unique ranks by adding a secondary sort by Rank\n",
+    "    merged_data['unique_rank'] = merged_data.groupby('student_id').cumcount() + 1\n",
+    "    \n",
+    "    # Create the final dataframe\n",
+    "    student_ranking = merged_data.pivot(index='student_id', columns='unique_rank', values='learning_indicator_id').reset_index()\n",
+    "    student_ranking.columns = ['student_id'] + [f'P{i+1}_li' for i in range(student_ranking.shape[1] - 1)]\n",
+    "    \n",
+    "    return student_ranking"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "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>student_id</th>\n",
+       "      <th>P1_li</th>\n",
+       "      <th>P2_li</th>\n",
+       "      <th>P3_li</th>\n",
+       "      <th>P4_li</th>\n",
+       "      <th>P5_li</th>\n",
+       "      <th>P6_li</th>\n",
+       "      <th>P7_li</th>\n",
+       "      <th>P8_li</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>11</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>12</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>13</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>14</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>15</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>16</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>17</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>18</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>19</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>20</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>21</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>22</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>23</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>24</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>25</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>26</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>27</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>28</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>29</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>30</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>31</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>32</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>33</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>34</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>35</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>LI006</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    student_id  P1_li  P2_li  P3_li  P4_li  P5_li  P6_li  P7_li  P8_li\n",
+       "0            1  LI008  LI001  LI004  LI002  LI005  LI007  LI003  LI006\n",
+       "1            2  LI007  LI005  LI002  LI001  LI004  LI003  LI008  LI006\n",
+       "2            3  LI008  LI007  LI001  LI004  LI006  LI003  LI005  LI002\n",
+       "3            4  LI002  LI004  LI007  LI006  LI001  LI005  LI008  LI003\n",
+       "4            5  LI004  LI006  LI001  LI003  LI007  LI002  LI005  LI008\n",
+       "5            6  LI005  LI007  LI008  LI002  LI004  LI003  LI001  LI006\n",
+       "6            7  LI005  LI007  LI003  LI001  LI004  LI006  LI002  LI008\n",
+       "7            8  LI006  LI005  LI008  LI003  LI004  LI002  LI001  LI007\n",
+       "8            9  LI008  LI002  LI001  LI004  LI005  LI007  LI003  LI006\n",
+       "9           10  LI007  LI003  LI008  LI001  LI006  LI002  LI004  LI005\n",
+       "10          11  LI002  LI007  LI001  LI004  LI005  LI006  LI008  LI003\n",
+       "11          12  LI003  LI004  LI001  LI007  LI008  LI002  LI006  LI005\n",
+       "12          13  LI008  LI007  LI006  LI004  LI005  LI003  LI001  LI002\n",
+       "13          14  LI001  LI006  LI002  LI007  LI003  LI004  LI005  LI008\n",
+       "14          15  LI002  LI007  LI005  LI008  LI003  LI001  LI004  LI006\n",
+       "15          16  LI008  LI004  LI006  LI005  LI007  LI003  LI001  LI002\n",
+       "16          17  LI007  LI004  LI002  LI006  LI005  LI003  LI001  LI008\n",
+       "17          18  LI008  LI003  LI002  LI006  LI005  LI004  LI001  LI007\n",
+       "18          19  LI007  LI003  LI004  LI006  LI002  LI005  LI008  LI001\n",
+       "19          20  LI004  LI008  LI007  LI002  LI005  LI001  LI003  LI006\n",
+       "20          21  LI003  LI002  LI001  LI004  LI005  LI008  LI007  LI006\n",
+       "21          22  LI008  LI004  LI001  LI003  LI006  LI005  LI002  LI007\n",
+       "22          23  LI001  LI004  LI002  LI008  LI007  LI003  LI005  LI006\n",
+       "23          24  LI001  LI002  LI005  LI004  LI003  LI006  LI007  LI008\n",
+       "24          25  LI002  LI008  LI006  LI001  LI007  LI003  LI004  LI005\n",
+       "25          26  LI008  LI001  LI002  LI007  LI003  LI004  LI006  LI005\n",
+       "26          27  LI004  LI008  LI002  LI001  LI005  LI007  LI003  LI006\n",
+       "27          28  LI001  LI008  LI007  LI006  LI005  LI002  LI003  LI004\n",
+       "28          29  LI006  LI007  LI003  LI002  LI001  LI005  LI004  LI008\n",
+       "29          30  LI002  LI008  LI004  LI003  LI005  LI007  LI006  LI001\n",
+       "30          31  LI008  LI007  LI003  LI002  LI006  LI001  LI005  LI004\n",
+       "31          32  LI007  LI006  LI002  LI004  LI005  LI001  LI003  LI008\n",
+       "32          33  LI005  LI001  LI008  LI002  LI004  LI006  LI003  LI007\n",
+       "33          34  LI008  LI001  LI006  LI005  LI002  LI004  LI007  LI003\n",
+       "34          35  LI008  LI004  LI005  LI007  LI003  LI001  LI002  LI006"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "student_level_analysis(student_df,question_df,prioritized_lis)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "def prepare_data_for_ridge_plot(student_data, question_data):\n",
+    "    # Merge the DataFrames\n",
+    "    merged_data = pd.merge(student_data, question_data, on='question_number', how='inner')\n",
+    "    \n",
+    "    # Normalize the marks obtained for each learning indicator by each student\n",
+    "    normalized_data = merged_data.groupby(['student_id', 'learning_indicator_id'])[['marks_obtained', 'maximum_marks']].sum().reset_index()\n",
+    "    normalized_data['normalized_marks'] = normalized_data['marks_obtained'] / normalized_data['maximum_marks']\n",
+    "    \n",
+    "    # Add learning_indicator_text to normalized_data\n",
+    "    plot_data = pd.merge(normalized_data, question_data[['learning_indicator_id', 'learning_indicator_text']].drop_duplicates(), on='learning_indicator_id')\n",
+    "    \n",
+    "    return plot_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
+    "def prepare_data_for_ridge_plot(student_data, question_data):\n",
+    "    # Merge the DataFrames\n",
+    "    merged_data = pd.merge(student_data, question_data, on='question_number', how='inner')\n",
+    "    \n",
+    "    # Normalize the marks obtained for each learning indicator by each student\n",
+    "    normalized_data = merged_data.groupby(['student_id', 'learning_indicator_id'])[['marks_obtained', 'maximum_marks']].sum().reset_index()\n",
+    "    normalized_data['normalized_marks'] = normalized_data['marks_obtained'] / normalized_data['maximum_marks']\n",
+    "    \n",
+    "    # Add learning_indicator_text to normalized_data\n",
+    "    plot_data = pd.merge(normalized_data, question_data[['learning_indicator_id', 'learning_indicator_text']].drop_duplicates(), on='learning_indicator_id')\n",
+    "    \n",
+    "    return plot_data\n",
+    "\n",
+    "def calculate_logical_quantiles(data, num_quantiles=5):\n",
+    "    \"\"\"\n",
+    "    Calculate logical quantiles for a given data set to ensure they are informative.\n",
+    "    \n",
+    "    Parameters:\n",
+    "    data (array-like): The input data for which to calculate quantiles.\n",
+    "    num_quantiles (int): The number of quantiles to calculate. Default is 5.\n",
+    "    \n",
+    "    Returns:\n",
+    "    list: A list of quantile values.\n",
+    "    \"\"\"\n",
+    "    # Ensure there are enough unique values to calculate the quantiles\n",
+    "    if len(np.unique(data)) < num_quantiles:\n",
+    "        # If not enough unique values, use unique values as quantiles\n",
+    "        quantiles = np.unique(data)\n",
+    "    else:\n",
+    "        # Calculate evenly spaced quantiles\n",
+    "        quantiles = np.percentile(data, np.linspace(0, 100, num_quantiles))\n",
+    "    \n",
+    "    return quantiles.tolist()\n",
+    "\n",
+    "def create_ridge_plot(plot_data):\n",
+    "    unique_learning_indicators = plot_data['learning_indicator_text'].unique()\n",
+    "    n_indicators = len(unique_learning_indicators)\n",
+    "    bandwidth = 0.5  # Adjust bandwidth for smoother graphs\n",
+    "    darkgreen = '#9BC184'\n",
+    "    midgreen = '#C2D6A4'\n",
+    "    lightgreen = '#E7E5CB'\n",
+    "    colors = [lightgreen, midgreen, darkgreen, midgreen, lightgreen]\n",
+    "\n",
+    "    fig, axs = plt.subplots(nrows=n_indicators, ncols=1, figsize=(10, n_indicators * 1.5), sharex=True)\n",
+    "    axs = axs.flatten()  # Flatten in case of single plot\n",
+    "\n",
+    "    for i, indicator in enumerate(unique_learning_indicators):\n",
+    "        # Subset the data for each learning indicator\n",
+    "        subset = plot_data[plot_data['learning_indicator_text'] == indicator]\n",
+    "\n",
+    "        # Plot the distribution of normalized marks\n",
+    "        sns.kdeplot(\n",
+    "            subset['normalized_marks'],\n",
+    "            shade=True,\n",
+    "            bw_adjust=bandwidth,\n",
+    "            ax=axs[i],\n",
+    "            color=sns.color_palette('coolwarm', n_colors=n_indicators)[i]\n",
+    "        )\n",
+    "        quantiles = calculate_logical_quantiles(subset[\"normalized_marks\"].tolist())\n",
+    "\n",
+    "        # fill space between each pair of quantiles\n",
+    "        for j in range(len(quantiles) - 1):\n",
+    "            axs[i].fill_between(\n",
+    "                [quantiles[j], # lower bound\n",
+    "                quantiles[j+1]], # upper bound\n",
+    "                0.1, # max y=0\n",
+    "                0.3, # max y=0.0002\n",
+    "                color=colors[j]\n",
+    "            )\n",
+    "        mean = subset['marks_obtained'].sum()/subset['maximum_marks'].sum()\n",
+    "        axs[i].scatter([mean], [0.3], color='black', s=15)\n",
+    "\n",
+    "        global_mean = plot_data['normalized_marks'].mean()\n",
+    "        axs[i].axvline(global_mean, color='#525252', linestyle='--')\n",
+    "\n",
+    "\n",
+    "        axs[i].set_xlim(0, 1)\n",
+    "        axs[i].set_ylim(0,3)\n",
+    "\n",
+    "        # Add the learning indicator text as the title\n",
+    "        axs[i].set_title(indicator, loc='left', fontsize=12, fontweight='bold')\n",
+    "\n",
+    "        # Remove y-axis label\n",
+    "        axs[i].set_ylabel('')\n",
+    "\n",
+    "        # Add a horizontal line for the baseline\n",
+    "        axs[i].axhline(0, color='black', linewidth=1.3, linestyle='-')\n",
+    "\n",
+    "    # Set common labels\n",
+    "    plt.xlabel('Normalized Marks', fontsize=12, fontweight='bold')\n",
+    "\n",
+    "    #legend\n",
+    "    subax = inset_axes(\n",
+    "        parent_axes=axs[0],\n",
+    "        width=\"40%\",\n",
+    "        height=\"350%\",\n",
+    "        loc=1\n",
+    "    )\n",
+    "    subax.set_xticks([])\n",
+    "    subax.set_yticks([])\n",
+    "    beautiful_subset = df[df['word'] == 'beautiful']\n",
+    "    sns.kdeplot(\n",
+    "        beautiful_subset['price'],\n",
+    "        shade=True,\n",
+    "        ax=subax,\n",
+    "        color='grey',\n",
+    "        edgecolor='lightgrey'\n",
+    "    )\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pp_data = prepare_data_for_ridge_plot(student_df,question_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "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>student_id</th>\n",
+       "      <th>learning_indicator_id</th>\n",
+       "      <th>marks_obtained</th>\n",
+       "      <th>maximum_marks</th>\n",
+       "      <th>normalized_marks</th>\n",
+       "      <th>learning_indicator_text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.700000</td>\n",
+       "      <td>Understand basic algebraic concepts</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>Apply geometric principles</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>Solve linear equations</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>8</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>Interpret statistical data</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>Analyze historical events</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>275</th>\n",
+       "      <td>35</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>6</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>Interpret statistical data</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>276</th>\n",
+       "      <td>35</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>Analyze historical events</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>277</th>\n",
+       "      <td>35</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>Understand economic theories</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>278</th>\n",
+       "      <td>35</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>Apply scientific method</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>279</th>\n",
+       "      <td>35</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>Comprehend literary texts</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>280 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     student_id learning_indicator_id  marks_obtained  maximum_marks  \\\n",
+       "0             1                 LI001               7             10   \n",
+       "1             1                 LI002               5              8   \n",
+       "2             1                 LI003               3              6   \n",
+       "3             1                 LI004               8             12   \n",
+       "4             1                 LI005               6             10   \n",
+       "..          ...                   ...             ...            ...   \n",
+       "275          35                 LI004               6             12   \n",
+       "276          35                 LI005               5             10   \n",
+       "277          35                 LI006               2              8   \n",
+       "278          35                 LI007               3              6   \n",
+       "279          35                 LI008               4              6   \n",
+       "\n",
+       "     normalized_marks              learning_indicator_text  \n",
+       "0            0.700000  Understand basic algebraic concepts  \n",
+       "1            0.625000           Apply geometric principles  \n",
+       "2            0.500000               Solve linear equations  \n",
+       "3            0.666667           Interpret statistical data  \n",
+       "4            0.600000            Analyze historical events  \n",
+       "..                ...                                  ...  \n",
+       "275          0.500000           Interpret statistical data  \n",
+       "276          0.500000            Analyze historical events  \n",
+       "277          0.250000         Understand economic theories  \n",
+       "278          0.500000              Apply scientific method  \n",
+       "279          0.666667            Comprehend literary texts  \n",
+       "\n",
+       "[280 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pp_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/r7/d4c3yx954xl2wr5v109lqgvh0000gn/T/ipykernel_24220/512666519.py:56: FutureWarning: \n",
+      "\n",
+      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
+      "This will become an error in seaborn v0.14.0; please update your code.\n",
+      "\n",
+      "  sns.kdeplot(\n",
+      "/var/folders/r7/d4c3yx954xl2wr5v109lqgvh0000gn/T/ipykernel_24220/512666519.py:56: FutureWarning: \n",
+      "\n",
+      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
+      "This will become an error in seaborn v0.14.0; please update your code.\n",
+      "\n",
+      "  sns.kdeplot(\n",
+      "/var/folders/r7/d4c3yx954xl2wr5v109lqgvh0000gn/T/ipykernel_24220/512666519.py:56: FutureWarning: \n",
+      "\n",
+      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
+      "This will become an error in seaborn v0.14.0; please update your code.\n",
+      "\n",
+      "  sns.kdeplot(\n",
+      "/var/folders/r7/d4c3yx954xl2wr5v109lqgvh0000gn/T/ipykernel_24220/512666519.py:56: FutureWarning: \n",
+      "\n",
+      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
+      "This will become an error in seaborn v0.14.0; please update your code.\n",
+      "\n",
+      "  sns.kdeplot(\n",
+      "/var/folders/r7/d4c3yx954xl2wr5v109lqgvh0000gn/T/ipykernel_24220/512666519.py:56: FutureWarning: \n",
+      "\n",
+      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
+      "This will become an error in seaborn v0.14.0; please update your code.\n",
+      "\n",
+      "  sns.kdeplot(\n",
+      "/var/folders/r7/d4c3yx954xl2wr5v109lqgvh0000gn/T/ipykernel_24220/512666519.py:56: FutureWarning: \n",
+      "\n",
+      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
+      "This will become an error in seaborn v0.14.0; please update your code.\n",
+      "\n",
+      "  sns.kdeplot(\n",
+      "/var/folders/r7/d4c3yx954xl2wr5v109lqgvh0000gn/T/ipykernel_24220/512666519.py:56: FutureWarning: \n",
+      "\n",
+      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
+      "This will become an error in seaborn v0.14.0; please update your code.\n",
+      "\n",
+      "  sns.kdeplot(\n",
+      "/var/folders/r7/d4c3yx954xl2wr5v109lqgvh0000gn/T/ipykernel_24220/512666519.py:56: FutureWarning: \n",
+      "\n",
+      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
+      "This will become an error in seaborn v0.14.0; please update your code.\n",
+      "\n",
+      "  sns.kdeplot(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1200 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "create_ridge_plot(pp_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "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>student_id</th>\n",
+       "      <th>learning_indicator_id</th>\n",
+       "      <th>marks_obtained</th>\n",
+       "      <th>maximum_marks</th>\n",
+       "      <th>normalized_marks</th>\n",
+       "      <th>learning_indicator_text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>LI001</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.700000</td>\n",
+       "      <td>Understand basic algebraic concepts</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>LI002</td>\n",
+       "      <td>5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>Apply geometric principles</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>LI003</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>Solve linear equations</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>8</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>Interpret statistical data</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>Analyze historical events</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>275</th>\n",
+       "      <td>35</td>\n",
+       "      <td>LI004</td>\n",
+       "      <td>6</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>Interpret statistical data</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>276</th>\n",
+       "      <td>35</td>\n",
+       "      <td>LI005</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>Analyze historical events</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>277</th>\n",
+       "      <td>35</td>\n",
+       "      <td>LI006</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>Understand economic theories</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>278</th>\n",
+       "      <td>35</td>\n",
+       "      <td>LI007</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>Apply scientific method</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>279</th>\n",
+       "      <td>35</td>\n",
+       "      <td>LI008</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>Comprehend literary texts</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>280 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     student_id learning_indicator_id  marks_obtained  maximum_marks  \\\n",
+       "0             1                 LI001               7             10   \n",
+       "1             1                 LI002               5              8   \n",
+       "2             1                 LI003               3              6   \n",
+       "3             1                 LI004               8             12   \n",
+       "4             1                 LI005               6             10   \n",
+       "..          ...                   ...             ...            ...   \n",
+       "275          35                 LI004               6             12   \n",
+       "276          35                 LI005               5             10   \n",
+       "277          35                 LI006               2              8   \n",
+       "278          35                 LI007               3              6   \n",
+       "279          35                 LI008               4              6   \n",
+       "\n",
+       "     normalized_marks              learning_indicator_text  \n",
+       "0            0.700000  Understand basic algebraic concepts  \n",
+       "1            0.625000           Apply geometric principles  \n",
+       "2            0.500000               Solve linear equations  \n",
+       "3            0.666667           Interpret statistical data  \n",
+       "4            0.600000            Analyze historical events  \n",
+       "..                ...                                  ...  \n",
+       "275          0.500000           Interpret statistical data  \n",
+       "276          0.500000            Analyze historical events  \n",
+       "277          0.250000         Understand economic theories  \n",
+       "278          0.500000              Apply scientific method  \n",
+       "279          0.666667            Comprehend literary texts  \n",
+       "\n",
+       "[280 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pp_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ngroups = pp_data['learning_indicator_id'].nunique()    # Dynamically calculate the number of rows in the chart.\n",
+    "\n",
+    "bandwidth = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x1000 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(nrows=ngroups, ncols=1, figsize=(8, 10))\n",
+    "axs = axs.flatten() # needed to access each individual axis\n",
+    "\n",
+    "# iterate over axes\n",
+    "words = pp_data['learning_indicator_id'].unique().tolist()\n",
+    "for i, word in enumerate(words):\n",
+    "\n",
+    "    # subset the data for each word\n",
+    "    subset = pp_data[pp_data['learning_indicator_id'] == word]\n",
+    "\n",
+    "    # plot the distribution of prices\n",
+    "    sns.histplot(\n",
+    "        subset['normalized_marks'],\n",
+    "        ax=axs[i]\n",
+    "    )\n",
+    "\n",
+    "    # set title and labels\n",
+    "    axs[i].set_xlim(0, 10000)\n",
+    "    axs[i].set_ylim(0, 0.001)\n",
+    "    axs[i].set_ylabel('')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}