smartcourseevaluation/Kursevaluation_Python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aca45071-42cb-49b5-9e91-a9a2942ab1ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in /opt/homebrew/Cellar/jupyterlab/4.3.6/libexec/lib/python3.13/site-packages (2.2.3)\n",
      "Requirement already satisfied: matplotlib in /opt/homebrew/Cellar/jupyterlab/4.3.6/libexec/lib/python3.13/site-packages (3.10.1)\n",
      "Requirement already satisfied: seaborn in /opt/homebrew/Cellar/jupyterlab/4.3.6/libexec/lib/python3.13/site-packages (0.13.2)\n",
      "Requirement already satisfied: numpy>=1.26.0 in /opt/homebrew/Cellar/jupyterlab/4.3.6/libexec/lib/python3.13/site-packages (from pandas) (2.2.4)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/homebrew/Cellar/jupyterlab/4.3.6/libexec/lib/python3.13/site-packages (from pandas) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/homebrew/Cellar/jupyterlab/4.3.6/libexec/lib/python3.13/site-packages (from pandas) (2025.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /opt/homebrew/Cellar/jupyterlab/4.3.6/libexec/lib/python3.13/site-packages (from pandas) (2025.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /opt/homebrew/Cellar/jupyterlab/4.3.6/libexec/lib/python3.13/site-packages (from matplotlib) (1.3.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/homebrew/Cellar/jupyterlab/4.3.6/libexec/lib/python3.13/site-packages (from matplotlib) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /opt/homebrew/Cellar/jupyterlab/4.3.6/libexec/lib/python3.13/site-packages (from matplotlib) (4.57.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/homebrew/Cellar/jupyterlab/4.3.6/libexec/lib/python3.13/site-packages (from matplotlib) (1.4.8)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/homebrew/Cellar/jupyterlab/4.3.6/libexec/lib/python3.13/site-packages (from matplotlib) (24.2)\n",
      "Requirement already satisfied: pillow>=8 in /opt/homebrew/Cellar/jupyterlab/4.3.6/libexec/lib/python3.13/site-packages (from matplotlib) (11.1.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /opt/homebrew/Cellar/jupyterlab/4.3.6/libexec/lib/python3.13/site-packages (from matplotlib) (3.2.3)\n",
      "Requirement already satisfied: six>=1.5 in /opt/homebrew/Cellar/jupyterlab/4.3.6/libexec/lib/python3.13/site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49m/opt/homebrew/Cellar/jupyterlab/4.3.6/libexec/bin/python -m pip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pandas matplotlib seaborn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "14ad9809-9030-4858-ae0e-ababf7e2bd6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.backends.backend_pdf import PdfPages\n",
    "\n",
    "# Load CSV\n",
    "df = pd.read_csv(\"Kursevaluation_12052025.csv\")\n",
    "\n",
    "# Set style\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Calculate absolute number\n",
    "def absolute_value(val):\n",
    "    total = sum(course_source_counts)\n",
    "    absolute = int(round(val / 100 * total))\n",
    "    return f'{absolute}' \n",
    "\n",
    "with PdfPages('evaluation.pdf') as pdf:\n",
    "\n",
    "    # 1. Semester Distribution (Line Plot)\n",
    "    semester_counts = df['Semester'].value_counts().sort_index()\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    sns.lineplot(x=semester_counts.index, y=semester_counts.values, marker=\"o\")\n",
    "    plt.title(\"Semester Distribution\")\n",
    "    plt.xlabel(\"Semester\")\n",
    "    plt.ylabel(\"Number of Students\")\n",
    "    plt.tight_layout()\n",
    "    pdf.savefig()\n",
    "    plt.close()\n",
    "    \n",
    "    # 2. Gender Distribution (Bar Chart)\n",
    "    gender_counts = df['Geschlecht'].value_counts()\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    ax = sns.barplot(x=gender_counts.index, y=gender_counts.values, palette=\"pastel\", hue=gender_counts.index, legend=False)\n",
    "    # Add numbers above the bars\n",
    "    for i, v in enumerate(gender_counts.values):\n",
    "        ax.text(i, v + 0.2, str(v), color='black', ha='center', fontweight='bold')\n",
    "    plt.title(\"Gender Distribution\")\n",
    "    plt.xlabel(\"Gender\")\n",
    "    plt.ylabel(\"Number of Students\")\n",
    "    plt.tight_layout()\n",
    "    pdf.savefig()\n",
    "        \n",
    "    # 3. Faculty Distribution (Bar Chart)\n",
    "    # First replace values in the DataFrame\n",
    "    df['Fakultät'] = df['Fakultät'].replace({\n",
    "        'B (Biotechnologie)': 'B',\n",
    "        'N (Informationstechnik)': 'N',\n",
    "        'M (Maschinenbau)': 'M',\n",
    "        'I (Informatik)': 'I',\n",
    "        'E (Elektrotechnik)': 'E',\n",
    "    })\n",
    "    faculty_counts = df['Fakultät'].value_counts()\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    ax = sns.barplot(x=faculty_counts.index, y=faculty_counts.values, palette=\"muted\", hue=faculty_counts.index, legend=False)\n",
    "    for i, v in enumerate(faculty_counts.values):\n",
    "        ax.text(i, v + 0.2, str(v), color='black', ha='center', fontweight='bold')\n",
    "    plt.title(\"Faculty Distribution\")\n",
    "    plt.xlabel(\"Faculty\")\n",
    "    plt.ylabel(\"Number of Students\")\n",
    "    plt.tight_layout()\n",
    "    pdf.savefig()\n",
    "    plt.close()\n",
    "    \n",
    "    # 4. Course Discovery (Pie Chart)\n",
    "    course_source_counts = df['Wie hast du von diesem Kurs erfahren?'].value_counts()\n",
    "    plt.figure(figsize=(6, 6))\n",
    "    plt.pie(course_source_counts, labels=course_source_counts.index, autopct=absolute_value, startangle=140)\n",
    "    plt.title(\"How Did Students Hear About the Course?\")\n",
    "    plt.tight_layout()\n",
    "    pdf.savefig()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5148f744-38e2-41e0-91cc-abb57b0b510a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "# Plot directly from df, set hue to 'Geschlecht' to apply the palette\n",
    "ax = sns.countplot(data=df, x='Geschlecht', palette='pastel', hue='Geschlecht', legend=False)\n",
    "\n",
    "# Add numbers above bars\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.text(p.get_x() + p.get_width() / 2., height + 0.2, int(height),\n",
    "            ha=\"center\", va=\"bottom\", fontweight='bold')\n",
    "\n",
    "plt.title(\"Gender Distribution\")\n",
    "plt.xlabel(\"Gender\")\n",
    "plt.ylabel(\"Number of Students\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "65224540-b276-4486-ac44-d0341ccb830b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0                      B\n",
      "1                      N\n",
      "2                      N\n",
      "3                      M\n",
      "4                      B\n",
      "5                      I\n",
      "6                      M\n",
      "7                      B\n",
      "8                      B\n",
      "9                      B\n",
      "10                     M\n",
      "11                     B\n",
      "12                     B\n",
      "13                     I\n",
      "14    E (Elektrotechnik)\n",
      "15                     B\n",
      "16                     M\n",
      "17                     I\n",
      "18                     B\n",
      "19                     M\n",
      "Name: Fakultät, dtype: object\n",
      "0     B\n",
      "1     N\n",
      "2     N\n",
      "3     M\n",
      "4     B\n",
      "5     I\n",
      "6     M\n",
      "7     B\n",
      "8     B\n",
      "9     B\n",
      "10    M\n",
      "11    B\n",
      "12    B\n",
      "13    I\n",
      "14    E\n",
      "15    B\n",
      "16    M\n",
      "17    I\n",
      "18    B\n",
      "19    M\n",
      "Name: Fakultät, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df['Fakultät']) \n",
    "\n",
    "\n",
    "\n",
    "print(df['Fakultät']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91f872d0-e7e9-4d56-88fe-570db4b2b4cf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df684c5f-b8f8-40d7-966c-9b0db7c35e5f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.13.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}