diff --git a/_toc.yml b/_toc.yml
index 00fa1fe417ff37ae65d6ef90b1a321e14bf50766..1f0236aabbd162a4731d543d48bfe4e5838929a6 100644
--- a/_toc.yml
+++ b/_toc.yml
@@ -15,6 +15,7 @@ parts:
- file: zufallsvariablen-verteilungen
- file: erwartungswert
- file: numpy
+ - file: explorative-analyse-mit-numpy
- caption: Stetige Stochastik und Scipy
numbered: true
diff --git a/explorative-analyse-mit-numpy.ipynb b/explorative-analyse-mit-numpy.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..52ad14346192e65ee2a3f29ab2cb641a2b7ade2f
--- /dev/null
+++ b/explorative-analyse-mit-numpy.ipynb
@@ -0,0 +1,377 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "92481f49-d2f9-417a-bf8f-9fe035244456",
+ "metadata": {},
+ "source": [
+ "# Titanic EDA, erster Anlauf\n",
+ "\n",
+ "In diesem Notebook nutzen wir nur Numpy (kein Pandas, Scikit-learn o.ä.) um eine diskrete Analyse des Kaggle-Titanic-Datensatzes durchzuführen. Die naheliegenden Aufgaben sind explorative Analyse (EDA) und das Modellieren der Variable 'Überlebenswahrscheinlichkeit' für die Instanzen (ein Auszug aus der Passagierliste der Titanic). Wir wollen am Rande zur Motivation auch eine erste multivariate Analyse betrachten, auch wenn wir die Theorie dazu noch nicht besprochen haben.\n",
+ "\n",
+ "## Daten laden\n",
+ "\n",
+ "Wir werden erstmal naiv die Komma-separierte Datei von Kaggle (die mit Überschriften daher kommt) laden, und schauen, was passiert."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "0219cbda-6932-4924-a566-0aaa30a8a5a5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Some errors were detected !\n",
+ " Line #2 (got 13 columns instead of 12)\n",
+ " Line #3 (got 13 columns instead of 12) \n",
+ "\t...\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "filename = 'titanic.csv'\n",
+ "\n",
+ "try:\n",
+ " data = np.genfromtxt(\n",
+ " filename,\n",
+ " delimiter=',',\n",
+ " dtype=None,\n",
+ " encoding='utf-8',\n",
+ " names=True\n",
+ " )\n",
+ "except ValueError as e:\n",
+ " print(\"\\n\".join(e.args[0].split(\"\\n\")[:3]), \"\\n\\t...\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "34f0a457-595d-43ed-bcca-fe41b94a802b",
+ "metadata": {},
+ "source": [
+ "Offenbar enthält Zeile 2 mehr Variablen als die Überschriftzeile!\n",
+ "\n",
+ "Wir drucken uns die ersten zwei Zeilen zur manuellen Inspektion aus:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "06181214-71f4-4611-9d7f-43e898a98952",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked\n",
+ "\n",
+ "1,0,3,\"Braund, Mr. Owen Harris\",male,22,1,0,A/5 21171,7.25,,S\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "with open(filename, \"r\") as file:\n",
+ " for i in range(2):\n",
+ " print(file.readline())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "17cb9168-8bd2-4f52-970c-91db333c9a44",
+ "metadata": {},
+ "source": [
+ "Die einfachste Lösung ist nun einen besseren CSV-Parser einzusetzen. Damit wir uns jetzt nicht damit auseinandersetzen müssen, tun wir einfach so, als hätten wir das in der Vergangenheit getan. Tatsächlich ist die CSV mit Libreoffice in ein TSV (tab-separated-values) umgewandelt worden."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "383e10f3-a3ee-48f7-b3f4-6df4f01c0b0f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "filename = 'titanic.tsv'\n",
+ "\n",
+ "data = np.genfromtxt(\n",
+ " filename,\n",
+ " delimiter='\\t',\n",
+ " dtype=None,\n",
+ " encoding='utf-8',\n",
+ " names=True\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "e1c33de5-500e-45f2-a0e2-e8dbd3a12cf0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Columns: ('PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked')\n",
+ "First 5 rows:\n",
+ "[(1, 0, 3, 'Braund, Mr. Owen Harris', 'male', 22., 1, 0, 'A/5 21171', 7.25000e+00, '', 'S')\n",
+ " (2, 1, 1, 'Cumings, Mrs. John Bradley (Florence Briggs Thayer)', 'female', 38., 1, 0, 'PC 17599', 7.12833e+01, 'C85', 'C')\n",
+ " (3, 1, 3, 'Heikkinen, Miss. Laina', 'female', 26., 0, 0, 'STON/O2. 3101282', 7.92500e+03, '', 'S')\n",
+ " (4, 1, 1, 'Futrelle, Mrs. Jacques Heath (Lily May Peel)', 'female', 35., 1, 0, '113803', 5.31000e+01, 'C123', 'S')\n",
+ " (5, 0, 3, 'Allen, Mr. William Henry', 'male', 35., 0, 0, '373450', 8.05000e+00, '', 'S')]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"Columns:\", data.dtype.names)\n",
+ "print(\"First 5 rows:\")\n",
+ "print(data[:5])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "18f77c7d-3e8c-4546-b21d-34a58589de6a",
+ "metadata": {},
+ "source": [
+ "## Relevante Features auswählen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "a1a96cb3-1db2-4118-a755-48d400870d85",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Unused columns: {'Name', 'Cabin', 'PassengerId', 'Fare', 'Ticket'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "survived = data['Survived'].astype(int)\n",
+ "pclass = data['Pclass'].astype(int)\n",
+ "sex = data['Sex']\n",
+ "age = data['Age'] # may contain nan\n",
+ "sibsp = data['SibSp'].astype(int)\n",
+ "parch = data['Parch'].astype(int)\n",
+ "embarked = data['Embarked']\n",
+ "\n",
+ "used_columns = {'Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Embarked'}\n",
+ "all_columns = set(data.dtype.names)\n",
+ "unused_columns = all_columns - used_columns\n",
+ "print(\"Unused columns:\", unused_columns)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bae16ee-fdfa-4b1e-a0b7-9947bb670f91",
+ "metadata": {},
+ "source": [
+ "## NaN = Not a Number"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "886315e7-fc10-4266-99c3-0d6a2932413d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Number of missing Age entries: 177\n",
+ "Filled missing ages with median: 28.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"\\nNumber of missing Age entries:\", np.sum(np.isnan(age)))\n",
+ "\n",
+ "# Handle missing ages by filling with median (just as an example)\n",
+ "non_nan_age = age[~np.isnan(age)]\n",
+ "age_filled = age.copy()\n",
+ "age_filled[np.isnan(age)] = np.median(non_nan_age)\n",
+ "\n",
+ "print(\"Filled missing ages with median:\", np.median(non_nan_age))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "33526f3f-ac0d-4e18-ac45-c59fdde16e5a",
+ "metadata": {},
+ "source": [
+ "## Überleben analysieren"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "5595d36d-ea2d-40b8-9702-eb3b8c93a117",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Sex: female | Total: 314 | Survived: 233 | Rate: 0.74\n",
+ "Sex: male | Total: 577 | Survived: 109 | Rate: 0.19\n",
+ "Pclass: 1 | Total: 216 | Survived: 136 | Rate: 0.63\n",
+ "Pclass: 2 | Total: 184 | Survived: 87 | Rate: 0.47\n",
+ "Pclass: 3 | Total: 491 | Survived: 119 | Rate: 0.24\n",
+ "Age Group: Adult | Total: 619 | Survived: 243 | Rate: 0.39\n",
+ "Age Group: Child | Total: 246 | Survived: 92 | Rate: 0.37\n",
+ "Age Group: Senior | Total: 26 | Survived: 7 | Rate: 0.27\n"
+ ]
+ }
+ ],
+ "source": [
+ "# neues Feature\n",
+ "def survival_rate(mask):\n",
+ " total = np.sum(mask)\n",
+ " survived_total = np.sum(survived[mask])\n",
+ " rate = survived_total / total if total > 0 else 0\n",
+ " return total, survived_total, rate\n",
+ "\n",
+ "# Gruppieren nach Sex\n",
+ "for s in np.unique(sex):\n",
+ " mask = (sex == s)\n",
+ " total, survived_s, rate = survival_rate(mask)\n",
+ " print(f\"Sex: {s:6s} | Total: {total:3d} | Survived: {survived_s:3d} | Rate: {rate:.2f}\")\n",
+ "\n",
+ "# Gruppieren nach Passenger Class\n",
+ "for c in np.unique(pclass):\n",
+ " mask = (pclass == c)\n",
+ " total, survived_c, rate = survival_rate(mask)\n",
+ " print(f\"Pclass: {c} | Total: {total:3d} | Survived: {survived_c:3d} | Rate: {rate:.2f}\")\n",
+ "\n",
+ "# Binning\n",
+ "age_clean = age.copy()\n",
+ "age_clean[np.isnan(age_clean)] = -1 # Fehlende Einträge\n",
+ "\n",
+ "age_group = np.full(age.shape, 'Unknown', dtype='<U10')\n",
+ "age_group[age_clean >= 0] = 'Adult'\n",
+ "age_group[age_clean < 13] = 'Child'\n",
+ "age_group[age_clean >= 60] = 'Senior'\n",
+ "\n",
+ "for grp in np.unique(age_group):\n",
+ " mask = (age_group == grp)\n",
+ " total, survived_grp, rate = survival_rate(mask)\n",
+ " print(f\"Age Group: {grp:7s} | Total: {total:3d} | Survived: {survived_grp:3d} | Rate: {rate:.2f}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "91067a03-cdf2-4444-adb6-de21a0d992e7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Visualisierung: Survival by Sex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "1d815c04-9e83-4d80-b66d-d5363d49e332",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x300 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "labels = []\n",
+ "rates = []\n",
+ "for s in np.unique(sex):\n",
+ " mask = (sex == s)\n",
+ " _, _, rate = survival_rate(mask)\n",
+ " labels.append(s)\n",
+ " rates.append(rate)\n",
+ "\n",
+ "plt.figure(figsize=(5, 3))\n",
+ "plt.bar(labels, rates, color='skyblue')\n",
+ "plt.title('Survival Rate by Sex')\n",
+ "plt.ylabel('Rate')\n",
+ "plt.ylim(0, 1)\n",
+ "plt.grid(axis='y')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cdaa5e32-4d3d-4cad-8b2e-228daa4e9ff2",
+ "metadata": {},
+ "source": [
+ "## Blick auf Korrelationen\n",
+ "\n",
+ "Wir haben noch nicht formal eingeführt, was das ist, aber so lassen wir schnell den sogenannten Korrelationskoeffizienten ausrechnen. Wir können uns für später aufheben, wie wir das interpretieren, was wir hier sehen."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "460cb886-4477-4d69-a4c8-73d29301f999",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Correlation between being female and survival: 0.54\n",
+ "Correlation between being male and survival: -0.54\n"
+ ]
+ }
+ ],
+ "source": [
+ "is_female = (sex == 'female').astype(int)\n",
+ "is_male = (sex == 'male').astype(int)\n",
+ "\n",
+ "corf = np.corrcoef(survived, is_female)[0, 1]\n",
+ "corm = np.corrcoef(survived, is_male)[0, 1]\n",
+ "\n",
+ "print(f\"Correlation between being female and survival: {corf:.2f}\")\n",
+ "print(f\"Correlation between being male and survival: {corm:.2f}\")"
+ ]
+ }
+ ],
+ "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": "3.11.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/titanic.tsv b/titanic.tsv
new file mode 100644
index 0000000000000000000000000000000000000000..ed3ac3dcc4c5ebe420e6d7926fca2a0d086aa8c1
--- /dev/null
+++ b/titanic.tsv
@@ -0,0 +1,892 @@
+PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
+1 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.25 S
+2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0 PC 17599 71.2833 C85 C
+3 1 3 Heikkinen, Miss. Laina female 26 0 0 STON/O2. 3101282 7925 S
+4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0 113803 53.1 C123 S
+5 0 3 Allen, Mr. William Henry male 35 0 0 373450 8.05 S
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