From 470b07b3a3fdd3bfe9700538c41f0cd35d9d5ed7 Mon Sep 17 00:00:00 2001 From: YannAhlgrim Date: Fri, 6 Jun 2025 11:22:12 +0200 Subject: [PATCH] zufallszahlenlabor update --- .../0_zufallszahlenlabor/main.ipynb | 193 +++++++++++++++++- 1 file changed, 185 insertions(+), 8 deletions(-) diff --git a/1_data_science/0_zufallszahlenlabor/main.ipynb b/1_data_science/0_zufallszahlenlabor/main.ipynb index 28d32f6..c183eb8 100644 --- a/1_data_science/0_zufallszahlenlabor/main.ipynb +++ b/1_data_science/0_zufallszahlenlabor/main.ipynb @@ -105,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -134,7 +134,7 @@ "\n", "np.random.seed(22)\n", "\n", - "# Parameter für die geometrische Verteilung\n", + "# Parameter fuer die geometrische Verteilung\n", "durchschnitt_tage = 125\n", "anzahl_maschinen = 1000000\n", "\n", @@ -166,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -202,7 +202,7 @@ "\n", "np.random.seed(33)\n", "\n", - "# Parameter für die Binomialverteilung\n", + "# Parameter fuer die Binomialverteilung\n", "anzahl_tage = 1_000_000\n", "anzahl_kunden_pro_tag = 10_000\n", "laden_oeffnungszeiten = 12 # Stunden\n", @@ -370,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -405,7 +405,7 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "# Berechnung der Parameter für die Lognormalverteilung\n", + "# Berechnung der Parameter fuer die Lognormalverteilung\n", "mu = np.log(median_jahreseinkommen)\n", "\n", "# Berechnung von sigma mit der variable mu\n", @@ -438,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -472,7 +472,7 @@ "a = bmi_untere_grenze\n", "mu = 26 # Durchschnittlicher BMI\n", "\n", - "# alpha und beta für die Beta-Verteilung\n", + "# alpha und beta fuer die Beta-Verteilung\n", "alpha = 4\n", "beta = 7\n", "\n", @@ -805,6 +805,183 @@ "plt.tight_layout()\n", "plt.show()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Häufigkeitstabelle" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Vorgegebene Häufigkeitstabelle:\n", + " bis 20 ueber 20 bis 40 ueber 40 bis 60 ueber 60 bis 80 ueber 80\n", + "Mann 10000 15000 10000 5000 5000\n", + "Frau 18000 11000 14000 7000 5000\n", + "\n", + "Simulierte Häufigkeitstabelle:\n", + "Alter bis 20 ueber 20 bis 40 ueber 40 bis 60 ueber 60 bis 80 \\\n", + "Geschlecht \n", + "Frau 17915 11036 13993 6945 \n", + "Mann 10094 14974 10016 5145 \n", + "\n", + "Alter ueber 80 \n", + "Geschlecht \n", + "Frau 4999 \n", + "Mann 4883 \n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Vorgabeverteilung\n", + "alter_klassen = ['bis 20', 'ueber 20 bis 40', 'ueber 40 bis 60', 'ueber 60 bis 80', 'ueber 80']\n", + "maenner = np.array([10_000, 15_000, 10_000, 5_000, 5_000])\n", + "frauen = np.array([18_000, 11_000, 14_000, 7_000, 5_000])\n", + "\n", + "gesamt_maenner = maenner.sum()\n", + "gesamt_frauen = frauen.sum()\n", + "gesamt = gesamt_maenner + gesamt_frauen\n", + "\n", + "# Wahrscheinlichkeiten fuer Geschlecht\n", + "p_maenner = gesamt_maenner / gesamt\n", + "p_frauen = gesamt_frauen / gesamt\n", + "\n", + "# Bedingte Wahrscheinlichkeiten fuer Alter gegeben Geschlecht\n", + "p_alter_maenner = maenner / gesamt_maenner\n", + "p_alter_frauen = frauen / gesamt_frauen\n", + "\n", + "n = 100_000\n", + "\n", + "# Simuliere Geschlecht\n", + "geschlecht = np.random.choice(['Mann', 'Frau'], size=n, p=[p_maenner, p_frauen])\n", + "\n", + "# Simuliere Alter bedingt auf Geschlecht\n", + "alter = []\n", + "for g in geschlecht:\n", + " if g == 'Mann':\n", + " alter.append(np.random.choice(alter_klassen, p=p_alter_maenner))\n", + " else:\n", + " alter.append(np.random.choice(alter_klassen, p=p_alter_frauen))\n", + "alter = np.array(alter)\n", + "\n", + "# Häufigkeitstabelle simuliert\n", + "simuliert = pd.crosstab(geschlecht, alter, rownames=['Geschlecht'], colnames=['Alter'])\n", + "\n", + "# Häufigkeitstabelle vorgegeben\n", + "vorgegeben = pd.DataFrame({'bis 20': [maenner[0], frauen[0]],\n", + " 'ueber 20 bis 40': [maenner[1], frauen[1]],\n", + " 'ueber 40 bis 60': [maenner[2], frauen[2]],\n", + " 'ueber 60 bis 80': [maenner[3], frauen[3]],\n", + " 'ueber 80': [maenner[4], frauen[4]]},\n", + " index=['Mann', 'Frau'])\n", + "\n", + "# Grafische Darstellung\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 6), sharey=True)\n", + "vorgegeben.T.plot(kind='bar', stacked=True, ax=axes[0], color=['blue', 'red'])\n", + "axes[0].set_title('Vorgegebene Verteilung')\n", + "axes[0].set_ylabel('Anzahl')\n", + "axes[0].set_xlabel('Alter')\n", + "simuliert.T.plot(kind='bar', stacked=True, ax=axes[1], color=['blue', 'red'])\n", + "axes[1].set_title('Simulierte Verteilung')\n", + "axes[1].set_xlabel('Alter')\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(\"Vorgegebene Häufigkeitstabelle:\")\n", + "print(vorgegeben)\n", + "print(\"\\nSimulierte Häufigkeitstabelle:\")\n", + "print(simuliert)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bedingte Verteilung" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "np.random.seed(11)\n", + "\n", + "n = 1_000_000\n", + "anzahl_kunden_pro_tag = 10_000\n", + "laden_oeffnungszeiten = 12\n", + "\n", + "tageszeiten = ['Früh', 'Vormittag', 'Nachmittag', 'Abend']\n", + "anteile = np.array([0.35, 0.05, 0.20, 0.40])\n", + "\n", + "# Simulation\n", + "sim_tageszeit = np.random.choice(tageszeiten, size=n, p=anteile)\n", + "\n", + "# Berechnung der Anzahl Kunden pro Tageszeit\n", + "anzahl_kunden = np.zeros(len(tageszeiten))\n", + "for i, t in enumerate(tageszeiten):\n", + " anzahl_kunden[i] = np.random.binomial(anzahl_kunden_pro_tag, anteile[i], n).mean()\n", + "\n", + "df = pd.DataFrame({\n", + " 'Tageszeit': tageszeiten,\n", + " 'Anzahl Kunden': anzahl_kunden\n", + "})\n", + "\n", + "# Plot der Verteilung\n", + "for i, t in enumerate(tageszeiten):\n", + " plt.bar(t, anzahl_kunden[i], label=t, alpha=0.7)\n", + "plt.title('Anzahl Kunden pro Tageszeit')\n", + "plt.xlabel('Tageszeit')\n", + "plt.ylabel('Anzahl Kunden')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": {