diff --git a/04-pandas-und-seaborn/02-old-faithful.ipynb b/04-pandas-und-seaborn/02-old-faithful.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3daf0a689df15cb46d1d9b1fa512affdd6f44b25
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+++ b/04-pandas-und-seaborn/02-old-faithful.ipynb
@@ -0,0 +1,291 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Old Faithful Geysir\n",
+    "\n",
+    "![](attachment:old-faithful.jpg \"Gemälde des Old Faithful Geysir\")\n",
+    "\n",
+    "Der Old Faithful Geysir ist ein Geysir im Yellowstone-Nationalpark in\n",
+    "den USA. Er ist bekannt für seine regelmäßigen Ausbrüche, die alle 34\n",
+    "bis 125 Minuten auftreten und 1.5 bis 5 min andauern. Der Geysir kann\n",
+    "Wasser bis zu einer Höhe von 56 Metern ausstoßen.\n",
+    "\n",
+    "## a) Einlesen und einfache Statistik\n",
+    "\n",
+    "In der Datei `old-faithful-1938.csv` sind 272 aufeinanderfolgende Daten\n",
+    "von 1938 zu Eruptionen des Old Faithful Geysirs gespeichert. Die Spalten\n",
+    "sind:\n",
+    "\n",
+    "-   `eruption_duration`: Dauer des Ausbruchs in Minuten\n",
+    "-   `waiting_time`: Zeit bis zum nächsten Ausbruch in Minuten\n",
+    "\n",
+    "Lesen Sie die Daten mit Pandas ein und ermitteln Sie die kürzeste,\n",
+    "längste und mittlere Wartezeit bis zur nächsten Eruption.\n",
+    "\n",
+    "Hier Ihr Start-Code:"
+   ],
+   "attachments": {
+    "old-faithful.jpg": {
+     "image/jpeg": 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Z8ls5cAAY7ECuhVFYwcNdCeGzJBIG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TWlrehxX29IluzG0EaTttkjc5Mbjv8A7pwP0rnp13FNJaM0nBSd2zJ1VPs8UV5PGrLOhbZF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\n7c5x1HqK0qU4ySnEzhOUZ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+    }
+   },
+   "id": "0007-0b99a24526af47653d58d5771072dd8e6c54bfc3ced388fedeb30d686b6"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# TODO: Daten laden\n",
+    "df1 = ...\n",
+    "\n",
+    "# TODO: Statistische Werte ermitteln\n",
+    "shortest = ...\n",
+    "longest = ...\n",
+    "average = ..."
+   ],
+   "id": "0008-571c8a583e7ade647560d126e99bf1a875804d5f8489696e4c3bab7ecc0"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Tests zu a)\n",
+    "\n",
+    "Hier eine Ausgabe mit den statischen Werten:"
+   ],
+   "id": "0010-a201a2eb23ee94cf852ac27a8e841d0555ed7620cd8a5851967c742baba"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Old Faithful bricht alle 43 bis 96 min und alle 70.8970588235294 min im Mittel aus."
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Old Faithful bricht alle\", shortest, \"bis\", longest, \"min und alle\", average, \"min im Mittel aus.\")"
+   ],
+   "id": "0011-9138aae2c7a798439e7af9c10aaea671af179a5f4494248d429f09101f0"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## b) Messdauer\n",
+    "\n",
+    "<p><img\n",
+    "src=\"data:image/svg+xml;base64,<?xml version="1.0" encoding="UTF-8"?>
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\" /></p>\n",
+    "\n",
+    "Die Messungen fanden alle nacheinander statt. Wenn Sie davon ausgehen,\n",
+    "dass die Wartezeit jeweils direkt bis zum nächste Ausbruch geht und nach\n",
+    "dem Ausbruch direkt die nächste Wartezeit beginnt, wie lange hat es\n",
+    "gedauert den gesamten Datensatz aufzunehmen? Geben Sie die Zeitdauer in\n",
+    "Tagen, Stunden und Minuten an."
+   ],
+   "id": "0014-7db59db54c6e905d2e95c368903334a6b44315ca8e0f4addb76e631996c"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [],
+   "id": "0015-44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## c) Differenz der Wartezeiten\n",
+    "\n",
+    "Berechnen Sie die Differenzen der Wartezeiten. Zum Beispiel beträgt die\n",
+    "erste Wartezeit 79 min und die zweite 54 min. Die Differenz ist also -25\n",
+    "min. Geben Sie die größte Abnahme und die größte Zunahme der Wartezeiten\n",
+    "als Absolutzahlen aus.\n",
+    "\n",
+    "Plotten Sie anschließend das Histogramm der Differenzen um zu sehen, ob\n",
+    "sich die Wartezeiten vielleicht nur langsam verändern oder langsame\n",
+    "Änderungen zumindest wahrscheinlicher als schnelle Änderungen sind."
+   ],
+   "id": "0018-f70407a5074a163bc2e8536302dab3036968b75eb91be17cda46af668ce"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [],
+   "id": "0019-44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## d) Modellierung\n",
+    "\n",
+    "Wenn man ein Modell erstellt, will man auch wissen, wie gut es ist.\n",
+    "Daher macht man oft ein triviales Modell, damit man einen Vergleichswert\n",
+    "hat. Als triviales Modell für die Wartezeit nehmen wir zunächst eine\n",
+    "konstante Vorhersage und zwar den Mittelwert der Wartezeiten. Wie hoch\n",
+    "ist der mittlere absolute Fehler (MAE) und die Wurzel aus dem mittleren\n",
+    "quadratischen Fehler (RMSE) für das triviale Modell? Berechnen Sie die\n",
+    "Werte ohne Schleifen.\n",
+    "\n",
+    "$$MAE = \\frac 1 n \\sum_{i=0}^{n-1} |y - \\hat y| = \\frac{1}{272} \\cdot (|54 - 70.9| + |74 - 70.9| + \\dots + |74 - 70.9|)$$\n",
+    "$$RMSE = \\sqrt{\\frac 1 n \\sum_{i=0}^{n-1} (y - \\hat y)^2}$$\n",
+    "\n",
+    "Dabei ist $\\hat y$ die Vorhersage, also zunächst die mittlere\n",
+    "Wartedauer."
+   ],
+   "id": "0023-dd90a9c5f47aeb463fc83037cea1afca29221b76b28c628bb2b2413b7d6"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [],
+   "id": "0024-44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Als nächstes betrachten wir als Modell als nächste Wartezeit die\n",
+    "vorherige Wartezeit vorherzusagen. Wie hoch wären die Fehlerwerte dann?\n",
+    "Die Berechnung des MAE wäre also\n",
+    "\n",
+    "$$MAE = \\frac{1}{271} \\cdot (|54 - 79| + |74 - 54| + \\dots + |74 - 46|)$$"
+   ],
+   "id": "0026-1314ebc28c0c6337a876c6bd3e8c27e6be258572fb9e604a2eaca71f30b"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [],
+   "id": "0027-44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wenn man die Fehlerwerte verschiedener Modelle vergleicht, kriegt man\n",
+    "ein Gefühl dafür, wie gut ein Modell ist.\n",
+    "\n",
+    "Schauen Sie sich nun die Daten mit einem Pairplot mit Kernel Density\n",
+    "Estimation (KDE) Plots auf der Diagonalen (`diag_kind`) an (siehe\n",
+    "[Doku](https://seaborn.pydata.org/generated/seaborn.pairplot.html)).\n",
+    "\n",
+    "*Bonus: Versuchen Sie ein besseres Modell zu entwickeln! Sie dürfen auch\n",
+    "die aktuelle Eruptionsdauer verwenden. Denken Sie daran nur einen Teil\n",
+    "der Daten für das Training und einen Teil als Test zu verwenden. Streng\n",
+    "genommen hätten wir vorhin den Mittelwert auch nur auf der Trainingmenge\n",
+    "bilden und auf der Testmenge den Fehler ermitteln dürfen.*"
+   ],
+   "id": "0030-1e5c2934b1940cf84478bfd1fb46024b3cb859d8a2560b75aa40dad6944"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [],
+   "id": "0031-44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## e) Weitere Datensätze\n",
+    "\n",
+    "Es gibt noch weitere Datensätze aus anderen Jahren zum Old Faithful\n",
+    "Geysir. Lesen Sie diese ein und bringen Sie sie in die Form des\n",
+    "Datensatzes von 1938, sodass sie gemeinsam geplottet werden können.\n",
+    "Starten Sie am besten mit einem Scatter-Plot des Datensatzes von 1938,\n",
+    "in dem auf der x-Achse die `eruption_duration` und auf der y-Achse die\n",
+    "`waiting_time` liegen soll.\n",
+    "\n",
+    "`old-faithful-1978.txt`:\n",
+    "\n",
+    "-   Die Daten stammen vom 1. August bis zum 8. August, 1978. Der Tag\n",
+    "    steht in der ersten Spalte.\n",
+    "-   Es ist nicht klar, welche Eruptionen aufeinanderfolgend sind.\n",
+    "-   Das Format ist nicht wirklich CSV, da es mit Leerzeichen gefüllt\n",
+    "    ist, sodass sich Spalten fester breite ergeben. Verwenden Sie\n",
+    "    [`pd.read_fwf`](https://pandas.pydata.org/docs/reference/api/pandas.read_fwf.html)\n",
+    "    anstatt `pd.read_csv` um die Datei einzulesen.\n",
+    "-   Ändern Sie die Spaltennamen der entsprechenden Spalten zu\n",
+    "    `eruption_duration` und `waiting_time`.\n",
+    "\n",
+    "Plotten Sie die Daten in den bestehenden Scatter Plot.\n",
+    "\n",
+    "`old-faithful-1985.csv`:\n",
+    "\n",
+    "-   Die Daten stammen vom 1. August bis zum 15. August, 1985.\n",
+    "-   Es ist eine kontinuierliche Messung.\n",
+    "-   Einige nächtliche Eruptionsdauermesswerte wurden nur als *kurz*,\n",
+    "    *mittel* oder *lang* eingetragen. Diese Werte sind wurden auf 2, 3\n",
+    "    bzw. 4 Minuten geschätzt.\n",
+    "-   Die Spalte `waiting` gibt die Wartezeit **auf** diese Eruption an.\n",
+    "    Sie müssen hier vorverarbeiten, damit dieser Datensatz zu den\n",
+    "    anderen passt.\n",
+    "\n",
+    "Plotten Sie auch diesen Datensatz gemeinsam mit den anderen.\n",
+    "\n",
+    "`old-faithful-2018.csv`:\n",
+    "\n",
+    "-   Die Daten wurden von freiwilligen im Rahmen eines Citizen Science\n",
+    "    Projekts aufgezeichnet.\n",
+    "-   Es können Fehler enthalten sein. Filtern Sie ggf. offensichtliche\n",
+    "    Ausreißer weg.\n",
+    "-   Die Zeiteinheiten sind jeweils Sekunden.\n",
+    "\n",
+    "Wenn Sie den Datensatz hinzugefügt haben, schauen Sie nach\n",
+    "Auffälligkeiten. Sind Anpassungen im Modell notwendig?"
+   ],
+   "id": "0042-a3bb358c1131d6d7c69fb75b624d3eb21b5ab37dcb1d20d8bbf1f0b3450"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [],
+   "id": "0043-44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"
+  }
+ ],
+ "nbformat": 4,
+ "nbformat_minor": 5,
+ "metadata": {}
+}
diff --git a/04-pandas-und-seaborn/folien-code/folien-code.ipynb b/04-pandas-und-seaborn/folien-code/folien-code.ipynb
index 888a25d0560572dc01899315ccf48858bf2378b9..27ac9949c8f524a2b93ef3467203f318bbe2c9e1 100644
--- a/04-pandas-und-seaborn/folien-code/folien-code.ipynb
+++ b/04-pandas-und-seaborn/folien-code/folien-code.ipynb
@@ -17,7 +17,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% import Pandas\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "from IPython.display import display\n",
@@ -30,7 +29,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Iris Flower Dataset\n",
     "url = 'https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv'\n",
     "df = pd.read_csv(url)\n",
     "\n",
@@ -51,7 +49,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Informationen\n",
     "print(df.shape)\n",
     "print(df.columns)\n",
     "print(df.dtypes)\n",
@@ -66,7 +63,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Statistischer Überblick\n",
     "display(df.describe())\n",
     "display(df.describe(exclude='number'))\n",
     "\n"
@@ -78,7 +74,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Kuchendiagramm\n",
     "counts = df['species'].value_counts()\n",
     "display(counts)\n",
     "\n",
@@ -93,7 +88,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Boxplot\n",
     "df.boxplot(column='petal_length', by='species')\n",
     "\n"
    ]
@@ -104,7 +98,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Boxplots aller Features\n",
     "fig, axs = plt.subplots(2, 2, sharey=False)    # y-Achsen unabhängig\n",
     "pd.plotting.boxplot(df, by='species', ax=axs)  # übergebe axs\n",
     "[ax.set_xlabel('') for ax in axs.ravel()]      # entferne x-Labels\n",
@@ -117,7 +110,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Violinenplot\n",
     "import seaborn as sns\n",
     "sns.violinplot(hue='species', y='petal_length', data=df)\n",
     "\n"
@@ -129,7 +121,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Scatterplots\n",
     "df.plot.scatter(x='petal_length', y='petal_width', c='species', colormap='viridis', alpha=0.7)\n",
     "\n"
    ]
@@ -140,7 +131,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Pair Plot\n",
     "sns.pairplot(df, hue='species', plot_kws={'alpha': 0.5})\n",
     "\n"
    ]
@@ -151,7 +141,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Parallele Koordinaten Plot, unskaliert\n",
     "pd.plotting.parallel_coordinates(df, 'species', colormap='viridis', alpha=.5)\n",
     "\n"
    ]
@@ -162,7 +151,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Parallele Koordinaten Plot, normiert\n",
     "from sklearn.preprocessing import minmax_scale\n",
     "num_cols = df.columns.drop('species')\n",
     "df_scaled = df.copy()\n",
@@ -176,7 +164,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Parallele Koordinaten Plot, custom Code from https://stackoverflow.com/a/60401570/2414411\n",
     "import numpy as np\n",
     "from matplotlib.path import Path\n",
     "import matplotlib.patches as patches\n",
@@ -244,7 +231,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Parallele Koordinaten Plot mit Plotly Express\n",
     "import plotly.express as px\n",
     "# fig = px.parallel_coordinates(df, color=\"species\", labels={'species': tuple('ABC')})\n",
     "fig = px.parallel_coordinates(df, color=df[\"species\"].cat.codes)\n",
@@ -258,7 +244,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Slicing\n",
     "cp = df.copy()\n",
     "cp.loc[1, 'sepal_width'] = 1\n",
     "cp.loc[0:2, 'petal_length'] = 2\n",
@@ -275,11 +260,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% komplexe Indizierung\n",
     "display(df.loc[[0, 149, 2], 'petal_width'])\n",
     "\n",
     "part = df.loc[[0, 149, 2], ['petal_width', 'sepal_width']]\n",
-    "part\n",
+    "display(part)\n",
     "\n"
    ]
   },
@@ -289,7 +273,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% integer location\n",
     "display(part.iloc[1, -1])\n",
     "display(part.iloc[:2, -1])\n",
     "display(part.iloc[[0, 1], [0, 1]])\n",
@@ -302,7 +285,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% boolesche Indizierung\n",
     "pw = part.loc[:, 'petal_width'] <= 1\n",
     "sw = part.loc[:, 'sepal_width'] < 3.5\n",
     "display(pw)\n",
@@ -319,7 +301,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Daten fallen lassen\n",
     "display(part.drop(index=149, columns='petal_width'))\n",
     "display(part.drop(index=[149, 0]))\n",
     "\n"
@@ -331,7 +312,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% einzelne Daten hinzufügen\n",
     "part.loc[3] = [2, 6]\n",
     "display(part)\n",
     "part.loc[:, 'weight'] = [1, 2, 3, 4]\n",
@@ -345,7 +325,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% DataFrames zusammenführen\n",
     "a = part.drop(index=3)\n",
     "b = df.loc[:2, ['petal_length', 'petal_width']]\n",
     "display(a)\n",
@@ -361,7 +340,62 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Kategoriale Daten\n",
+    "df = pd.DataFrame({'name': ['Paul', 'John', 'Bill'], 'type': ['student', 'student', 'teacher']})\n",
+    "# kein Effekt, denn name wird name zugewiesen und type wird type zugewiesen\n",
+    "df.loc[:, ['name', 'type']] = df.loc[:, ['type', 'name']]\n",
+    "print(df)\n",
+    "print()\n",
+    "\n",
+    "# kein Effekt, denn name wird name zugewiesen und type wird type zugewiesen\n",
+    "df.loc[:, ['name', 'type']] = df[['type', 'name']]\n",
+    "print(df)\n",
+    "print()\n",
+    "\n",
+    "# tauscht Werte, weil __index__ (also []) kein Alignment hat\n",
+    "df[['name', 'type']] = df[['type', 'name']]\n",
+    "print(df)\n",
+    "print()\n",
+    "\n",
+    "# tauscht Werte, weil auf der rechten Seite ein Numpy-Array steht.\n",
+    "df.loc[:, ['name', 'type']] = df[['type', 'name']].to_numpy()\n",
+    "print(df)\n",
+    "print()\n",
+    "\n",
+    "# spaltenweise Zuweisung (ohne extra Klammern) ergibt Series, also werden keine Spalten aligned\n",
+    "temp = df['name'].copy()        # kopiere die Werte, damit sie im nächsten Schritt nicht überschrieben werden\n",
+    "df.loc[:, 'name'] = df['type']  # tauscht Werte, weil es hier keine Spalten zu alignen gibt (sondern nur Zeilen, die aber zueinander passen) ...\n",
+    "df.loc[:, 'type'] = temp        # ... auf der rechten und linken Seite steht jeweils eine pd.Series\n",
+    "print(df)\n",
+    "print()\n",
+    "\n",
+    "# wegen Alignment hat Zeile 0 keinen Partner und bekommt NaN. Zeile 1 bekommt Zeile 1 zugewiesen.\n",
+    "df.loc[[0]] = df.loc[[1, 2]]\n",
+    "print(df)\n",
+    "print()\n",
+    "\n",
+    "# wegen Alignment hat Zeile 0 keinen Partner und bekommt NaN. Zeile 1 bekommt Zeile 1 zugewiesen.\n",
+    "df.loc[[0, 1]] = df.loc[[1, 2]].to_numpy()\n",
+    "print(df)\n",
+    "print()\n",
+    "\n",
+    "# wegen Alignment hat Spalte name keinen Partner und bekommt NaN.\n",
+    "df.loc[:, ['name']] = df[['type']]\n",
+    "print(df)\n",
+    "print()\n",
+    "\n",
+    "# hier findet kein Alignment der Spalten statt, weil auf der rechten Seite nur eine pd.Series steht, es also keine Spalten zu alignen gibt\n",
+    "df.loc[:, ['name']] = df['type']\n",
+    "print(df)\n",
+    "print()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "df['species']\n",
     "df['species'].info()\n",
     "\n"
@@ -373,7 +407,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Statistische Funktionen\n",
     "X = df.drop(columns='species')\n",
     "y = df['species']\n",
     "\n",
@@ -388,7 +421,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Gruppierung\n",
     "species_means = X.groupby(y).mean()\n",
     "display(species_means)\n",
     "\n",
diff --git a/04-pandas-und-seaborn/folien-code/folien-code.py b/04-pandas-und-seaborn/folien-code/folien-code.py
index b119bfe93b03e2ee6c765420a5ac0268ee968cda..d3229c908ad4562c94380a5b0de12d1f6dfcac60 100644
--- a/04-pandas-und-seaborn/folien-code/folien-code.py
+++ b/04-pandas-und-seaborn/folien-code/folien-code.py
@@ -160,7 +160,7 @@ cp
 display(df.loc[[0, 149, 2], 'petal_width'])
 
 part = df.loc[[0, 149, 2], ['petal_width', 'sepal_width']]
-part
+display(part)
 
 
 # %% integer location
@@ -200,6 +200,56 @@ display(pd.concat((a, b), axis='columns'))
 display(pd.concat((a, b), axis='index'))
 
 
+# %% Alignment
+df = pd.DataFrame({'name': ['Paul', 'John', 'Bill'], 'type': ['student', 'student', 'teacher']})
+# kein Effekt, denn name wird name zugewiesen und type wird type zugewiesen
+df.loc[:, ['name', 'type']] = df.loc[:, ['type', 'name']]
+print(df)
+print()
+
+# kein Effekt, denn name wird name zugewiesen und type wird type zugewiesen
+df.loc[:, ['name', 'type']] = df[['type', 'name']]
+print(df)
+print()
+
+# tauscht Werte, weil __index__ (also []) kein Alignment hat
+df[['name', 'type']] = df[['type', 'name']]
+print(df)
+print()
+
+# tauscht Werte, weil auf der rechten Seite ein Numpy-Array steht.
+df.loc[:, ['name', 'type']] = df[['type', 'name']].to_numpy()
+print(df)
+print()
+
+# spaltenweise Zuweisung (ohne extra Klammern) ergibt Series, also werden keine Spalten aligned
+temp = df['name'].copy()        # kopiere die Werte, damit sie im nächsten Schritt nicht überschrieben werden
+df.loc[:, 'name'] = df['type']  # tauscht Werte, weil es hier keine Spalten zu alignen gibt (sondern nur Zeilen, die aber zueinander passen) ...
+df.loc[:, 'type'] = temp        # ... auf der rechten und linken Seite steht jeweils eine pd.Series
+print(df)
+print()
+
+# wegen Alignment hat Zeile 0 keinen Partner und bekommt NaN. Zeile 1 bekommt Zeile 1 zugewiesen.
+df.loc[[0]] = df.loc[[1, 2]]
+print(df)
+print()
+
+# wegen Alignment hat Zeile 0 keinen Partner und bekommt NaN. Zeile 1 bekommt Zeile 1 zugewiesen.
+df.loc[[0, 1]] = df.loc[[1, 2]].to_numpy()
+print(df)
+print()
+
+# wegen Alignment hat Spalte name keinen Partner und bekommt NaN.
+df.loc[:, ['name']] = df[['type']]
+print(df)
+print()
+
+# hier findet kein Alignment der Spalten statt, weil auf der rechten Seite nur eine pd.Series steht, es also keine Spalten zu alignen gibt
+df.loc[:, ['name']] = df['type']
+print(df)
+print()
+
+
 # %% Kategoriale Daten
 df['species']
 df['species'].info()
diff --git a/04-pandas-und-seaborn/old-faithful-1938.csv b/04-pandas-und-seaborn/old-faithful-1938.csv
new file mode 100644
index 0000000000000000000000000000000000000000..3ffc1d2406ef2f25408ae5d48b1f3b5b9c8fe858
--- /dev/null
+++ b/04-pandas-und-seaborn/old-faithful-1938.csv
@@ -0,0 +1,273 @@
+eruption_duration,waiting_time
+3.6,79
+1.8,54
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+4.083,70
+2.417,54
+4.183,86
+2.217,50
+4.45,90
+1.883,54
+1.85,54
+4.283,77
+3.95,79
+2.333,64
+4.15,75
+2.35,47
+4.933,86
+2.9,63
+4.583,85
+3.833,82
+2.083,57
+4.367,82
+2.133,67
+4.35,74
+2.2,54
+4.45,83
+3.567,73
+4.5,73
+4.15,88
+3.817,80
+3.917,71
+4.45,83
+2,56
+4.283,79
+4.767,78
+4.533,84
+1.85,58
+4.25,83
+1.983,43
+2.25,60
+4.75,75
+4.117,81
+2.15,46
+4.417,90
+1.817,46
+4.467,74
diff --git a/04-pandas-und-seaborn/old-faithful-1978.txt b/04-pandas-und-seaborn/old-faithful-1978.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cbb7267fdf3867ca8844458f1ef0d494c30f66c1
--- /dev/null
+++ b/04-pandas-und-seaborn/old-faithful-1978.txt
@@ -0,0 +1,108 @@
+ D  Y  X 
+ 1 78 4.4
+ 1 74 3.9
+ 1 68 4.0
+ 1 76 4.0
+ 1 80 3.5
+ 1 84 4.1
+ 1 50 2.3
+ 1 93 4.7
+ 1 55 1.7
+ 1 76 4.9
+ 1 58 1.7
+ 1 74 4.6
+ 1 75 3.4
+ 2 80 4.3
+ 2 56 1.7
+ 2 80 3.9
+ 2 69 3.7
+ 2 57 3.1
+ 2 90 4.0
+ 2 42 1.8
+ 2 91 4.1
+ 2 51 1.8
+ 2 79 3.2
+ 2 53 1.9
+ 2 82 4.6
+ 2 51 2.0
+ 3 76 4.5
+ 3 82 3.9
+ 3 84 4.3
+ 3 53 2.3
+ 3 86 3.8
+ 3 51 1.9
+ 3 85 4.6
+ 3 45 1.8
+ 3 88 4.7
+ 3 51 1.8
+ 3 80 4.6
+ 3 49 1.9
+ 3 82 3.5
+ 4 75 4.0
+ 4 73 3.7
+ 4 67 3.7
+ 4 68 4.3
+ 4 86 3.6
+ 4 72 3.8
+ 4 75 3.8
+ 4 75 3.8
+ 4 66 2.5
+ 4 84 4.5
+ 4 70 4.1
+ 4 79 3.7
+ 4 60 3.8
+ 4 86 3.4
+ 5 71 4.0
+ 5 67 2.3
+ 5 81 4.4
+ 5 76 4.1
+ 5 83 4.3
+ 5 76 3.3
+ 5 55 2.0
+ 5 73 4.3
+ 5 56 2.9
+ 5 83 4.6
+ 5 57 1.9
+ 5 71 3.6
+ 5 72 3.7
+ 5 77 3.7
+ 6 55 1.8
+ 6 75 4.6
+ 6 73 3.5
+ 6 70 4.0
+ 6 83 3.7
+ 6 50 1.7
+ 6 95 4.6
+ 6 51 1.7
+ 6 82 4.0
+ 6 54 1.8
+ 6 83 4.4
+ 6 51 1.9
+ 6 80 4.6
+ 6 78 2.9
+ 7 81 3.5
+ 7 53 2.0
+ 7 89 4.3
+ 7 44 1.8
+ 7 78 4.1
+ 7 61 1.8
+ 7 73 4.7
+ 7 75 4.2
+ 7 73 3.9
+ 7 76 4.3
+ 7 55 1.8
+ 7 86 4.5
+ 7 48 2.0
+ 8 77 4.2
+ 8 73 4.4
+ 8 70 4.1
+ 8 88 4.1
+ 8 75 4.0
+ 8 83 4.1
+ 8 61 2.7
+ 8 78 4.6
+ 8 61 1.9
+ 8 81 4.5
+ 8 51 2.0
+ 8 80 4.8
+ 8 79 4.1
\ No newline at end of file
diff --git a/04-pandas-und-seaborn/old-faithful-1985.csv b/04-pandas-und-seaborn/old-faithful-1985.csv
new file mode 100644
index 0000000000000000000000000000000000000000..93ac9d8f82aabb39fa41cd33092a98534ce41bc3
--- /dev/null
+++ b/04-pandas-und-seaborn/old-faithful-1985.csv
@@ -0,0 +1,300 @@
+waiting,duration
+80,4.0167
+71,2.15
+57,4
+80,4
+75,4
+77,2
+60,4.3833
+86,4.2833
+77,2.0333
+56,4.8333
+81,1.8333
+50,5.45
+89,1.6167
+54,4.8667
+90,4.3833
+73,1.7667
+60,4.6667
+83,2
+65,4.7333
+82,4.2167
+84,1.9
+54,4.9667
+85,2
+58,4
+79,2
+57,4
+88,2.8333
+68,4.5
+76,4.0667
+78,3.7167
+74,3.5167
+85,4.4667
+75,2.2167
+65,4.8833
+76,2.6
+58,4.15
+91,2.2
+50,4.7667
+87,1.8333
+48,4.6
+93,2.2667
+54,4.1333
+86,2
+53,4
+78,2
+52,4
+83,1.8833
+60,4.2667
+87,2.0833
+49,4.4667
+80,2.5
+60,4
+92,1.7667
+43,4.3333
+89,2.1833
+60,4.4833
+84,3.8833
+69,3.3333
+74,3.7333
+71,4
+108,1.95
+50,5.2667
+77,2
+57,4
+80,2
+61,4
+82,2
+48,4
+81,3.5333
+73,2.1667
+62,4.5
+79,2.0167
+54,4.15
+80,4.2
+73,4.3333
+81,1.9333
+62,4.65
+81,3.8167
+71,4.0333
+79,4.1667
+81,4.6667
+74,1.8167
+59,4
+81,3
+66,4
+87,2
+53,4.45
+80,2.05
+50,4.25
+87,1.9167
+51,4.6667
+82,1.7333
+58,4.3833
+81,1.7667
+49,4.6
+92,1.8667
+50,4.45
+88,1.6333
+62,5.0333
+93,1.8167
+56,5.1
+89,1.6333
+51,4.2833
+79,2
+58,4
+82,2
+52,4.5333
+88,2
+52,4
+78,2.9333
+69,4.7333
+75,3.9
+77,1.95
+53,4.1167
+80,1.8
+55,4.6667
+87,1.8333
+53,4.7
+85,2.1167
+61,4.7833
+93,1.8167
+54,4.1
+76,4.65
+80,4
+81,2
+59,4
+86,4
+78,4.2167
+71,4.1333
+77,3.9333
+76,3.75
+94,4.4167
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+83,3.8
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+72,3.8667
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+75,1.7
+65,4.9667
+79,4.2667
+72,4.5833
+78,4
+77,4
+79,4
+75,4
+78,1.9833
+64,4.6
+80,0.8333
+49,4.9167
+88,1.7333
+54,4.5833
+85,1.7
+51,4.75
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+50,4.5
+80,1.8667
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+81,4.45
+72,4
+75,4.8
+78,4
+87,4
+69,2
+55,4
+83,1.9333
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+82,2
+57,3.7
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+84,3.45
+73,4.3833
+78,1.8
+57,4.4
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+57,4.5167
+90,2.1
+62,4.35
+87,4.3667
+78,1.7833
+52,4.9167
+98,1.8167
+48,4
+78,4
+79,4
+65,3.8667
+84,1.85
+50,4.7
+83,2.0167
+60,4.4667
+80,1.8667
+50,4.1667
+88,1.9
+50,4.25
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+74,4.2167
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+49,4
+88,1.9667
+51,4.7667
+78,4
+85,2
+65,4
+75,4
+77,2.3833
+69,4.4167
+92,4.2167
+68,4.3667
+87,2
+61,4.45
+81,1.75
+55,4.5
+93,1.6167
+53,4.7
+84,2.5667
+70,3.7
+73,4.2333
+93,1.9333
+50,4.35
+87,4
+77,4
+74,4
+72,4.2167
+82,4
+74,4.1333
+80,1.8833
+49,4.4667
+91,1.95
+53,4.2167
+86,1.7167
+49,4.45
+79,4.25
+89,3.9667
+87,4.3833
+76,1.9667
+59,4.45
+80,4.2667
+89,1.9167
+45,4.4167
+93,3
+72,4
+71,2
+54,4
+79,3.2833
+74,1.8333
+65,4.6167
+78,1.8333
+57,4.6167
+87,4.6
+72,4.25
+84,1.9333
+47,4.9833
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+57,4.3
+87,4.2
+68,4.5333
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+75,4.6167
+73,2
+53,4
+82,4
+93,3.9167
+77,2
+54,4.5
+96,1.8
+48,4
+89,2.75
+63,4.7333
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+83,1.85
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+85,4
+78,4
+78,4
+81,4
+78,4
+76,4
+74,4
+81,2
+66,4
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+48,4.3333
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+87,2.1333
+52,4.0833
+85,2.0667
+58,4
+88,4
+79,2
diff --git a/04-pandas-und-seaborn/solutions/02-old-faithful-sol.ipynb b/04-pandas-und-seaborn/solutions/02-old-faithful-sol.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..777fc4c87f1e33bc96748cb0b0a1fd31aba882dc
--- /dev/null
+++ b/04-pandas-und-seaborn/solutions/02-old-faithful-sol.ipynb
@@ -0,0 +1,749 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Old Faithful Geysir\n",
+    "\n",
+    "![](attachment:old-faithful.jpg \"Gemälde des Old Faithful Geysir\")\n",
+    "\n",
+    "Der Old Faithful Geysir ist ein Geysir im Yellowstone-Nationalpark in\n",
+    "den USA. Er ist bekannt für seine regelmäßigen Ausbrüche, die alle 34\n",
+    "bis 125 Minuten auftreten und 1.5 bis 5 min andauern. Der Geysir kann\n",
+    "Wasser bis zu einer Höhe von 56 Metern ausstoßen.\n",
+    "\n",
+    "## a) Einlesen und einfache Statistik\n",
+    "\n",
+    "In der Datei `old-faithful-1938.csv` sind 272 aufeinanderfolgende Daten\n",
+    "von 1938 zu Eruptionen des Old Faithful Geysirs gespeichert. Die Spalten\n",
+    "sind:\n",
+    "\n",
+    "-   `eruption_duration`: Dauer des Ausbruchs in Minuten\n",
+    "-   `waiting_time`: Zeit bis zum nächsten Ausbruch in Minuten\n",
+    "\n",
+    "Lesen Sie die Daten mit Pandas ein und ermitteln Sie die kürzeste,\n",
+    "längste und mittlere Wartezeit bis zur nächsten Eruption.\n",
+    "\n",
+    "### Lösung zu a)\n",
+    "\n",
+    "Wir laden zunächst die Daten:"
+   ],
+   "attachments": {
+    "old-faithful.jpg": {
+     "image/jpeg": 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C1nZj31NnRYo4nBCDkDNRU7IpHQtFDcqzPt3DjHXgVxxco3sbNRe5T1HEQILD5ugBq6cb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\nlGpGo3f+vMupRTj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Z8ls5cAAY7ECuhVFYwcNdCeGzJBIG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TWlrehxX29IluzG0EaTttkjc5Mbjv8A7pwP0rnp13FNJaM0nBSd2zJ1VPs8UV5PGrLOhbZF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\n7c5x1HqK0qU4ySnEzhOUZ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+    }
+   },
+   "id": "0008-807056fbb7dc9180c1cef92d674cbb5d4cbfcaa19fc89cabde2b2821b8c"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "df1 = pd.read_csv('old-faithful-1938.csv')"
+   ],
+   "id": "0009-b55382a5d67a7d482fceb0474f9df51e36848047f1fd89b487ae10cdd03"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nun können wir die Werte mit `min`, `max` und `mean` ermitteln:"
+   ],
+   "id": "0010-0e9af1d0355bcff4f08204299d01a4f4d0a305729d374a1e85ab8e97d48"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [
+    "shortest = df1['waiting_time'].min()\n",
+    "longest = df1['waiting_time'].max()\n",
+    "average = df1['waiting_time'].mean()"
+   ],
+   "id": "0011-46bbabe4d35d2f1fe47dc7062500f23e6553edba827c1f11a9ed93e81d6"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Tests zu a)\n",
+    "\n",
+    "Hier eine Ausgabe mit den statischen Werten:"
+   ],
+   "id": "0013-a201a2eb23ee94cf852ac27a8e841d0555ed7620cd8a5851967c742baba"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Old Faithful bricht alle 43 bis 96 min und alle 70.8970588235294 min im Mittel aus."
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Old Faithful bricht alle\", shortest, \"bis\", longest, \"min und alle\", average, \"min im Mittel aus.\")"
+   ],
+   "id": "0014-9138aae2c7a798439e7af9c10aaea671af179a5f4494248d429f09101f0"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## b) Messdauer\n",
+    "\n",
+    "<p><img\n",
+    "src=\"data:image/svg+xml;base64,<?xml version="1.0" encoding="UTF-8"?>
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\" /></p>\n",
+    "\n",
+    "Die Messungen fanden alle nacheinander statt. Wenn Sie davon ausgehen,\n",
+    "dass die Wartezeit jeweils direkt bis zum nächste Ausbruch geht und nach\n",
+    "dem Ausbruch direkt die nächste Wartezeit beginnt, wie lange hat es\n",
+    "gedauert den gesamten Datensatz aufzunehmen? Geben Sie die Zeitdauer in\n",
+    "Tagen, Stunden und Minuten an.\n",
+    "\n",
+    "### Lösung zu b)\n",
+    "\n",
+    "Wir summieren zunächst alles auf. Das machen wir hier mit zwei Aufrufen\n",
+    "von `sum`. Der erste Aufruf summiert spaltenweise auf, also jeweils alle\n",
+    "Eruptionsdauern und alle Wartezeiten. Der zweite Aufruf addiert die\n",
+    "beiden Werte.\n",
+    "\n",
+    "Anschließend teilen wir die Summe durch 60 um die Zeit in Minuten zu\n",
+    "erhalten. Dann teilen wir durch 24 um die Zeit in Tagen zu erhalten. Da\n",
+    "wir nur ganze Tage zählen wollen (weil wir ja noch kleine Einheiten\n",
+    "haben), nehmen wir direkt die Integer-Division mittels `//`. Für die\n",
+    "Stunden bestimmen wir erst mittels `// 60` die ganzen Stunden und\n",
+    "ermitteln dann mit `% 24` die übrig gebliebenen Stunden, wenn man durch\n",
+    "24 teilen würde. Für die Minuten müssen wir die Restlichen Minuten\n",
+    "haben, die nicht in den Stunden enthalten sind.\n",
+    "\n",
+    "Zum Schluss fügen wir noch eine Probe hinzu, ob die Werte ungefähr (bis\n",
+    "auf 7 Nachkommastellen) gleich sind."
+   ],
+   "id": "0021-e2cf21287fd774120df13b7ccb89959512a5f8e6cd4e5c083b174637554"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [
+    "total_duration_min = df1.sum().sum()\n",
+    "\n",
+    "total_days = total_duration_min // (60 * 24)\n",
+    "total_hours = total_duration_min // 60 % 24\n",
+    "total_minutes = total_duration_min % 60\n",
+    "\n",
+    "np.testing.assert_almost_equal(total_duration_min, total_days * 60 * 24 + total_hours * 60 + total_minutes)"
+   ],
+   "id": "0022-8de0ea7526873d5e4bd97141b36dc9231fed3d09bfeb817b6c5021b9bbc"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nun geben wir die Werte aus:"
+   ],
+   "id": "0023-5c06a410812f3c83669b96e7f3d60a66cf144d3e8242a7f8b32dba0b906"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Die Messung dauerte 14.0 Tage, 1.0 Stunden und 12.67699999999968 Minuten."
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Die Messung dauerte\", total_days, \"Tage,\", total_hours, \"Stunden und\", total_minutes, \"Minuten.\")"
+   ],
+   "id": "0024-760c2dfab162bda32f34a5271a5a2635a7fa672bed6cda7bcc1a8825bf9"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## c) Differenz der Wartezeiten\n",
+    "\n",
+    "Berechnen Sie die Differenzen der Wartezeiten. Zum Beispiel beträgt die\n",
+    "erste Wartezeit 79 min und die zweite 54 min. Die Differenz ist also -25\n",
+    "min. Geben Sie die größte Abnahme und die größte Zunahme der Wartezeiten\n",
+    "als Absolutzahlen aus.\n",
+    "\n",
+    "Plotten Sie anschließend das Histogramm der Differenzen um zu sehen, ob\n",
+    "sich die Wartezeiten vielleicht nur langsam verändern oder langsame\n",
+    "Änderungen zumindest wahrscheinlicher als schnelle Änderungen sind.\n",
+    "\n",
+    "### Lösung zu c)\n",
+    "\n",
+    "Wir verwenden hier die Methode `diff` um die Differenzen zu berechnen.\n",
+    "Diese Methode gibt uns die Differenz des aktuellen Wertes mit dem\n",
+    "vorherigen Wert zurück. Das erste Element ist immer `NaN`, weil es\n",
+    "keinen vorherigen Wert gibt. Alternativ hätte man auch `np.diff` oder\n",
+    "`df1['waiting_time'].iloc[1:] - df1['waiting_time'].iloc[:-1].to_numpy()`\n",
+    "(Vorsicht: Ohne \\`to_numpy() tritt Alignment auf) verwenden können."
+   ],
+   "id": "0029-416a1d039c0c2ab3e9216049a939e3ac5e81cbb503c17db2f9d24fb8f51"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [
+    "diff = df1['waiting_time'].diff()\n",
+    "max_decrease = abs(diff.min())\n",
+    "max_increase = diff.max()"
+   ],
+   "id": "0030-5e54e3b321c98f109a79559471203fec83cb4ddd0fe78f9ad4094820cb9"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wir geben die größte Abnahme und die größte Zunahme der Wartezeiten aus:"
+   ],
+   "id": "0031-a8d9b30204e7d23cda4faf21aed3f18a4f3d119edae04f3dc950e68a845"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Die größte Abnahme der Wartezeiten beträgt 45.0 min und die größte zunahme 47.0 min."
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Die größte Abnahme der Wartezeiten beträgt\", max_decrease, \"min und die größte zunahme\", max_increase, \"min.\")"
+   ],
+   "id": "0032-d7b56d7413bf5af0c5995e70d3d20a689b96a25e117d3d60dd155714b18"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Da `diff` eine Pandas Series ist, können wir einfach die Methode `hist`\n",
+    "oder `plot.hist` aufrufen um das Histogramm zu plotten:"
+   ],
+   "id": "0033-89b9c6a0d707d92e5aa62c4d01d46b20b48df425cbc54abd7dde0e45f9e"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [
+    "diff.hist(bins=15)"
+   ],
+   "id": "0034-86a0bd19c284b2d3344bb726a5942fe1e0f3d428a3a8b08f733a07c1262"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bei so wenigen Daten ist die Interpretation des Plots nicht eindeutig.\n",
+    "Man sieht aber fast eine gleichmäßige Verteilung. Die Ränder kommen\n",
+    "weniger häufig vor, was aber auch klar ist, weil die Wartezeiten ja\n",
+    "einen kleinsten und größten Wert haben und nur von diesen die\n",
+    "größtmöglichen Differenzen kommen können, während die anderen\n",
+    "Differenzen auch von vielen anderen Werten kommen können. Allerdings\n",
+    "erkennt man keine klare Häufung um 0 herum. Also gibt es durchaus große\n",
+    "Sprünge in den Wartezeiten.\n",
+    "\n",
+    "## d) Modellierung\n",
+    "\n",
+    "Wenn man ein Modell erstellt, will man auch wissen, wie gut es ist.\n",
+    "Daher macht man oft ein triviales Modell, damit man einen Vergleichswert\n",
+    "hat. Als triviales Modell für die Wartezeit nehmen wir zunächst eine\n",
+    "konstante Vorhersage und zwar den Mittelwert der Wartezeiten. Wie hoch\n",
+    "ist der mittlere absolute Fehler (MAE) und die Wurzel aus dem mittleren\n",
+    "quadratischen Fehler (RMSE) für das triviale Modell? Berechnen Sie die\n",
+    "Werte ohne Schleifen.\n",
+    "\n",
+    "$$MAE = \\frac 1 n \\sum_{i=0}^{n-1} |y - \\hat y| = \\frac{1}{272} \\cdot (|54 - 70.9| + |74 - 70.9| + \\dots + |74 - 70.9|)$$\n",
+    "$$RMSE = \\sqrt{\\frac 1 n \\sum_{i=0}^{n-1} (y - \\hat y)^2}$$\n",
+    "\n",
+    "Dabei ist $\\hat y$ die Vorhersage, also zunächst die mittlere\n",
+    "Wartedauer."
+   ],
+   "id": "0039-3f6fa9a68f5102f22bcf39cf650fb1a099818131783672880cd19d606e3"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [],
+   "id": "0040-44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Als nächstes betrachten wir als Modell als nächste Wartezeit die\n",
+    "vorherige Wartezeit vorherzusagen. Wie hoch wären die Fehlerwerte dann?\n",
+    "Die Berechnung des MAE wäre also\n",
+    "\n",
+    "$$MAE = \\frac{1}{271} \\cdot (|54 - 79| + |74 - 54| + \\dots + |74 - 46|)$$"
+   ],
+   "id": "0042-1314ebc28c0c6337a876c6bd3e8c27e6be258572fb9e604a2eaca71f30b"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [],
+   "id": "0043-44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wenn man die Fehlerwerte verschiedener Modelle vergleicht, kriegt man\n",
+    "ein Gefühl dafür, wie gut ein Modell ist.\n",
+    "\n",
+    "Schauen Sie sich nun die Daten mit einem Pairplot mit Kernel Density\n",
+    "Estimation (KDE) Plots auf der Diagonalen (`diag_kind`) an (siehe\n",
+    "[Doku](https://seaborn.pydata.org/generated/seaborn.pairplot.html)).\n",
+    "\n",
+    "*Bonus: Versuchen Sie ein besseres Modell zu entwickeln! Sie dürfen auch\n",
+    "die aktuelle Eruptionsdauer verwenden. Denken Sie daran nur einen Teil\n",
+    "der Daten für das Training und einen Teil als Test zu verwenden. Streng\n",
+    "genommen hätten wir vorhin den Mittelwert auch nur auf der Trainingmenge\n",
+    "bilden und auf der Testmenge den Fehler ermitteln dürfen.*"
+   ],
+   "id": "0046-1e5c2934b1940cf84478bfd1fb46024b3cb859d8a2560b75aa40dad6944"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [],
+   "id": "0047-44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Lösung zu d)\n",
+    "\n",
+    "Den Mittelwert haben wir in `average` abgelegt und wir können ihn für\n",
+    "die Fehlerberechnung verwenden:"
+   ],
+   "id": "0049-4795a7dcddb40541fbeaae65108031f38bfa3a6627064153e12223b7d08"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [
+    "avg_err = (df1['waiting_time'] - average)\n",
+    "avg_mae = avg_err.abs().mean()\n",
+    "avg_rmse = np.sqrt((avg_err ** 2).mean())"
+   ],
+   "id": "0050-00c55c0abc106a9ad5ac0ac32e11aae24656f89b882f1d8582fa0469f38"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Mittelwertmodell.   MAE: 11.95, RMSE: 13.57"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'Mittelwertmodell.   MAE: {avg_mae:.2f}, RMSE: {avg_rmse:.2f}')"
+   ],
+   "id": "0051-4478840585df093490445e59c5ccf24eda25d0d3b27a51c145430efee69"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Für die Berechnung des Fehlers des Modells, das die letzte Wartezeit als\n",
+    "Vorhersage verwendet, können wir `diff` verwenden, wo ja schon die\n",
+    "Differenzen zur jeweils letzten Wartezeit berechnet sind:"
+   ],
+   "id": "0052-a044d32a03db5f17e9ff2d070681ae4f072c0b3637218db14e62e703e81"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [
+    "diff_mae = diff.abs().mean()\n",
+    "diff_rmse = np.sqrt((diff ** 2).mean())"
+   ],
+   "id": "0053-bd8c089f3966459bfa34576d28c9ad516465013f51e8efc48a8b5198e4d"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Letzte-Wert-Modell. MAE: 20.52, RMSE: 23.85"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'Letzte-Wert-Modell. MAE: {diff_mae:.2f}, RMSE: {diff_rmse:.2f}')"
+   ],
+   "id": "0054-db2c4202bfb4831517feefb877a6d812bf8dbf87198d83bf968f45183e8"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nun schauen wir uns den Pairplot an und erkennen, dass es einen\n",
+    "Zusammenhang zwischen der Eruptionsdauer und der Wartezeit gibt:"
+   ],
+   "id": "0055-be44853a9b6976808192db735ad24190d023eb9e284633973adc0d2afb6"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [
+    "sns.pairplot(df1, diag_kind='kde', plot_kws=dict(alpha=0.5))"
+   ],
+   "id": "0056-7fca65c15a5eee1266e38df1f400bb0699906b618fcfb1262cde78d893c"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Es ist also naheliegend ein Modell zu bauen, dass die Wartezeit aus der\n",
+    "aktuellen Eruption vorhersagt. Dazu nehmen wir einfach eine lineare\n",
+    "Regression eins Polynom ersten Grades:"
+   ],
+   "id": "0057-563be423d1d87141d2abb93a055369c6c982d0b0860e3aa7ce27b2ba1a7"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [
+    "x_train = df1['eruption_duration'].iloc[:200]\n",
+    "y_train = df1['waiting_time'].iloc[:200]\n",
+    "x_test = df1['eruption_duration'].iloc[200:]\n",
+    "y_test = df1['waiting_time'].iloc[200:]\n",
+    "\n",
+    "p = np.polynomial.Polynomial.fit(x_train, y_train, deg=1).convert()"
+   ],
+   "id": "0058-4f11d068e818a45992ff1d2f9655db761f4a49d930796085e9d3f384f63"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Den Fehler ermitteln wir jetzt aus der Vorhersage der Testdaten:"
+   ],
+   "id": "0059-4dc58f257323c22bd484e7af4083ecf6212d54910425984d6aab554ffee"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [
+    "y_pred = p(x_test)\n",
+    "\n",
+    "linreg_diff = y_test - y_pred\n",
+    "linreg_mae = linreg_diff.abs().mean()\n",
+    "linreg_rmse = np.sqrt((linreg_diff ** 2).mean())"
+   ],
+   "id": "0060-4dd7b0cfa418382f44a529d35c009cf52cf1c18d11752718d4c8e0687ad"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Regressionsmodell.  MAE: 4.69, RMSE: 5.82"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'Regressionsmodell.  MAE: {linreg_mae:.2f}, RMSE: {linreg_rmse:.2f}')"
+   ],
+   "id": "0061-9b8d038b883d47e0bc123b28e34a70f0569a2c93439365e2c45092df31c"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mit etwas Mühe wären hier vermutlich noch Verbesserungen möglich.\n",
+    "\n",
+    "## e) Weitere Datensätze\n",
+    "\n",
+    "Es gibt noch weitere Datensätze aus anderen Jahren zum Old Faithful\n",
+    "Geysir. Lesen Sie diese ein und bringen Sie sie in die Form des\n",
+    "Datensatzes von 1938, sodass sie gemeinsam geplottet werden können.\n",
+    "Starten Sie am besten mit einem Scatter-Plot des Datensatzes von 1938,\n",
+    "in dem auf der x-Achse die `eruption_duration` und auf der y-Achse die\n",
+    "`waiting_time` liegen soll.\n",
+    "\n",
+    "`old-faithful-1978.txt`:\n",
+    "\n",
+    "-   Die Daten stammen vom 1. August bis zum 8. August, 1978. Der Tag\n",
+    "    steht in der ersten Spalte.\n",
+    "-   Es ist nicht klar, welche Eruptionen aufeinanderfolgend sind.\n",
+    "-   Das Format ist nicht wirklich CSV, da es mit Leerzeichen gefüllt\n",
+    "    ist, sodass sich Spalten fester breite ergeben. Verwenden Sie\n",
+    "    [`pd.read_fwf`](https://pandas.pydata.org/docs/reference/api/pandas.read_fwf.html)\n",
+    "    anstatt `pd.read_csv` um die Datei einzulesen.\n",
+    "-   Ändern Sie die Spaltennamen der entsprechenden Spalten zu\n",
+    "    `eruption_duration` und `waiting_time`.\n",
+    "\n",
+    "Plotten Sie die Daten in den bestehenden Scatter Plot.\n",
+    "\n",
+    "`old-faithful-1985.csv`:\n",
+    "\n",
+    "-   Die Daten stammen vom 1. August bis zum 15. August, 1985.\n",
+    "-   Es ist eine kontinuierliche Messung.\n",
+    "-   Einige nächtliche Eruptionsdauermesswerte wurden nur als *kurz*,\n",
+    "    *mittel* oder *lang* eingetragen. Diese Werte sind wurden auf 2, 3\n",
+    "    bzw. 4 Minuten geschätzt.\n",
+    "-   Die Spalte `waiting` gibt die Wartezeit **auf** diese Eruption an.\n",
+    "    Sie müssen hier vorverarbeiten, damit dieser Datensatz zu den\n",
+    "    anderen passt.\n",
+    "\n",
+    "Plotten Sie auch diesen Datensatz gemeinsam mit den anderen.\n",
+    "\n",
+    "`old-faithful-2018.csv`:\n",
+    "\n",
+    "-   Die Daten wurden von freiwilligen im Rahmen eines Citizen Science\n",
+    "    Projekts aufgezeichnet.\n",
+    "-   Es können Fehler enthalten sein. Filtern Sie ggf. offensichtliche\n",
+    "    Ausreißer weg.\n",
+    "-   Die Zeiteinheiten sind jeweils Sekunden.\n",
+    "\n",
+    "Wenn Sie den Datensatz hinzugefügt haben, schauen Sie nach\n",
+    "Auffälligkeiten. Sind Anpassungen im Modell notwendig?"
+   ],
+   "id": "0073-ca96affb40213b63dd4c7c0149650711a1676067611b985280b5bc26ade"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [],
+   "id": "0074-44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Lösung zu e)\n",
+    "\n",
+    "Hier in der Lösung wird nur der Plot mit allen Datensätzen gezeigt.\n",
+    "\n",
+    "Aber zunächst lesen wir den Datensatz von 1978 ein. Hier die ersten\n",
+    "Zeilen in der Datei:\n",
+    "\n",
+    "     D  Y  X\n",
+    "     1 78 4.4\n",
+    "     1 74 3.9\n",
+    "     1 68 4.0\n",
+    "\n",
+    "Wir verwerfen die Spalte `D` (Day). Von den Werten erkennen wir, dass\n",
+    "`X` der `eruption_duration` entspricht und `Y` der `waiting_time`.\n",
+    "Dementsprechend setzen wir die Spaltennamen."
+   ],
+   "id": "0079-2ae859c1cf9c4b41739525347611ec719266fe284422493bea23d2cbd2f"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [
+    "df2 = pd.read_fwf('old-faithful-1978.txt')[['X', 'Y']]\n",
+    "df2.columns = ['eruption_duration', 'waiting_time']"
+   ],
+   "id": "0080-228608610beda35fe30f3e721d40f09fac21bdb1298bc38c0f770a0500b"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Die Datei für 1985 sieht so aus:\n",
+    "\n",
+    "    waiting,duration\n",
+    "    80,4.0167\n",
+    "    71,2.15\n",
+    "    57,4\n",
+    "\n",
+    "Die Spaltennamen müssen wieder geändert werden. Zusätzlich haben wir\n",
+    "aber die Information, dass `waiting` die Wartezeit vor einer Eruption\n",
+    "ist. Da es sich aber um eine kontinuierliche Messung handelt, können wir\n",
+    "das leicht durch Verschiebung der Spalte um eine Zeile nach oben lösen.\n",
+    "Um Alignment zu vermeiden, verwenden wir `to_numpy`. Die letzte Zeile\n",
+    "verwerfen wir, weil wir dort keine Wartezeit nach der Eruption kennen."
+   ],
+   "id": "0083-915c7798bfb8a99d08209a0de5c08722c3d20ce1d02f31d9ae9367d7e8a"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [
+    "df3 = pd.read_csv('old-faithful-1985.csv')\n",
+    "df3.columns = ['waiting_time', 'eruption_duration']\n",
+    "df3.iloc[:-1, 0] = df3.iloc[1:, 0].to_numpy()\n",
+    "df3 = df3.iloc[:-1]"
+   ],
+   "id": "0084-80bbe335df8442a6886304ac6ae394e9d385f11b14bbdfdbadce88af351"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Die Datei für 2018 enthält jeweils einen Zeitstempel und die Daten sind\n",
+    "in Sekunden:\n",
+    "\n",
+    "    time,duration,waiting\n",
+    "    2018-01-01 00:08:00,236,5520\n",
+    "    2018-01-02 22:55:00,236,5460\n",
+    "    2018-01-06 00:18:00,200,5700\n",
+    "\n",
+    "Es gibt einen offensichtlichen fehlerhaften Datensatz mit einer\n",
+    "Eruptionsdauer von 1 sec. Das sieht man spätestens im Plot. Weiterhin\n",
+    "nennen wir die Spalten wieder um und teilen durch 60 um auf Minuten zu\n",
+    "kommen."
+   ],
+   "id": "0087-994b767d70b2cb783ce9eea954d232f876c3a69b35e7bc6e72c0c66b508"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [
+    "df4 = pd.read_csv('old-faithful-2018.csv')[['duration', 'waiting']]\n",
+    "df4.columns = ['eruption_duration', 'waiting_time']\n",
+    "df4 = df4[df4['eruption_duration'] < 10]  # filter eruption_duration with less than 10 sec\n",
+    "df4 /= 60"
+   ],
+   "id": "0088-7f366659d82dd25a0c2da7bd49082739b67fb68d317f79c3a87b3df4179"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Erstellen wir nun den Plot mit verschiedenen Farben und Labels:"
+   ],
+   "id": "0089-a4e4f1b9ac428aeeb01e0369bdc9a2b7a9198d0842fd2255d8ff16c8813"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [
+    "ax = df1.plot.scatter(x='eruption_duration', y='waiting_time', c='b', label='1938', alpha=0.7)\n",
+    "df2.plot.scatter(x='eruption_duration', y='waiting_time', c='r', label='1978', ax=ax, alpha=0.5)\n",
+    "df3.plot.scatter(x='eruption_duration', y='waiting_time', c='g', label='1985', ax=ax, alpha=0.5)\n",
+    "df4.plot.scatter(x='eruption_duration', y='waiting_time', c='y', label='2018', ax=ax, alpha=0.4)"
+   ],
+   "id": "0090-98c722f31a92a07eaac042dc857d266c5463cde6978d860b60f07594588"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bei dem Datensatz von 2018 fällt auf, dass die Wartezeiten sich erhöht\n",
+    "haben und die kurzen Eruptionsdauern mit weniger als drei Minuten kaum\n",
+    "noch vorkommen. Die Verteilung der langen Eruptionsdauern hat sich etwas\n",
+    "stärker um vier Minuten konzentriert. Es gibt Untersuchungen, die\n",
+    "vermuten, dass ein lokales Erdbeben 1998 diese Veränderung ausgelöst\n",
+    "hat. Bis 2008 gab es noch deutlich weniger Eruptionsdauern mit weniger\n",
+    "als drei Minuten."
+   ],
+   "id": "0091-545c2250c3e26b244c3612b96d0a7a4a5dd479e9d62fe937605e56ef4e4"
+  }
+ ],
+ "nbformat": 4,
+ "nbformat_minor": 5,
+ "metadata": {}
+}
diff --git a/04-pandas-und-seaborn/solutions/folien-code/folien-code.ipynb b/04-pandas-und-seaborn/solutions/folien-code/folien-code.ipynb
index 888a25d0560572dc01899315ccf48858bf2378b9..27ac9949c8f524a2b93ef3467203f318bbe2c9e1 100644
--- a/04-pandas-und-seaborn/solutions/folien-code/folien-code.ipynb
+++ b/04-pandas-und-seaborn/solutions/folien-code/folien-code.ipynb
@@ -17,7 +17,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% import Pandas\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "from IPython.display import display\n",
@@ -30,7 +29,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Iris Flower Dataset\n",
     "url = 'https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv'\n",
     "df = pd.read_csv(url)\n",
     "\n",
@@ -51,7 +49,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Informationen\n",
     "print(df.shape)\n",
     "print(df.columns)\n",
     "print(df.dtypes)\n",
@@ -66,7 +63,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Statistischer Überblick\n",
     "display(df.describe())\n",
     "display(df.describe(exclude='number'))\n",
     "\n"
@@ -78,7 +74,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Kuchendiagramm\n",
     "counts = df['species'].value_counts()\n",
     "display(counts)\n",
     "\n",
@@ -93,7 +88,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Boxplot\n",
     "df.boxplot(column='petal_length', by='species')\n",
     "\n"
    ]
@@ -104,7 +98,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Boxplots aller Features\n",
     "fig, axs = plt.subplots(2, 2, sharey=False)    # y-Achsen unabhängig\n",
     "pd.plotting.boxplot(df, by='species', ax=axs)  # übergebe axs\n",
     "[ax.set_xlabel('') for ax in axs.ravel()]      # entferne x-Labels\n",
@@ -117,7 +110,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Violinenplot\n",
     "import seaborn as sns\n",
     "sns.violinplot(hue='species', y='petal_length', data=df)\n",
     "\n"
@@ -129,7 +121,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Scatterplots\n",
     "df.plot.scatter(x='petal_length', y='petal_width', c='species', colormap='viridis', alpha=0.7)\n",
     "\n"
    ]
@@ -140,7 +131,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Pair Plot\n",
     "sns.pairplot(df, hue='species', plot_kws={'alpha': 0.5})\n",
     "\n"
    ]
@@ -151,7 +141,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Parallele Koordinaten Plot, unskaliert\n",
     "pd.plotting.parallel_coordinates(df, 'species', colormap='viridis', alpha=.5)\n",
     "\n"
    ]
@@ -162,7 +151,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Parallele Koordinaten Plot, normiert\n",
     "from sklearn.preprocessing import minmax_scale\n",
     "num_cols = df.columns.drop('species')\n",
     "df_scaled = df.copy()\n",
@@ -176,7 +164,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Parallele Koordinaten Plot, custom Code from https://stackoverflow.com/a/60401570/2414411\n",
     "import numpy as np\n",
     "from matplotlib.path import Path\n",
     "import matplotlib.patches as patches\n",
@@ -244,7 +231,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Parallele Koordinaten Plot mit Plotly Express\n",
     "import plotly.express as px\n",
     "# fig = px.parallel_coordinates(df, color=\"species\", labels={'species': tuple('ABC')})\n",
     "fig = px.parallel_coordinates(df, color=df[\"species\"].cat.codes)\n",
@@ -258,7 +244,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Slicing\n",
     "cp = df.copy()\n",
     "cp.loc[1, 'sepal_width'] = 1\n",
     "cp.loc[0:2, 'petal_length'] = 2\n",
@@ -275,11 +260,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% komplexe Indizierung\n",
     "display(df.loc[[0, 149, 2], 'petal_width'])\n",
     "\n",
     "part = df.loc[[0, 149, 2], ['petal_width', 'sepal_width']]\n",
-    "part\n",
+    "display(part)\n",
     "\n"
    ]
   },
@@ -289,7 +273,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% integer location\n",
     "display(part.iloc[1, -1])\n",
     "display(part.iloc[:2, -1])\n",
     "display(part.iloc[[0, 1], [0, 1]])\n",
@@ -302,7 +285,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% boolesche Indizierung\n",
     "pw = part.loc[:, 'petal_width'] <= 1\n",
     "sw = part.loc[:, 'sepal_width'] < 3.5\n",
     "display(pw)\n",
@@ -319,7 +301,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Daten fallen lassen\n",
     "display(part.drop(index=149, columns='petal_width'))\n",
     "display(part.drop(index=[149, 0]))\n",
     "\n"
@@ -331,7 +312,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% einzelne Daten hinzufügen\n",
     "part.loc[3] = [2, 6]\n",
     "display(part)\n",
     "part.loc[:, 'weight'] = [1, 2, 3, 4]\n",
@@ -345,7 +325,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% DataFrames zusammenführen\n",
     "a = part.drop(index=3)\n",
     "b = df.loc[:2, ['petal_length', 'petal_width']]\n",
     "display(a)\n",
@@ -361,7 +340,62 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Kategoriale Daten\n",
+    "df = pd.DataFrame({'name': ['Paul', 'John', 'Bill'], 'type': ['student', 'student', 'teacher']})\n",
+    "# kein Effekt, denn name wird name zugewiesen und type wird type zugewiesen\n",
+    "df.loc[:, ['name', 'type']] = df.loc[:, ['type', 'name']]\n",
+    "print(df)\n",
+    "print()\n",
+    "\n",
+    "# kein Effekt, denn name wird name zugewiesen und type wird type zugewiesen\n",
+    "df.loc[:, ['name', 'type']] = df[['type', 'name']]\n",
+    "print(df)\n",
+    "print()\n",
+    "\n",
+    "# tauscht Werte, weil __index__ (also []) kein Alignment hat\n",
+    "df[['name', 'type']] = df[['type', 'name']]\n",
+    "print(df)\n",
+    "print()\n",
+    "\n",
+    "# tauscht Werte, weil auf der rechten Seite ein Numpy-Array steht.\n",
+    "df.loc[:, ['name', 'type']] = df[['type', 'name']].to_numpy()\n",
+    "print(df)\n",
+    "print()\n",
+    "\n",
+    "# spaltenweise Zuweisung (ohne extra Klammern) ergibt Series, also werden keine Spalten aligned\n",
+    "temp = df['name'].copy()        # kopiere die Werte, damit sie im nächsten Schritt nicht überschrieben werden\n",
+    "df.loc[:, 'name'] = df['type']  # tauscht Werte, weil es hier keine Spalten zu alignen gibt (sondern nur Zeilen, die aber zueinander passen) ...\n",
+    "df.loc[:, 'type'] = temp        # ... auf der rechten und linken Seite steht jeweils eine pd.Series\n",
+    "print(df)\n",
+    "print()\n",
+    "\n",
+    "# wegen Alignment hat Zeile 0 keinen Partner und bekommt NaN. Zeile 1 bekommt Zeile 1 zugewiesen.\n",
+    "df.loc[[0]] = df.loc[[1, 2]]\n",
+    "print(df)\n",
+    "print()\n",
+    "\n",
+    "# wegen Alignment hat Zeile 0 keinen Partner und bekommt NaN. Zeile 1 bekommt Zeile 1 zugewiesen.\n",
+    "df.loc[[0, 1]] = df.loc[[1, 2]].to_numpy()\n",
+    "print(df)\n",
+    "print()\n",
+    "\n",
+    "# wegen Alignment hat Spalte name keinen Partner und bekommt NaN.\n",
+    "df.loc[:, ['name']] = df[['type']]\n",
+    "print(df)\n",
+    "print()\n",
+    "\n",
+    "# hier findet kein Alignment der Spalten statt, weil auf der rechten Seite nur eine pd.Series steht, es also keine Spalten zu alignen gibt\n",
+    "df.loc[:, ['name']] = df['type']\n",
+    "print(df)\n",
+    "print()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "df['species']\n",
     "df['species'].info()\n",
     "\n"
@@ -373,7 +407,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Statistische Funktionen\n",
     "X = df.drop(columns='species')\n",
     "y = df['species']\n",
     "\n",
@@ -388,7 +421,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% Gruppierung\n",
     "species_means = X.groupby(y).mean()\n",
     "display(species_means)\n",
     "\n",
diff --git a/04-pandas-und-seaborn/solutions/folien-code/folien-code.py b/04-pandas-und-seaborn/solutions/folien-code/folien-code.py
index b119bfe93b03e2ee6c765420a5ac0268ee968cda..d3229c908ad4562c94380a5b0de12d1f6dfcac60 100644
--- a/04-pandas-und-seaborn/solutions/folien-code/folien-code.py
+++ b/04-pandas-und-seaborn/solutions/folien-code/folien-code.py
@@ -160,7 +160,7 @@ cp
 display(df.loc[[0, 149, 2], 'petal_width'])
 
 part = df.loc[[0, 149, 2], ['petal_width', 'sepal_width']]
-part
+display(part)
 
 
 # %% integer location
@@ -200,6 +200,56 @@ display(pd.concat((a, b), axis='columns'))
 display(pd.concat((a, b), axis='index'))
 
 
+# %% Alignment
+df = pd.DataFrame({'name': ['Paul', 'John', 'Bill'], 'type': ['student', 'student', 'teacher']})
+# kein Effekt, denn name wird name zugewiesen und type wird type zugewiesen
+df.loc[:, ['name', 'type']] = df.loc[:, ['type', 'name']]
+print(df)
+print()
+
+# kein Effekt, denn name wird name zugewiesen und type wird type zugewiesen
+df.loc[:, ['name', 'type']] = df[['type', 'name']]
+print(df)
+print()
+
+# tauscht Werte, weil __index__ (also []) kein Alignment hat
+df[['name', 'type']] = df[['type', 'name']]
+print(df)
+print()
+
+# tauscht Werte, weil auf der rechten Seite ein Numpy-Array steht.
+df.loc[:, ['name', 'type']] = df[['type', 'name']].to_numpy()
+print(df)
+print()
+
+# spaltenweise Zuweisung (ohne extra Klammern) ergibt Series, also werden keine Spalten aligned
+temp = df['name'].copy()        # kopiere die Werte, damit sie im nächsten Schritt nicht überschrieben werden
+df.loc[:, 'name'] = df['type']  # tauscht Werte, weil es hier keine Spalten zu alignen gibt (sondern nur Zeilen, die aber zueinander passen) ...
+df.loc[:, 'type'] = temp        # ... auf der rechten und linken Seite steht jeweils eine pd.Series
+print(df)
+print()
+
+# wegen Alignment hat Zeile 0 keinen Partner und bekommt NaN. Zeile 1 bekommt Zeile 1 zugewiesen.
+df.loc[[0]] = df.loc[[1, 2]]
+print(df)
+print()
+
+# wegen Alignment hat Zeile 0 keinen Partner und bekommt NaN. Zeile 1 bekommt Zeile 1 zugewiesen.
+df.loc[[0, 1]] = df.loc[[1, 2]].to_numpy()
+print(df)
+print()
+
+# wegen Alignment hat Spalte name keinen Partner und bekommt NaN.
+df.loc[:, ['name']] = df[['type']]
+print(df)
+print()
+
+# hier findet kein Alignment der Spalten statt, weil auf der rechten Seite nur eine pd.Series steht, es also keine Spalten zu alignen gibt
+df.loc[:, ['name']] = df['type']
+print(df)
+print()
+
+
 # %% Kategoriale Daten
 df['species']
 df['species'].info()
diff --git a/04-pandas-und-seaborn/solutions/old-faithful-1938.csv b/04-pandas-und-seaborn/solutions/old-faithful-1938.csv
new file mode 100644
index 0000000000000000000000000000000000000000..3ffc1d2406ef2f25408ae5d48b1f3b5b9c8fe858
--- /dev/null
+++ b/04-pandas-und-seaborn/solutions/old-faithful-1938.csv
@@ -0,0 +1,273 @@
+eruption_duration,waiting_time
+3.6,79
+1.8,54
+3.333,74
+2.283,62
+4.533,85
+2.883,55
+4.7,88
+3.6,85
+1.95,51
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+4.3,73
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+4.033,80
+3.833,74
+2.017,52
+1.867,48
+4.833,80
+1.833,59
+4.783,90
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diff --git a/04-pandas-und-seaborn/solutions/old-faithful-1978.txt b/04-pandas-und-seaborn/solutions/old-faithful-1978.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cbb7267fdf3867ca8844458f1ef0d494c30f66c1
--- /dev/null
+++ b/04-pandas-und-seaborn/solutions/old-faithful-1978.txt
@@ -0,0 +1,108 @@
+ D  Y  X 
+ 1 78 4.4
+ 1 74 3.9
+ 1 68 4.0
+ 1 76 4.0
+ 1 80 3.5
+ 1 84 4.1
+ 1 50 2.3
+ 1 93 4.7
+ 1 55 1.7
+ 1 76 4.9
+ 1 58 1.7
+ 1 74 4.6
+ 1 75 3.4
+ 2 80 4.3
+ 2 56 1.7
+ 2 80 3.9
+ 2 69 3.7
+ 2 57 3.1
+ 2 90 4.0
+ 2 42 1.8
+ 2 91 4.1
+ 2 51 1.8
+ 2 79 3.2
+ 2 53 1.9
+ 2 82 4.6
+ 2 51 2.0
+ 3 76 4.5
+ 3 82 3.9
+ 3 84 4.3
+ 3 53 2.3
+ 3 86 3.8
+ 3 51 1.9
+ 3 85 4.6
+ 3 45 1.8
+ 3 88 4.7
+ 3 51 1.8
+ 3 80 4.6
+ 3 49 1.9
+ 3 82 3.5
+ 4 75 4.0
+ 4 73 3.7
+ 4 67 3.7
+ 4 68 4.3
+ 4 86 3.6
+ 4 72 3.8
+ 4 75 3.8
+ 4 75 3.8
+ 4 66 2.5
+ 4 84 4.5
+ 4 70 4.1
+ 4 79 3.7
+ 4 60 3.8
+ 4 86 3.4
+ 5 71 4.0
+ 5 67 2.3
+ 5 81 4.4
+ 5 76 4.1
+ 5 83 4.3
+ 5 76 3.3
+ 5 55 2.0
+ 5 73 4.3
+ 5 56 2.9
+ 5 83 4.6
+ 5 57 1.9
+ 5 71 3.6
+ 5 72 3.7
+ 5 77 3.7
+ 6 55 1.8
+ 6 75 4.6
+ 6 73 3.5
+ 6 70 4.0
+ 6 83 3.7
+ 6 50 1.7
+ 6 95 4.6
+ 6 51 1.7
+ 6 82 4.0
+ 6 54 1.8
+ 6 83 4.4
+ 6 51 1.9
+ 6 80 4.6
+ 6 78 2.9
+ 7 81 3.5
+ 7 53 2.0
+ 7 89 4.3
+ 7 44 1.8
+ 7 78 4.1
+ 7 61 1.8
+ 7 73 4.7
+ 7 75 4.2
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\ No newline at end of file
diff --git a/04-pandas-und-seaborn/solutions/old-faithful-1985.csv b/04-pandas-und-seaborn/solutions/old-faithful-1985.csv
new file mode 100644
index 0000000000000000000000000000000000000000..93ac9d8f82aabb39fa41cd33092a98534ce41bc3
--- /dev/null
+++ b/04-pandas-und-seaborn/solutions/old-faithful-1985.csv
@@ -0,0 +1,300 @@
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