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{
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"source": [
"# 4. Programmierübung: Matplotlib\n",
"\n",
"<div style=\"display:flex;\">\n",
" <div style=\"text-align: left\">\n",
" Willkommen zur vierten Programmierübung Einführung in Python 3.\n",
" </div>\n",
" <img style=\"float: right; margin: 0px 15px 15px 0px\" src=\"https://www.python.org/static/img/python-logo-large.c36dccadd999.png?1576869008\" width=\"100\" />\n",
"</div>\n",
"\n",
"Wenn Sie Fragen oder Verbesserungsvorschläge zum Inhalt oder Struktur der Notebooks haben, dann können sie eine E-Mail an Phil Keier ([p.keier@hbk-bs.de](mailto:p.keier@hbk-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) oder Martin Le ([martin.le@tu-bs.de](mailto:martin.le@tu-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) schreiben.\n",
"\n",
"Link zu einem Python Spickzettel: [hier](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf)\n",
"\n",
"Der Großteil des Python-Tutorials stammt aus der Veranstaltung _Deep Learning Lab_ und von [www.python-kurs.eu](https://www.python-kurs.eu/python3_kurs.php) und wurde für _Signale und Systeme_, sowie _Einführung in die Programmierung für Nicht Informatiker_ angepasst.\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "d6320c43-281a-4c81-86ca-ded1f2d57f19",
"metadata": {
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"source": [
"# Was ist Matplotlib\n",
"\n",
"Matplotlib ist eine Python Bibliothek zum (interaktiven) Visualisieren von Daten. Die Bibliothek intergiert sich super mit anderen viel Benutzten Python Bibliotheken wie NumPy. Der Vorteil in Kombination mit Jupyter besteht in der direkten Ausgabe eines Plots auf dem Bildschirm.\n",
"\n",
"__Nutzen Sie für diese Aufgabe gerne die [Matplotlib Reference](https://matplotlib.org/stable/users/index.html)__\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "994c8d87-9f58-4915-9eb5-f55e16d31487",
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"source": [
"# Import & Jupyter Magick\n",
"\n",
"Um Matplotlib zu verwenden müssen wir diese importieren. Dabei ist das Objekt `pyplot` der Dreh und Angelpunkt der ganzen Magie. Wie auch bei NumPy hat das Internet ein ungeschriebenes Gesetz, dass die Abkürzung von `matplotlib.pyplot` `plt` heißt. \n",
"\n",
"Importieren wir im ersten Schritt matplotlib als plt:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4e07b9b9-adab-46d6-97bd-c9fce64f4280",
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"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
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"id": "cf8480a8-a92b-401f-90fb-a92d4d8cb33d",
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"source": [
"Jupyter hat magische Parameter die von den Bibliotheken verwendet werden können um gewissen Einstellungen im Hintergrund zu treffen. Daher brauchen wir in nächster Zeile genau dieses \"magische\" Statement. \n",
"\n",
"Falls Sie dazu mehr Wissen wollen Lesen Sie den Eintrag zu [Rich Outputs](https://ipython.readthedocs.io/en/stable/interactive/plotting.html) in der IPython Dokumentation."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "27f26d6b-2b3c-4c60-b878-b728d2467b1f",
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"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"id": "58332e87-d29d-4307-92de-bc5d70ac6419",
"metadata": {
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"source": [
"Im Allgemeinen steht folgendes Import Statement in jedem Jupyter Notebook das im entferntesten etwas mit Datenanalyse zu tun hat. Achtung in nächster Zelle wird NumPy gleich mit importiert."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f8d54007-5094-4d34-b5ce-11a62d367f53",
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"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"%matplotlib inline"
]
},
{
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"id": "e7b78221-3568-45c5-964f-422b2668f4e5",
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2024-11-14 19:06:50 +01:00
"jp-MarkdownHeadingCollapsed": true,
2024-10-25 13:28:49 +02:00
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"source": [
"# First plot\n",
"\n",
"Wie dem [Getting Started](https://matplotlib.org/stable/users/getting_started/index.html#getting-started) Beispiel zu entnehmen, wollen wir einmal die Sinus Funktion plotten.\n",
"\n",
"Dazu brauchen wir zwei Attribute:\n",
"1. Die x-Skala - Dies kann die Länge eines Datensets sein, oder ein allegemeiner Linespace. Aufjedenfall eine Liste bzw. Array.\n",
"2. Die y-Skala - Im Allgemeinen die Werte eines zu plottenden Datensets. Aufjedenfall auch eine Liste bzw. Array.\n",
"\n",
"Plotten wir im Folgenden die Sinus Funktion. Eine der schönen Eigenschaften der Sinus Funktion ist, dass diese sich nach dem Intervall $[0...2\\pi]$ wiederholt. Daher enthält die x-Skala einen linespace von $[0...2\\pi]$. Als Wert für $\\pi$ wird die NumPy Konstante [np.pi](https://numpy.org/doc/stable/reference/constants.html#numpy.pi) verwendet.\n",
"\n",
"Auf der y-Skala plotten wir im folgenden das zuvor berechnete Array mit den Sinus Werten. Die Hierfür verwendete Funktion ist [np.sin](https://numpy.org/doc/stable/reference/generated/numpy.sin.html). \n",
"\n",
"Das `plt` Objekt hat mehrere Funktionen die in einer bestimmten Reihenfolge aufgerufen werden müssen. Dabei können zuerst mehrere plots mit [plt.plot](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html) definiert werden. Zum Schluss wird zur Ausgabe [plt.show](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html) aufgerufen."
]
},
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"id": "72fa4224-095a-4d6a-9a9f-e1c136a79115",
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.linspace(0, 2*np.pi, num=200) # Definiere einen linearen Bereich von 0 bis 2pi\n",
"y = np.sin(x) # Berechne den Sinus mit den Werten von x\n",
"\n",
"plt.plot(x, y) # Setze für die X-Achse x und für die Y-Achse y\n",
"plt.show() # Zeige den Plot"
]
},
{
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"id": "b67896b4-8a1c-4c3f-92c7-7459f95916e0",
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"source": [
"# Zwei Funktionen \n",
"\n",
"Wie bereits zuvor erwähnt lässt sich `plt.plot` mehr als einmal aufrufen. Wollen wir im folgenden den Kosinus mittels [np.cos](https://numpy.org/doc/stable/reference/generated/numpy.cos.html) dazu plotten. Dafür werden die Werte aus dem bereits definierten x wiederverwendet. Die Variabelen `y1 = np.sin(x)` & `y2 = np.cos(x)` enthälten die jeweiligen y werte. "
]
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{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"y1 = np.sin(x) # Sinus Werte mittels x berechnen\n",
"y2 = np.cos(x) # Kosinus Werte mittels x berechnen\n",
"\n",
"plt.plot(x, y1) # Plotte den Sinus\n",
"plt.plot(x, y2) # Plotte den Kosinus\n",
"\n",
"plt.show() # Zeige das Diagramm"
]
},
{
"cell_type": "markdown",
"id": "caf293fb-ee33-48d1-85f1-82cc3afca0cf",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-d1d50ca1d203ac29",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"## Aufgabe - Erster eigener Plot Square Root\n",
"\n",
"Analog zu voheriger Erklärung Plotten Sie im folgenden die Funktion Square Root Mathematisch definiert als $f(x) = \\sqrt x; \\quad x \\geq 0$.\n",
"\n",
"Gehen Sie dabei wie folgt vor:\n",
"1. Definieren Sie einen geeigneten [Linespace](https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy-linspace) für die Zahlenraum 0...100. (Tipp: Achten Sie auf die Definition! Die Wurzel ist nur für positive Zahlen definiert.)\n",
"2. Berechnen Sie mittels der Funktion [np.sqrt](https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html#numpy.sqrt) die Werte für die Wurzel.\n",
"3. Plotten Sie das Ergebnis"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "94a1aacc-0c6f-443f-b11c-de8f3c6137bc",
"metadata": {
"nbgrader": {
"grade": true,
"grade_id": "cell-bae73642cf0a866a",
"locked": false,
"points": 3,
"schema_version": 3,
"solution": true,
"task": false
}
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# BEGIN SOLUTION\n",
"xs = np.linspace(0, 100, num=200)\n",
"ys = np.sqrt(xs)\n",
"plt.plot(xs, ys)\n",
"plt.show()\n",
"# END SOLUTION"
]
},
{
"cell_type": "markdown",
"id": "89a722de-8f9f-4d92-a3c1-867b61f4ddaf",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-998243908406c7d4",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"# Styling\n",
"\n",
"Da bei mehreren Plots der Überblick schnell verloren geht beschäftigen wir uns im folgenden mit dem Styling. Dabei gehen wir im Schnelldurchlauf durch alle Parameter.\n",
"\n",
"Die Grundlage für alle folgenden Plots werden in nächster Zelle gesetzt."
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "88f9cd6c-0347-4df4-9b5f-0cfb9b655a4c",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-a86ddac229c0bbbb",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [],
"source": [
"x = np.linspace(0, 2*np.pi, num=200)\n",
"s = np.sin(x)\n",
"c = np.cos(x)"
]
},
{
"cell_type": "markdown",
"id": "3b164da6-f153-4e50-98f0-18c75f6adf19",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-e27c575962048d7b",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"## Farbe ändern\n",
"\n",
"Die Standard Farbe für den ersten Plot ist immer Blau. Um die Farbe zu verändern wird `plt.plot` der Parameter `color` übergeben. Dieser erwartet einen String. Für eine genauere Erläuterung lesen Sie die Dokumentation zu [Specifying color](https://matplotlib.org/stable/users/explain/colors/colors.html). Für dieses Notebook werden die Beispiele mit den \"Single Character Shorthands\" (Aus der Dokumentation zu entnehmen) ausgestattet.\n",
"\n",
"Plotten wir den Sinus nun in Rot:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "de303799-2d02-4793-a40b-f5e528bc6f42",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-1ec79feac73af81f",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(x, s, color='r') # Plot mit der Farbe Rot\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "dcf5f847-7d69-4b29-81a2-c2dc2c0b3e90",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-6d559d4604922bd9",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"## Titel für den Plot setzen\n",
"\n",
"Dafür wird [plt.title](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.title.html) der Paramter wird als String übergeben:"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "aa2a1bba-3ec6-4097-874f-508a7b11c68f",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-106786a4fca81b67",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(x, s)\n",
"plt.title(\"Sinus Plot\") # Titel Setzen\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "f5de5666-aa6f-43cc-a230-8c54779b1f73",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-8d7bed3592e18530",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"## Legende und Labels\n",
"\n",
"Um eine Legende anzuzeigen muss vor `plt.show` die Funktion [plt.legend](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html) aufgerufen werden. Damit dies Wirkung zeigt braucht muss jeder Plot mit dem Parameter `label` (als String) ausgezeichnet werden. Plotten wir im Folgenden den Sinus und Kosinus mit entsprechenden Labels."
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "b6962f15-dd2d-45fe-895a-e388687847aa",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-b1d037b9a275622c",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(x, s, label=\"Sinus\") # Plotte Sinus mit label Sinus\n",
"plt.plot(x, c, label=\"Kosinus\") # Plotte Kosinus mit label Kosinus\n",
"plt.legend() # Füge die Legende ein\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "712b286b-d173-4aef-8bc0-04e067659a86",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-63bbc82ff5e6892a",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"## Linestyle\n",
"\n",
"Die letze wichtige Eigenschaft ist das Setzen eines Linestyles. Dazu wird `plt.plot` der parameter `linestyle` als String übergeben. Entnehmen Sie die verschiednen Linestyles bitte der Dokumentation zu [Linestyles](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html).\n",
"\n",
"Sinus als `dashed` line:"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "bc6a2a76-51c3-46d8-9e07-3c3b3f2a571f",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-402e40ea2ceafc35",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(x, s, linestyle=\"dashed\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "14d3e8ec-dec6-4d42-90a3-86ffdd11d061",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-88e04ff7645c08cd",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"## Aufgabe - Multiplot\n",
"\n",
"In der nächsten Aufgabe wollen wir gleich zwei Funktionen Plotten. $f(x) = \\sqrt x$ und $g(x) = x^2$.\n",
"\n",
"Gehen Sie dabei wie folgt vor:\n",
"1. Definieren Sie einen geeigneten [Linespace](https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy-linspace) für die Zahlenraum 0...3. (Tipp: Achten Sie auf die Definition! Die Wurzel ist nur für positive Zahlen definiert.)\n",
"2. Berechnen Sie mittels der Funktion [np.sqrt](https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html#numpy.sqrt) die Werte für die Wurzel.\n",
"3. Berechnen Sie mittels der Funktion [np.square](https://numpy.org/doc/stable/reference/generated/numpy.square.html#numpy-square) die Werte für die Quadrat Zahlen\n",
"4. Geben Sie den beiden Plots die Farben Grün & Rot. Nutzen Sie gerne die [Color Shorthands](https://matplotlib.org/stable/users/explain/colors/colors.html) aus der Dokumentation.\n",
"5. Plotten Sie die Square Funktion mit dem Linestyle `dashdot`, wie der Dokumentation zu entnehmen [Linestyles](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)\n",
"6. Geben Sie den beiden Plots angemessene Labels.\n",
"7. Fügen Sie die Legende hinzu.\n",
"8. Plotten Sie das Ergebnis."
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "b354952b-f922-42a7-914a-cda9522dff69",
"metadata": {
"nbgrader": {
"grade": true,
"grade_id": "cell-6bb6ab1d60fffde5",
"locked": false,
"points": 8,
"schema_version": 3,
"solution": true,
"task": false
2024-11-14 19:06:50 +01:00
},
"scrolled": true
2024-10-25 13:28:49 +02:00
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# BEGIN SOLUTION\n",
"xt = np.linspace(0, 3, num=200)\n",
"sqrt = np.sqrt(xt)\n",
"square = np.square(xt)\n",
"\n",
"plt.plot(xt, sqrt, color='r', label=\"Square Root\")\n",
"plt.plot(xt, square, color='g', label=\"Square Function\", linestyle=\"dashdot\")\n",
"plt.legend()\n",
"plt.show()\n",
"# END SOLUTION"
]
},
{
"cell_type": "markdown",
"id": "0f9041af-6d24-4786-969a-5f8fb9b5ba3a",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-486bea96505ad0a6",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"---\n",
"\n",
"# Plot Types\n",
"\n",
"Im folgenden Kapitel beschäftigen wir uns mit verschiedensten Plot typen."
]
},
{
"cell_type": "markdown",
"id": "4ffad0de-cd56-47e1-b327-7c9b16d46865",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-79e43d78c9874975",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"## Line Plots\n",
"\n",
"Line Plots haben wir im voherigen Kapitel bereits kennengelernt. Diese können mittels `plt.plot` augerufen werden.\n",
"\n",
"Beispiel Sinus:"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "2375e035-4413-4cc5-875d-5463685299f4",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-b48b1eec8fe65537",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.linspace(0, 2*np.pi, num=200)\n",
"y = np.sin(x) \n",
"\n",
"plt.plot(x, y) \n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "d5893a0a-65be-418b-889c-ee5d48ed1c9e",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-d81b654168ec4bc8",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"## Bar Charts\n",
"\n",
"Beliebt sind Barcharts. Dazu werden aber mehrere Parameter benötigt. Da einfache mathematische Funktionen bei dieser Art Plot keinen Sinn ergeben.\n",
"\n",
"Konsultieren wir dafür folgendes Beispiel.\n",
"\n",
"Wir wollen wissen wie viele Kinder an einer Grundschule in jeder Klassenstufe sind.\n",
"Dazu benötigen wir 2 Listen.\n",
"1. Die Klassenstufen\n",
"2. Die Anzahl an Kinder in der Klassenstufe"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "bc2b7a3f-0a8d-4f33-9c37-99850e33f6c6",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-3767e976a92e292a",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [],
"source": [
"classes = [\"1. Klasse\", \"2. Klasse\", \"3. Klasse\", \"4. Klasse\"]\n",
"kids = [42, 30, 26, 45]"
]
},
{
"cell_type": "markdown",
"id": "b484d826-1539-4650-891d-d80ab60f4e4f",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-a702a6b994c5809e",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"Plotten wir die Werte nun als Bar Chart mit der Funktion `plt.bar`:"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "2ceb28ef-7db1-4d2e-993b-5a0fa17b0785",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-8c604c68ae96c752",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/SrBM8AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAc10lEQVR4nO3df3TX1X348VdCSIIhCYICIqDsjBYZwo6oJXUd5YcyhhRG3JnOFVQ2znYCU9npKluHR482tDtD2zVa7XFw9BSxeoYe54rz0IFyCgpx+IuN4YaDjiY6OwKmJQK53z92zLcRVBLCDYmPxznvPz7v933fz02uJz7zySekIKWUAgAgk8LuXgAA8OkiPgCArMQHAJCV+AAAshIfAEBW4gMAyEp8AABZiQ8AIKui7l7Ah7W2tsb+/fujvLw8CgoKuns5AMBJSCnFoUOHYtiwYVFY+PGvbZxx8bF///4YMWJEdy8DAOiEffv2xfDhwz92zBkXH+Xl5RHxf4uvqKjo5tUAACfj4MGDMWLEiLb/j3+cMy4+PvhRS0VFhfgAgB7mZN4y4Q2nAEBW4gMAyEp8AABZiQ8AICvxAQBkJT4AgKzEBwCQlfgAALISHwBAVuIDAMhKfAAAWYkPACAr8QEAZCU+AICsirp7AQB8Ol142zPdvYRPrbdWzOrW5/fKBwCQlfgAALISHwBAVuIDAMhKfAAAWYkPACAr8QEAZCU+AICsxAcAkJX4AACyEh8AQFbiAwDISnwAAFmJDwAgK/EBAGQlPgCArMQHAJCV+AAAshIfAEBW4gMAyEp8AABZiQ8AICvxAQBkVdTdC8jtwtue6e4lfGq9tWJWdy8BgDOAVz4AgKzEBwCQlfgAALISHwBAVuIDAMhKfAAAWYkPACAr8QEAZCU+AICsxAcAkNUpxceKFSuioKAgbrnllrZzhw8fjpqamhg0aFD0798/qquro7Gx8VTXCQD0Ep2Oj23btsUDDzwQ48ePb3f+1ltvjaeffjoef/zx2LRpU+zfvz/mzZt3ygsFAHqHTsXHe++9F9dff31873vfi7PPPrvtfFNTUzz00EOxcuXKmDp1akycODFWrVoVP/7xj2Pr1q1dtmgAoOfqVHzU1NTErFmzYvr06e3O19fXx5EjR9qdHzNmTIwcOTK2bNlywrlaWlri4MGD7Q4AoPcq6ugNa9eujZdffjm2bdt23LWGhoYoLi6OAQMGtDs/ZMiQaGhoOOF8tbW1cccdd3R0GQBAD9WhVz727dsXN998c3z/+9+P0tLSLlnAsmXLoqmpqe3Yt29fl8wLAJyZOhQf9fX18fbbb8cll1wSRUVFUVRUFJs2bYpvf/vbUVRUFEOGDIn3338/Dhw40O6+xsbGGDp06AnnLCkpiYqKinYHANB7dejHLtOmTYvXXnut3bkbb7wxxowZE1/96ldjxIgR0bdv39iwYUNUV1dHRMSuXbti7969UVVV1XWrBgB6rA7FR3l5eYwbN67dubKyshg0aFDb+YULF8bSpUtj4MCBUVFREUuWLImqqqqYNGlS160aAOixOvyG009yzz33RGFhYVRXV0dLS0vMmDEj7rvvvq5+GgCghzrl+Ni4cWO7x6WlpVFXVxd1dXWnOjUA0Av52y4AQFbiAwDISnwAAFmJDwAgK/EBAGQlPgCArMQHAJCV+AAAshIfAEBW4gMAyEp8AABZiQ8AICvxAQBkJT4AgKzEBwCQlfgAALISHwBAVuIDAMhKfAAAWYkPACAr8QEAZCU+AICsxAcAkJX4AACyEh8AQFbiAwDISnwAAFmJDwAgK/EBAGQlPgCArMQHAJCV+AAAshIfAEBW4gMAyEp8AABZiQ8AICvxAQBkJT4AgKzEBwCQlfgAALISHwBAVuIDAMhKfAAAWYkPACAr8QEAZCU+AICsxAcAkJX4AACyEh8AQFbiAwDISnwAAFmJDwAgK/EBAGQlPgCArMQHAJCV+AAAshIfAEBW4gMAyEp8AABZiQ8AICvxAQBkJT4AgKzEBwCQlfgAALISHwBAVuIDAMhKfAAAWXUoPu6///4YP358VFRUREVFRVRVVcUPf/jDtuuHDx+OmpqaGDRoUPTv3z+qq6ujsbGxyxcNAPRcHYqP4cOHx4oVK6K+vj62b98eU6dOjTlz5sQbb7wRERG33nprPP300/H444/Hpk2bYv/+/TFv3rzTsnAAoGcq6sjg2bNnt3t89913x/333x9bt26N4cOHx0MPPRRr1qyJqVOnRkTEqlWr4qKLLoqtW7fGpEmTum7VAECP1en3fBw7dizWrl0bzc3NUVVVFfX19XHkyJGYPn1625gxY8bEyJEjY8uWLV2yWACg5+vQKx8REa+99lpUVVXF4cOHo3///rFu3boYO3Zs7NixI4qLi2PAgAHtxg8ZMiQaGho+cr6WlpZoaWlpe3zw4MGOLgkA6EE6HB+f/exnY8eOHdHU1BRPPPFELFiwIDZt2tTpBdTW1sYdd9zR6fvhAxfe9kx3L+FT6a0Vs7p7CUAP0+EfuxQXF8ev/uqvxsSJE6O2tjYmTJgQ3/rWt2Lo0KHx/vvvx4EDB9qNb2xsjKFDh37kfMuWLYumpqa2Y9++fR3+IACAnuOU/52P1tbWaGlpiYkTJ0bfvn1jw4YNbdd27doVe/fujaqqqo+8v6SkpO1Xdz84AIDeq0M/dlm2bFnMnDkzRo4cGYcOHYo1a9bExo0b49lnn43KyspYuHBhLF26NAYOHBgVFRWxZMmSqKqq8psuAECbDsXH22+/HfPnz4+f/vSnUVlZGePHj49nn302rrzyyoiIuOeee6KwsDCqq6ujpaUlZsyYEffdd99pWTgA0DN1KD4eeuihj71eWloadXV1UVdXd0qLAgB6L3/bBQDISnwAAFmJDwAgK/EBAGQlPgCArMQHAJCV+AAAshIfAEBW4gMAyEp8AABZiQ8AICvxAQBkJT4AgKzEBwCQlfgAALISHwBAVuIDAMhKfAAAWYkPACAr8QEAZCU+AICsxAcAkJX4AACyEh8AQFbiAwDIqqi7FwDwcS687ZnuXsKn1lsrZnX3EuilvPIBAGQlPgCArMQHAJCV+AAAshIfAEBW4gMAyEp8AABZiQ8AICvxAQBkJT4AgKzEBwCQlfgAALISHwBAVuIDAMhKfAAAWYkPACAr8QEAZCU+AICsxAcAkJX4AACyEh8AQFbiAwDISnwAAFmJDwAgK/EBAGQlPgCArMQHAJCV+AAAshIfAEBW4gMAyEp8AABZiQ8AICvxAQBkJT4AgKzEBwCQlfgAALISHwBAVuIDAMhKfAAAWYkPACAr8QEAZCU+AICsOhQftbW1cdlll0V5eXkMHjw45s6dG7t27Wo35vDhw1FTUxODBg2K/v37R3V1dTQ2NnbpogGAnqtD8bFp06aoqamJrVu3xnPPPRdHjhyJq666Kpqbm9vG3HrrrfH000/H448/Hps2bYr9+/fHvHnzunzhAEDPVNSRwevXr2/3ePXq1TF48OCor6+P3/zN34ympqZ46KGHYs2aNTF16tSIiFi1alVcdNFFsXXr1pg0aVLXrRwA6JFO6T0fTU1NERExcODAiIior6+PI0eOxPTp09vGjBkzJkaOHBlbtmw54RwtLS1x8ODBdgcA0Ht1Oj5aW1vjlltuiSuuuCLGjRsXERENDQ1RXFwcAwYMaDd2yJAh0dDQcMJ5amtro7Kysu0YMWJEZ5cEAPQAnY6PmpqaeP3112Pt2rWntIBly5ZFU1NT27Fv375Tmg8AOLN16D0fH1i8eHH8wz/8Qzz//PMxfPjwtvNDhw6N999/Pw4cONDu1Y/GxsYYOnToCecqKSmJkpKSziwDAOiBOvTKR0opFi9eHOvWrYsf/ehHMWrUqHbXJ06cGH379o0NGza0ndu1a1fs3bs3qqqqumbFAECP1qFXPmpqamLNmjXx1FNPRXl5edv7OCorK6Nfv35RWVkZCxcujKVLl8bAgQOjoqIilixZElVVVX7TBQCIiA7Gx/333x8REV/84hfbnV+1alXccMMNERFxzz33RGFhYVRXV0dLS0vMmDEj7rvvvi5ZLAD
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.bar(classes, kids)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "a46c7700-d039-4345-b59f-4fc0d0400e95",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-c2444cb0f1af6626",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"### Bessere Datenrepresentation\n",
"\n",
"Da die Daten aus `classes` & `kids` miteinander eine Verbindung teilen wäre die Repräsentation mittels Dictionary die klügere Wahl um keine Fehler in den Plot zu bringen.\n",
"\n",
"Mittels der `.keys` & `.values` Funktion auf dem Dictionary lassen sich dann die Daten gezielt plotten.\n",
"\n",
"Schauen Sie sich daher folgendes Beispiel an:"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "63a994e2-4f9c-44c2-940a-6ea202b50e44",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-1777b8fcd5bd30c4",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Definiere ein Dictionary welches die Anzahl der Schüler ihrer Klasse zuweist \n",
"school = { \n",
" \"1. Klasse\": 42,\n",
" \"2. Klasse\": 30,\n",
" \"3. Klasse\": 26,\n",
" \"4. Klasse\": 45,\n",
"}\n",
"\n",
"plt.bar(school.keys(), school.values()) # Plotte mit den Werten des Dictionarys\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "3371f47f-319b-4387-bcca-ae2363fe0662",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-d2c659803a58f15e",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"### Styling\n",
"\n",
"Bar plots können auch gestyled werden. Hierzu wird dem Parameter `color` eine Liste mit farbwerten übergeben:"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "ace35364-3865-4bbb-92c3-8d9804636329",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-a3604899d50585d2",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"school = { \n",
" \"1. Klasse\": 42,\n",
" \"2. Klasse\": 30,\n",
" \"3. Klasse\": 26,\n",
" \"4. Klasse\": 45,\n",
"}\n",
"\n",
"bar_colors = [\"red\", \"blue\", \"green\", \"yellow\"] # Farben definieren\n",
"\n",
"plt.bar(school.keys(), school.values(), color=bar_colors) # Farben übergeben\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "875f5a2f-9db1-49ba-a7ab-c4452f7b0cb7",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-964f579ce46a2882",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"### Y-Label\n",
"\n",
"Mit `plt.ylabel` (als String) lässt sich die y-Achse beschriften:"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "bedf0e03-583a-4a22-857f-0d786f9365c4",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-21cee3bf50f011e1",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"school = { \n",
" \"1. Klasse\": 42,\n",
" \"2. Klasse\": 30,\n",
" \"3. Klasse\": 26,\n",
" \"4. Klasse\": 45,\n",
"}\n",
"\n",
"bar_colors = [\"red\", \"blue\", \"green\", \"yellow\"]\n",
"\n",
"plt.bar(school.keys(), school.values(), color=bar_colors)\n",
"\n",
"plt.ylabel(\"Anzahl Kinder\") # Beschriften der Y-Achse\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "329b675f-75a8-49c2-a6cc-331882010127",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-32dabb34444f6190",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"### X-Label\n",
"\n",
"Analog Dazu die Beschriftung der X-Achse mit `plt.xlabel`."
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "212535af-d1db-4e08-bb9f-8e14d047cee7",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-3737280b071f9d91",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"school = { \n",
" \"1. Klasse\": 42,\n",
" \"2. Klasse\": 30,\n",
" \"3. Klasse\": 26,\n",
" \"4. Klasse\": 45,\n",
"}\n",
"\n",
"bar_colors = [\"red\", \"blue\", \"green\", \"yellow\"]\n",
"\n",
"plt.bar(school.keys(), school.values(), color=bar_colors)\n",
"\n",
"plt.ylabel(\"Anzahl Kinder\") # Beschriften der Y-Achse\n",
"plt.xlabel(\"Klassenstufen\") # Beschriften der X-Achse\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "147244b1-7bdc-40bc-9f87-93997f9742ed",
"metadata": {
2024-11-14 19:06:50 +01:00
"jp-MarkdownHeadingCollapsed": true,
2024-10-25 13:28:49 +02:00
"nbgrader": {
"grade": false,
"grade_id": "cell-230328a26793cddb",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"### Aufgabe\n",
"\n",
"Ihnen ist ein Datenset `sec_school` einer Hauptschule gegeben, welches die Klassenstufen von 5 bis 9 auf die Anzahl ihrer Schüler im Jahrgang mappt. \n",
"\n",
"Definieren Sie einen Barplot. Gehen Sie dabei wie folgt vor:\n",
"1. Definieren Sie ein geeignetes Farbschema zur Darstellung der Daten.\n",
"2. Extrahieren Sie die Schlüssel und Werte aus dem Datenset und übergeben Sie diese zusammen mit den Farbwerten an die Funktion `plt.bar`.\n",
"3. Setzen Sie geeignete Werte für die X & Y-Achse.\n",
"4. Setzen Sie einen geeigneten Titel für den Plot.\n",
"5. Plotten Sie den Werte"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "faec1d5e-c08c-4401-9fbd-5e324c045555",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-b954e989a8bbc2fa",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [],
"source": [
"sec_school = {\n",
" '5. Klasse': 29,\n",
" '6. Klasse': 35,\n",
" '7. Klasse': 25,\n",
" '8. Klasse': 28,\n",
" '9. Klasse': 31\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "0713b074-1eb9-4b5a-ae37-7ee237f814d9",
"metadata": {
"nbgrader": {
"grade": true,
"grade_id": "cell-8caba57a6ad34b87",
"locked": false,
"points": 5,
"schema_version": 3,
"solution": true,
"task": false
}
},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# BEGIN SOLUTION\n",
"colors = [\"red\", \"blue\", \"green\", \"yellow\", \"pink\"]\n",
"plt.bar(sec_school.keys(), sec_school.values(), color=colors)\n",
"plt.xlabel(\"Klassenstufe\")\n",
"plt.ylabel(\"Anzahl Kinder\")\n",
"plt.title(\"Verteilung Kinder einer Hauptschule pro Klassenstufe\")\n",
"plt.show()\n",
"# END SOLUTION"
]
},
{
"cell_type": "markdown",
"id": "75b88cfc-ac01-4af4-9b90-ca3a3f522115",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-42bf44a09515d0fd",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"## Horizontales Bar Chart\n",
"\n",
"Analog zum Barchart erzeugt `plt.barh` einen Horizontales Barchart.\n",
"\n",
"Beispiel:"
]
},
{
"cell_type": "code",
"execution_count": 87,
"id": "43ec4deb-261a-43c7-ac88-8f43338420b8",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-54166820b406e29e",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"school = { \n",
" \"1. Klasse\": 42,\n",
" \"2. Klasse\": 30,\n",
" \"3. Klasse\": 26,\n",
" \"4. Klasse\": 45,\n",
"}\n",
"\n",
"plt.barh(list(school.keys()), list(school.values()), color=\"maroon\") # barh statt bar\n",
"\n",
"plt.xlabel(\"Anzahl Kinder\") \n",
"plt.ylabel(\"Klassenstufen\") \n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "d5275062-b7f5-4193-9b5a-70f4e861c819",
"metadata": {
2024-11-14 19:06:50 +01:00
"jp-MarkdownHeadingCollapsed": true,
2024-10-25 13:28:49 +02:00
"nbgrader": {
"grade": false,
"grade_id": "cell-3adde3f53176bcb0",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"## Pie Chart <3\n",
"\n",
"Kommen wir als nächstes zu der besten, tollsten und schönsten Darstellung von Daten. Den KUCHENDIAGRAMMEN!\n",
"\n",
"\n",
"\n",
"Kuchendiagramme können mittels `plt.pie` erstellt werden. Nehmen wir dazu wieder das Beispiel aus voherigem Kapitel. Dabei verlangt das Pie Chart nur die Werte (`school.values`) des Datensets:"
]
},
{
"cell_type": "code",
"execution_count": 94,
"id": "0e4c5699-5710-4c32-b4cc-e275bec35cc8",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-46cde6d166912ad0",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"school = { \n",
" \"1. Klasse\": 42,\n",
" \"2. Klasse\": 30,\n",
" \"3. Klasse\": 26,\n",
" \"4. Klasse\": 45,\n",
"}\n",
"\n",
"plt.pie(school.values()) # Pie Chart\n",
"\n",
"plt.title(\"Klassenverteilung einer Grundschule\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "d44f2914-3a55-4f00-b580-498931891efa",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-aec08dc408437049",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"## Styling\n",
"\n",
"Alle Parameter wie `title`, `color`, `xlabel`, `ylabel`, etc. lassen sich auch für das Pie Chart setzen. Die Beschriftung der einzelnen Stücke jedoch Funktioniert etwas anders.\n",
"\n",
"Dazu wird der Parameter `label` mit den dazugehörigen Werten ausgestattet:"
]
},
{
"cell_type": "code",
"execution_count": 95,
"id": "130abb6f-9241-4d2f-8975-8baaf7df6482",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-c742155fd484b71b",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"school = { \n",
" \"1. Klasse\": 42,\n",
" \"2. Klasse\": 30,\n",
" \"3. Klasse\": 26,\n",
" \"4. Klasse\": 45,\n",
"}\n",
"\n",
"plt.pie(school.values(), labels=school.keys()) # Setzen der Labels\n",
"\n",
"plt.title(\"Klassenverteilung einer Grundschule\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "5a3a3264-e1af-4c7f-91f9-8bae4dfedb32",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-5336af155ef45527",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"Zum setzen von Prozentwerten wird der Parameter `autopct` verwendet. Dieser nutzt einen Format String oder eine Funktion zum definieren der Werte. Schaue dazu für mehr in die Dokumentation für [autpct](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.pie.html#matplotlib-pyplot-pie), eine Dokumentation zu Formatstrings findest du [hier](https://www.geeksforgeeks.org/format-specifiers-in-c/).\n",
"\n",
"Beispiel für Prozentwerte:"
]
},
{
"cell_type": "code",
"execution_count": 96,
"id": "f575e892-bbe5-45ae-8df4-f1dfa54b1d1b",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-183044afa87a0492",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"school = { \n",
" \"1. Klasse\": 42,\n",
" \"2. Klasse\": 30,\n",
" \"3. Klasse\": 26,\n",
" \"4. Klasse\": 45,\n",
"}\n",
"\n",
"plt.pie(school.values(), labels=school.keys(), autopct='%1.1f%%') # Setzen von Prozentwerten\n",
"\n",
"plt.title(\"Klassenverteilung einer Grundschule\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "b53f5919-ed5a-4a68-9420-911098cf2491",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-430a11a7f58f4fa5",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"Pie Charts haben auch einen Parameter `shadow`. Dieser ist Standardmässig `False`. Setzt man den Wert auf `True` sieht man einen Schatten:"
]
},
{
"cell_type": "code",
"execution_count": 98,
"id": "f5f47060-123c-4b4f-b518-4fc0a795dfb0",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-408e63a1464d64ea",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
2024-11-14 19:06:50 +01:00
},
"scrolled": true
2024-10-25 13:28:49 +02:00
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"school = { \n",
" \"1. Klasse\": 42,\n",
" \"2. Klasse\": 30,\n",
" \"3. Klasse\": 26,\n",
" \"4. Klasse\": 45,\n",
"}\n",
"\n",
"plt.pie(school.values(), labels=school.keys(), autopct='%1.1f%%', shadow=True) # Zeige einen Schatten\n",
"\n",
"plt.title(\"Klassenverteilung einer Grundschule\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "4dc1531b-4c95-4f39-8f47-209e0df9cdc8",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-376fc818f2a3d818",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"Zum herausnehmen von Kuchenstücken gibt es den Parameter `explode` dieser erwartet eine Liste mit Fließkommezahlen die zwischen 0.0 - Standardwert und 1.0 - absoluter Explode liegen.\n",
"\n",
"Beispiel Klasse 3 ist vom Ursprung 20% entfernt:"
]
},
{
"cell_type": "code",
"execution_count": 99,
"id": "5788aefe-5e4f-4430-b7a6-af82f3b1541b",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-e3921561732c3895",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"school = { \n",
" \"1. Klasse\": 42,\n",
" \"2. Klasse\": 30,\n",
" \"3. Klasse\": 26,\n",
" \"4. Klasse\": 45,\n",
"}\n",
"\n",
"plt.pie(school.values(), labels=school.keys(), autopct='%1.1f%%', explode=[0, 0, 0.2, 0]) # Zeige einen Schatten\n",
"\n",
"plt.title(\"Klassenverteilung einer Grundschule\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "cb2b8d52-cd07-4ed4-a4b6-46d90fd6b614",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-ed3d080835960776",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"## Aufgabe\n",
"\n",
"Ihnen ist ein Datenset `sec_school` einer Hauptschule gegeben, welches die Klassenstufen von 5 bis 9 auf die Anzahl ihrer Schüler im Jahrgang mappt. \n",
"\n",
"Definieren Sie einen Pieplot. Gehen Sie dabei wie folgt vor:\n",
"1. Definieren Sie ein geeignetes Farbschema zur Darstellung der Daten.\n",
"2. Extrahieren Sie die Schlüssel und Werte aus dem Datenset und übergeben Sie diese zusammen mit den Farbwerten an die Funktion `plt.pie`. (Nutzen Sie zum Anzeigen der Prozentwerte autopct='%1.1f%%')\n",
"3. Lassen Sie die 6. Klasse 25% und die 9. Klasse 40% explodieren.\n",
"4. Setzen Sie einen geeigneten Titel für den Plot.\n",
"5. Plotten Sie den Werte."
]
},
{
"cell_type": "code",
"execution_count": 104,
"id": "143c7633-8e06-45a5-9aff-864eb0dc21d3",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-bf48088c515caf5c",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [],
"source": [
"sec_school = {\n",
" '5. Klasse': 29,\n",
" '6. Klasse': 35,\n",
" '7. Klasse': 25,\n",
" '8. Klasse': 28,\n",
" '9. Klasse': 31\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 105,
"id": "32479818-10ee-4800-86b6-a811bc72cb03",
"metadata": {
"nbgrader": {
"grade": true,
"grade_id": "cell-d201bd3e919fcf1c",
"locked": false,
"points": 5,
"schema_version": 3,
"solution": true,
"task": false
2024-11-14 19:06:50 +01:00
},
"scrolled": true
2024-10-25 13:28:49 +02:00
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAGbCAYAAAAcMUOXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/SrBM8AAAACXBIWXMAAA9hAAAPYQGoP6dpAAB6G0lEQVR4nO3dd3hTZfvA8W+SNt2bttBBaQu0jCJDkCkioyDIVBGRJSjvDxSV14Xi3oK+KiqKAxEU2QIiSxFk7z1LoayWtnTvkZzfH5VKbYG2NDlJen+uK5c2OTnPfUKSO8/WKIqiIIQQQgiz0qodgBBCCFEbSQIWQgghVCAJWAghhFCBJGAhhBBCBZKAhRBCCBVIAhZCCCFUIAlYCCGEUIEkYCGEEEIFkoCFEEIIFUgCNrPXXnsNjUajdhi12l133cVdd91V+ndcXBwajYbvv/9etZhqkrzHatb333+PRqMhLi6uxs+t0Wh4/PHHa/ScDRo0YPTo0TV6TmEakoBrwNUP6J49e8rcn5GRQbt27XB0dGTNmjUqRWdbfvvtN1577TW1wxDVVFBQwIwZM+jcuTNeXl7o9XoCAgLo378/8+fPx2AwqB2iEGYjCdhEMjMz6dWrF4cOHWLZsmX07t1b7ZBswm+//cbrr79+S+dYt24d69atq6GILM/UqVPJy8tTO4xykpOT6dSpE5MmTcLV1ZWpU6fy1Vdf8cQTT5CTk8NDDz3EO++8o3aYQpiNndoB2KKsrCyio6M5cOAAS5cupU+fPmqHZPVycnJwcXGpkXPp9foaOY+lsrOzw87O/B/t/Px89Ho9Wm3Fv+tHjBjB/v37WbJkCYMHDy7z2JQpU9izZw8nT568pTKEsCbyLq5h2dnZ9O7dm3379rFkyRL69u170+fMnj2bu+++Gz8/PxwcHGjatCkzZ84sd9yePXuIjo6mTp06ODk5ERoayiOPPFLmmJ9//pk2bdrg5uaGu7s7UVFRfPLJJ2WOSU9P56mnniI4OBgHBwcaNmzI+++/j9FoLD3mar/o9OnTmTVrFuHh4Tg4ONC2bVt2795detz06dPRaDScO3euXLxTpkxBr9eTlpZWet/OnTvp3bs3Hh4eODs707VrV7Zu3VrmeVf7MI8dO8ZDDz2El5cXnTt3ZvTo0Xz++edASd/Z1dtVRqORjz/+mGbNmuHo6Ii/vz/jx48vUz6U7wOuyPWOGT16NA0aNKjy63TVokWLaNq0KY6OjjRv3pxly5aVO+eNrF69mi5duuDi4oKbmxt9+/bl6NGjZY6pqA/4al/jL7/8QvPmzXFwcKBZs2YVdo1cunSJRx55BH9//9LjvvvuuzLHbNy4EY1Gw88//8zUqVMJDAzE2dmZzMzMCuPevn07a9eu5bHHHiuXfK+6/fbbGT58eKXKuF4/d0X9tQ0aNKBfv35s2bKltEsoLCyMH374odzzjx49yt13342TkxNBQUG89dZbZT4XV1Xms2g0Gvnkk0+IiorC0dERX19fevfuXa6rCrjpv8v13iOV7e+vzGdemJ/UgGtQTk4Offr0Yffu3SxevJh+/fpV6nkzZ86kWbNm9O/fHzs7O1auXMmECRMwGo1MnDgRgKSkJHr16oWvry8vvPACnp6exMXFsXTp0tLzrF+/nmHDhtG9e3fef/99AI4fP87WrVt58sknAcjNzaVr165cunSJ8ePHU79+fbZt28aUKVNISEjg448/LhPbTz/9RFZWFuPHj0ej0fDBBx8wePBgzpw5g729PQ888ADPPfccCxcu5Nlnny3z3IULF9KrVy+8vLwA2LBhA3369KFNmza8+uqraLXa0h8fmzdvpl27dmWef//999OoUSPeeecdFEWhVatWxMfHs379eubOnVvudRw/fjzff/89Y8aMYdKkSZw9e5bPPvuM/fv3s3XrVuzt7Sv171EdN3udAFatWsXQoUOJiori3XffJS0tjbFjxxIYGFipMubOncuoUaOIjo7m/fffJzc3l5kzZ9K5c2f2799/0yS+ZcsWli5dyoQJE3Bzc+PTTz9lyJAhnD9/Hh8fHwASExNp3759acL29fVl9erVjB07lszMTJ566qky53zzzTfR6/U888wzFBQUXLd1YeXKlQA8/PDDlbrW6pRxI6dPn+a+++5j7NixjBo1iu+++47Ro0fTpk0bmjVrBsDly5fp1q0bxcXFvPDCC7i4uDBr1iycnJzKnKsyn0WAsWPH8v3339OnTx/GjRtHcXExmzdvZseOHdx+++2lx1Xm3+VWVPUzL8xIEbds9uzZCqCEhIQo9vb2yi+//HLdY1999VXl3y97bm5uueOio6OVsLCw0r+XLVumAMru3buve+4nn3xScXd3V4qLi697zJtvvqm4uLgop06dKnP/Cy+8oOh0OuX8+fOKoijK2bNnFUDx8fFRUlNTS49bvny5AigrV64sva9Dhw5KmzZtypxv165dCqD88MMPiqIoitFoVBo1aqRER0crRqOxzLWHhoYqPXv2LL3v6ms0bNiwcvFPnDix3OunKIqyefNmBVB+/PHHMvevWbOm3P1du3ZVunbtWvr31WudPXv2dY+5atSoUUpISEi551bmdYqKilKCgoKUrKys0vs2btxY+t65kaysLMXT01N59NFHy9x/+fJlxcPDo8z9Fb3HAEWv1yunT58uve/gwYMKoMyYMaP0vrFjxyr16tVTrly5Uub5Dz74oOLh4VH6Xv3zzz8VQAkLC6vw/ftvgwYNUgAlPT29zP15eXlKcnJy6S0tLa30sRuVUdE1Kso/n8WzZ8+W3hcSEqIAyl9//VV6X1JSkuLg4KD897//Lb3vqaeeUgBl586dZY7z8PAoc87KfBY3bNigAMqkSZPKPXbt+7+y/y7/ft/d6HUICQlRRo0aVfp3ZT/zwvykCboGJSYm4ujoSHBwcJWed+0v7IyMDK5cuULXrl05c+YMGRkZAHh6egLw66+/UlRUVOF5PD09ycnJYf369dcta9GiRXTp0gUvLy+uXLlSeuvRowcGg4G//vqrzPFDhw4trcECdOnSBYAzZ86UOWbv3r3ExsaW3rdgwQIcHBwYMGAAAAcOHCAmJoaHHnqIlJSU0nJzcnLo3r07f/31V7nmsP/85z/XvY6KrsvDw4OePXuWua42bdrg6urKn3/+WelzVcfNXqf4+HgOHz7MyJEjcXV1LT2ua9euREVF3fT869evJz09nWHDhpW5Pp1Oxx133FGp6+vRowfh4eGlf7do0QJ3d/fSGBVFYcmSJdx7770oilKmnOjoaDIyMti3b1+Zc44aNapcDbEiV5umr712gC+//BJfX9/SW+fOncs9t7Jl3EjTpk1L/00AfH19iYiIKPM+/u2332jfvn2ZlhhfX98yzeJQuc/ikiVL0Gg0vPrqq+Ue+3eT8c3+XW5VVT/zwnwkAdegr776Cr1eT+/evW86mORaW7dupUePHri4uODp6Ymvry8vvvgiQGkC7tq1K0OGDOH111+nTp06DBgwgNmzZ1NQUFB6ngkTJtC4cWP69OlDUFAQjzzySLm+pJiYGNasWVPmS8/X15cePXoAJc1r16pfv36Zv68mmWv7Ve+//360Wi0LFiwASr7IFy1aRJ8+fXB3dy8tF0q+TP9d9jfffENBQUHptV4VGhpa6dcwJiaGjIwM/Pz8yp0/Ozu73HXVtJu9Tlf7yBs2bFjuuRXd929XX7+777673PWtW7euUtf37xivxnk1xuTkZNLT05k1a1a5MsaMGQOUf39U9t/Izc0NKBkjca0hQ4awfv161q9fT4sWLSp8blXeB9dzs2uHkn+jRo0alTsuIiKizN+V+SzGxsYSEBCAt7d3jcR2K6r6mRfmI33ANahp06b89ttvdO/enZ49e7J169ab1oZjY2Pp3r07kZGRfPTRRwQHB6PX6/ntt9/43//+V1or1Gg0LF68mB07drBy5UrWrl3LI488wocffsiOHTtwdXXFz8+PAwcOsHbtWlavXs3q1auZPXs2I0eOZM6cOUDJwJCePXvy3HPPVRhP48aNy/yt0+kqPE5RlNL/DwgIoEuXLixcuJAXX3yRHTt
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# BEGIN SOLUTION\n",
"plt.pie(\n",
" sec_school.values(),\n",
" labels=sec_school.keys(),\n",
" autopct='%1.1f%%',\n",
" explode=[0, 0.25, 0, 0, 0.4]\n",
")\n",
"\n",
"plt.title(\"Klassenverteilung einer Grundschule\")\n",
"\n",
"plt.show()\n",
"# END SOLUTION"
]
},
{
"cell_type": "markdown",
"id": "83c08253-bc04-4b87-b906-4002ff210bad",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-e189ada272b135b2",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"---\n",
"\n",
"# Extra\n",
"\n",
"Das Meme:\n",
"\n",
"\n",
"\n",
"lässt sich mit folgendem Python Code replizieren:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0079d10b-26fb-4aaa-b925-c6e31e456c6c",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-1120f5b30213d9b5",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = {\n",
" \"Sky\": 0.77,\n",
" \"Sunny side of pyramid\": 0.17,\n",
" \"Shady side of pyramid\": 0.05\n",
"}\n",
"\n",
"colors = [\"#0095d9\", \"#f5e837\", \"#c4b633\"]\n",
"\n",
"plt.pie(data.values(), startangle=-50, colors=colors)\n",
"plt.legend(data.keys(), bbox_to_anchor=(1, 0, 0.5, 0.6))\n",
"plt.savefig(\"meme.png\", bbox_inches=\"tight\", transparent=True)"
]
}
],
"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",
2024-11-14 19:06:50 +01:00
"version": "3.12.7"
2024-10-25 13:28:49 +02:00
}
},
"nbformat": 4,
"nbformat_minor": 5
}
