"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 11) Einfache grafische Datenanalyse\n",
"\n",
"Dieses Kapitel stellt die Möglichkeiten von **Pandas** vor, div. einfache und gebräuchliche Grafiken (z.B. Boxplot, Histogramm, Streudiagramm und viele andere) zu erstellen. Importieren wir dazu erstmal wieder unseren bereits bekannten einfachen Datensatz."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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sex
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wohnort
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volksmusik
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hardrock
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"text/plain": [
" sex age wohnort volksmusik hardrock\n",
"0 1 50 2 2.67 3.67\n",
"1 1 57 1 1.00 3.33\n",
"2 2 66 3 2.00 4.33"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"daten = pd.read_csv(\"C:\\\\Datenfiles\\\\daten.csv\")\n",
"\n",
"daten.head(3).round(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 11.1) Plotting Teil 1\n",
"\n",
"**Pandas** bietet mit der Funktion **plot()** bzw. dem Attribut **plot** (vgl. nachfolgenden Link, mit beiden Möglichkeiten können die hier behandelten Grafiken aufgerufen werden) die Möglichkeit, auf einfache Weise diverse gebräuchliche Grafiken zu erstellen. Im Grunde werden die Grafiken dabei nicht von **Pandas** erstellt, sondern die **Pandas** Funktion **plot()** bzw. das Attribut **plot** stehen dabei einfach für die entsprechende Funktion aus dem Paket **Matplotlib** (welches in Kapitel 13 behandelt wird): **matplotlib.pyplot.plot()** (kurz meist mit **plt.plot()** importiert und verwendet). Dies soll uns hier aber nicht weiter beschäftigen, das Kapitel heißt schließlich 'einfache grafische Datenanalyse' - daher nutzen wir die einfach zugänglichen Möglichkeiten von **Pandas**, wenn auch die Gestaltungsmöglichkeiten der Grafiken mit diesen Möglichkeiten (zumindest auf den ersten Blick) nicht sehr umfangreich sind. Publikationsreife Grafiken werden mit **Pandas** kaum erstellt, dies ist Thema von Kapitel 13. Die Grafiken des aktuellen Kapitels dienen eher einer ersten Übersicht und explorativen Datenanalyse.\n",
"\n",
"[Plotting](https://pandas.pydata.org/pandas-docs/stable/reference/frame.html#plotting)\n",
"\n",
"[Visualization](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html#)\n",
"\n",
"[Plot formatting](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html#plot-formatting)\n",
"\n",
"Unter [pandas.DataFrame.plot](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html#pandas.DataFrame.plot) werden diverse Parameter gelistet, welche man bei den folgenden Grafiken zur weiteren Gestaltung heranziehen kann.\n",
"\n",
"Im Folgenden eine Übersicht der verfügbaren Grafiken und ihrer Standardaufrufe (*df* steht dabei für den Namen des Dataframes):"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"|Grafik|Attribut *plot*|Funktion *plot()*|\n",
"|-|-|-|\n",
"|Liniendiagramm|df.plot.line()|df.plot(kind = 'line')|\n",
"|Säulendiagramm|df.plot.bar()|df.plot(kind = 'bar')|\n",
"|Balkendiagramm|df.plot.barh()|df.plot(kind = 'barh')|\n",
"|Histogramm|df.plot.hist()|df.plot(kind = 'hist')|\n",
"|Boxplot|df.plot.box()|df.plot(kind = 'box')|\n",
"|KDE Plot|df.plot.kde()|df.plot(kind = 'kde')|\n",
"|Density Plot|df.plot.density()|df.plot(kind = 'density')|\n",
"|Area Plot|df.plot.area()|df.plot(kind = 'area')|\n",
"|Tortendiagramm|df.plot.pie()|df.plot(kind = 'pie')|\n",
"|Streudiagramm|df.plot.scatter()|df.plot(kind = 'scatter')|\n",
"|Hexbin Plot|df.plot.hexbin()|df.plot(kind = 'hexbin')|\n",
"\n",
"Boxplots können zusätzlich noch mit **df.boxplot()** aufgerufen werden, Histogramme zusätzlich mit **df.hist()**. Wir sehen, es gibt in **Pandas** viele Möglichkeiten, zu all diesen Grafiken zu kommen...\n",
"\n",
"Sehen wir uns nun einige Beispiele an."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Boxplot\n",
"\n",
"Ein Boxplot kann mit **plot.box()** aufgerufen werden. Man kann dies auf das ganze Dataframe anwenden.\n",
"\n",
"[pandas.DataFrame.plot.box](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.box.html#pandas.DataFrame.plot.box)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = daten.plot.box()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dies ist - wie oben ersichtlich - jedoch meist nicht empfehlenswert, da unterschiedlichste Arten von Variablen vorliegen, und meist auch nicht gewünscht, da man sich typischerweise nur über ausgewählte Variablen ein Bild verschaffen möchte. Wählen wir nachfolgend die beiden Variablen *volksmusik* und *hardrock* aus. Bei beiden handelt es sich um 5-stufige Skalen, welche etwas über die Musikpräferenz der Befragten aussagen - es macht also Sinn, die beiden Variablen zu vergleichen."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = daten[['volksmusik', 'hardrock']].plot.box()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Pandas** bietet eine weitere Möglichkeit, Boxplots aufzurufen, nämlich mit der Funktion **boxplot()**. Das Ergebnis ist das Gleiche, außer dass dabei standardmäßig horizontale Bezugslinien angezeigt werden.\n",
"\n",
"[pandas.DataFrame.boxplot](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.boxplot.html#pandas.DataFrame.boxplot)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = daten.boxplot(column = [\"volksmusik\", \"hardrock\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Diese Bezugslinien (*grid*) kann man aber entfernen, wenn man möchte. Außerdem können wir die Schriftgröße der Achsen anpassen."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = daten[[\"volksmusik\", \"hardrock\"]].boxplot(grid = False, fontsize = \"small\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Schließlich kann mit dem Parameter *figsize* auch die Größe der Grafik verändert werden."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = daten.boxplot(column = \"volksmusik\", by = \"wohnort\", figsize = (8, 6)) # 8 inches breit, 6 inches hoch\n",
"\n",
"# column: Die 'abhängige' Variable\n",
"# by: Die Gruppierungsvariable"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vieles, was für den Boxplot galt, gilt auch für einige andere nun noch folgende Grafiken. Dies wird nicht nochmal wiederholt, sodass weitere Grafikarten mit weniger Beispielen auskommen :-)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Histogramm\n",
"\n",
"Aufruf des Histogramms mit **plot.hist()** oder mit der Funktion **hist()**. Stellen wir im Folgenden die Verteilung des Alters (Variable *age*) dar.\n",
"\n",
"[pandas.DataFrame.plot.hist](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.hist.html#pandas.DataFrame.plot.hist)\n",
"\n",
"[pandas.DataFrame.hist](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.hist.html#pandas.DataFrame.hist)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = daten.age.plot.hist(bins = 20, alpha = 0.8, legend = True)\n",
"\n",
"# bins: Anzahl der Balken\n",
"# alpha: Grad der Transparenz von 0 bis 1\n",
"# legend: Anzeige der Legende"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Wir können die Altersverteilung auch getrennt nach Geschlecht (Variable *sex*, 1 = weiblich, 2 = männlich) abbilden. Färben wir die Balken dann auch zur Abwechslung auch in einer anderen Farbe."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = daten.hist(column = \"age\", by = \"sex\", bins = 15, figsize = (12,4), color = \"green\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Obige Abbildungen stellen zwar die gleiche Variable *age* dar, die Y-Achse ist jedoch unterschiedlich skaliert, sodass ein Vergleich schwer fällt. Unten sehen wir, dass dies leicht angepasst werden kann und zwar mit dem Parameter *sharey*. Nun weist die Y-Achse in beiden Abbildungen (links und recht) die gleiche Skalierung auf."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = daten.hist(column = \"age\", by = \"sex\", bins = 15, figsize = (12,4), color = \"blue\", sharey = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### KDE Plot (Density Plot)\n",
"\n",
"KDE steht für Kernel Density Estimation. Es geht dabei um die grafische Darstellung der Verteilung der Werte von Variablen. Sehen wir uns nachfolgend die Verteilung für die beiden Variablen *volksmusik* und *hardrock* an.\n",
"\n",
"[pandas.DataFrame.plot.kde](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.kde.html#pandas.DataFrame.plot.kde)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = daten[[\"volksmusik\", \"hardrock\"]].plot.kde()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Density Plot (KDE Plot)\n",
"\n",
"Und nun, mit anderer Schreibweise, die Altersverteilung als Density Plot (was einem KDE Plot entspricht).\n",
"\n",
"[pandas.DataFrame.plot.density](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.density.html#pandas.DataFrame.plot.density)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = daten.age.plot.density(color = \"k\", lw = 5)\n",
"\n",
"# mit color kann die Linienfarbe geändert werden, 'k' steht für black\n",
"# lw = line width, also die Stärke der Linie"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Streudiagramm\n",
"\n",
"Mit einem Streudiagramm bilden wir nun erstmals 2 Variablen gegeneinander ab. Auf diese Weise kann - so vorhanden - auch leicht visuell ein Zusammenhang zwischen beiden Variablen erkannt werden (daher ist ein Streudiagramm auch sinnvoll, wenn man plant, eine Korrelation oder Regression zu rechnen).\n",
"\n",
"[pandas.DataFrame.plot.scatter](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.scatter.html#pandas.DataFrame.plot.scatter)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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juAZ4AHgbcB3wteyxFUl6hqS/k3SPpAckvXuZZSTpekkPSbpX0kVFd8DMzMpJNgJJ64ADEfHeiPjliPiliPjfEfF0YtWngVdGxAXAhcBrJF2cW+Zy4Lzstgv4YOE9aKhUvkDROfnn5rrs3/9Nut3uss/n5Zcvqt+89altF933MrUMUk/Z+srWW2T91LJlf69my0k2goiYB+6R9IIiA2fzHD2V3d2Q3fKv7quAm7NlvwycKmlTke00USpfID/nfWpO/iNHjrFx424uu+xWTjxxN0eOHOs7b/3cXHfJ8nNzxf7R6DdvfX7f8tsuuu+pOfGLZjmk5vAvWl/Zeousn9rXsr9Xs5UM+tHQJuABSZ+X9MmFW2olSROS7gYeBz4XEV/JLbIZ+Nai+49mj+XH2SVpRtLM7OzsgCWPTipfID/nfWpO/r17D9Lt9v5R6HaDvXsP9p23fnr68JLlp6cPF6q/37z1+X3Lb7vovqfmxC+a5ZCaw79ofWXrLbJ+al/L/l7NVjJoI3g3cCXwHnqnki7c+oqIbkRcCJwNbJX04twiOn6t4941EBE3RsRURExNTjY/ITOVL5Cf8z41J//OneczMdE7VBMTYufO8/vOW79t2+Yly2/bdlxv7avfvPX5fctvu+i+p+bEL5rlkJrDv2h9Zestsn5qX8v+Xs1WtNL81FXfgHcBv5l77EP0prNeuP8gsKnfOE3NI8hL5QsUnZP/6ac7ceed34hOp7Ps83n55YvqN299attF971MLYPUU7a+svUWWT+1bNnfq7UXq80jkPQjlvkf+qIm8uw+604CxyLiB5JOojc/0e9HxB2LlrkCeCvwWuBlwPURsbVf43IegZlZcf3yCPpeRxARz8oGeA/wHXrXEYjedQTPSmx3E3BTNmX1OuDWiLhD0jXZ2HuAffSawEPAEeBNg+6UmZlVY9CEsq9ExMtSjw2D3xGYmRVXRUJZV9Ibs7OA1kl6I+Bz18zMxsCgjeDfAv8G+G52+5XsMTMzW+OScw1ln/FfGxFXDaEeMzMbskGuLO7i6abNzMbWoLOPfjW7kvgvgB8vPBgRt9VSlZmZDc2gjeA04AnglYseC8CNwMxsjRs0s9jn95uZjalBM4ufAbwZeBHwjIXHI+I/1FSXmZkNyaCnj/4x8FzgF4G/pjeJ3I/qKsrMzIZn0EbwLyPid4AfR8RNwBXAz9ZX1tpXNOwkFZaSer7uehdLBcGkaisb/JIar2hQTV4qJCgvHxZT9f4tVvWxqvp1ZGvToI3gWPbnD7KppE8BzqmlojGQCidJBdPkw1JSz6fCUsrWWySYJhWuUia4ZZDa88cqFVSTl9+/fEhQKgTo6NFOpfvXb1/LHqsqa7O1bdBGcKOk5wD/Hfgkvczi36+tqjUuFU6SCqbJh6Wknk+FpZStt0gwTSpcpUxwyyC1549VKqgmL79/+ZCgVAjQvn0PV7p//fa17LGqsjZb24p8R3A58PPATcD/Ac6qq6i1LhVOkgqmyYelpJ5PhaWUrbdIME0qXKVMcMsgteePVSqoJi+/f/mQoFQI0I4d51a6f/32teyxqrI2W9sGnX3008CTwAEWTTYXEcmUsqqtldlH5+eD2dkjnHnmRiQl73c68xw69ARbtpzOunXrCj9fd72L5WtJjVVmW6upPVVfSn791Hhzc12mpw9nTWGi8v3rt69l16/6dWTN1W/20UEbwf0RkY+ZHIm10gjMzJqkimmopyX5LCEzszHU94IySffRm0piPfAmSQ8DT9NLKYuI+Lk+6z4fuJne9QfzwI0R8b7cMtuBvwL+MXvotoh4z6r2xMzMViV1ZfGVJcbuAL8REXdJehZwQNLnIuJrueW+GBFltjMW/Fnt8PhYmy3V96OhiPhGv1ti3cci4q7s5x8BB4HN1ZU+PsqeH26D87E2O17xUypWQdI5wEuAryzz9Msl3SPpU5JetML6uyTNSJqZnZ2ts9SRKHt+uA3Ox9rseLU3AknPBD4OvD0ifph7+i7ghRFxAfB+4PblxoiIGyNiKiKmJicna613FMqeH26D87E2O95Ap4+uenBpA3AH8JmIeO8Ayz8CTEXE91ZaZlxPH/Xn1sPjY21tVMXpo6vZqICPAgdXagKSnpsth6StWT3F5gQYE+vWibPOOtn/MA2Bj7XZUoMmlK3GK4CdwH2S7s4e+23gBQARsQd4PfAWSR3gJ8DV4WkQzcyGqrZGEBFfone9Qb9lbgBuqKsGMzNLG8pZQ+Oo6jn3y+YPlM0r6Fdvaqxhzse/mn0Z9vplxnM+gI2CG8EqVD3nfmpO/VT+QH4O/aJ5Bal6t2/fu+JYw5yPfxBVz9lfddZDv/F8jYONihvBKlQ9535qTv1U/kB+Dv2ieQX96+0/1jDn4x9E1XP2V5310G88X+Ngo+JGsApVz7mfmlM/lT+Qn0O/aF5B/3r7jzXM+fgHUfWc/VVnPfQbz9c42KjUeh1BHZpyHUHVc+6XzR8om1fQr94I+o41zPn4B1H1nP3DrMfXOFhdSucRNElTGoGZ2VoykgvKzMxsbXAjMDNrOTcCM7OWcyMwM2s5NwIzs5ZzIzAzazk3AjOzlnMjMDNrOTcCM7OWqzOh7PmS7pR0UNIDkq5bZhlJul7SQ5LulXRRXfWYmdny6nxH0AF+IyLOBy4GrpW0JbfM5cB52W0X8MEa66lVag7+1P18nkBq/KL1lKm/bNZB2drKjjfsY1d2vCprMxtEbY0gIh6LiLuyn38EHAQ25xa7Crg5er4MnCppU1011SU1n3/qfj5/oNOZ7zt+0XyBMnkEl156S6msg7K1lR1v2Meu7ParrM1sUEP5jkDSOcBLgK/kntoMfGvR/Uc5vlkgaZekGUkzs7OztdW5Wqn8gdT9fP7AoUNP9B2/aL5AuTyCb5fKOihbW9nxhn3sym6/ytrMBlV7I5D0TODjwNsj4of5p5dZ5bj/5kTEjRExFRFTk5OTdZRZSip/IHU/nz+wZcvpfccvmi9QLo/geaWyDsrWVna8YR+7stuvsjazQdU6DbWkDcAdwGci4r3LPP8hYH9E3JLdfxDYHhGPrTRmU6ehTs3Bn7qfzxNIjV+0njL1d7tRKuugbG1lxxv2sSs7XpW1mS0YSR6Beq/Sm4DvR8TbV1jmCuCtwGuBlwHXR8TWfuM2tRGYmTVZv0awvsbtvgLYCdwn6e7ssd8GXgAQEXuAffSawEPAEeBNNdZjZmbLqK0RRMSXWP47gMXLBHBtXTWYmVmaryw2M2s5NwIzs5ZzIzAzazk3AjOzlnMjMDNrOTcCM7OWcyMwM2s5NwIzs5ZzIzAzazk3giHJh7scPdrhttu+TqfTAdLBNVUrM37VQTVmNlpuBEPQ6cwvCXd56qk5TjppN6973SfZsGE3R44c6xtcU3UASZmAk/y+lA2qMbPRcyMYgkOHnlgS7vLhD9+z5Pm9ew/2DYapOoCkTMBJfl/KBtWY2ei5EQzBli2nLwl3ufbaC5Y8v3Pn+X2DYaoOICkTcJLfl7JBNWY2erUG09RhreYR5INnjh7tsG/fw+zYcS7r169PBtdUrcz4+X1xWIpZ840kmKYua7URmJmNUr9G4I+GzMxarrZGIOljkh6XdP8Kz2+X9KSku7PbO+uqxczMVlZnVOUfATcAN/dZ5osRcWWNNZiZWUJt7wgi4m+A79c1vpmZVWPU3xG8XNI9kj4l6UUrLSRpl6QZSTOzs7PDrM/MbOyNshHcBbwwIi4A3g/cvtKCEXFjRExFxNTk5OSw6jMza4WRNYKI+GFEPJX9vA/YIOmMUdVjZtZWI2sEkp6r7OojSVuzWp4YVT1mZm1V21lDkm4BtgNnSHoUeBewASAi9gCvB94iqQP8BLg61trVbWZmY6C2RhARb0g8fwO900vNzGyERn3W0NhIzdE/N9dl//5v0u12l10+78iRY3zsY/dy7NixZZ8fdn5Bv20Xfb7p1nr9i43Tvlh93AgqkJqj/+jRDhs37uayy27lxBN7+QOLl+90ljaDI0eOcfLJ7+PNb/4sJ5zwPo4cWdoM8nkCdecX9Nt2fltlsg6aYK3Xv9g47YvVy42gAqk5+vfte5hut/eXsNsN9u49uGT5Q4eWfke+d+/BvvfzeQJ15xf023Z+W2WyDppgrde/2Djti9XLjaACqTn6d+w4l4mJ3vTMExNi587zlyy/ZcvpS8bbufP8vvfzeQJ15xf023Z+W2WyDppgrde/2Djti9XL01BXJDVH/9xcl+npw2zbtpmJiYnjls87cuQYe/ceZOfO89mwYcNxzw87v6Dftos+33Rrvf7FxmlfrBznEZiZtZzzCMzMbEVuBGZmLedGYGbWcm4EZmYt50ZgZtZybgRmZi3nRmBm1nJuBGZmLedGYGbWcm4EZmYtV1sjkPQxSY9Lun+F5yXpekkPSbpX0kV11WJmZiur8x3BHwGv6fP85cB52W0X8MEaa6ldKpgmFUSTCppJjV91WMzi5YvWXrWqxx/3YB2zomprBBHxN8D3+yxyFXBz9HwZOFXSprrqqVMqmGZurts3iCYVNJNfPz9+KpimaEDJ4uUvvfSWQrVXHX5S9fjjHqxjthqj/I5gM/CtRfcfzR47jqRdkmYkzczOzg6luCJSwTTT04f7BtGkgmby6+fHTwXTFA0oyS9fpPaqw0+qHn/cg3XMVmOUjWC5ydGX/e9XRNwYEVMRMTU5OVlzWcWlgmm2bdvcN4gmFTSTXz8/fiqYpmhASX75IrVXHX5S9fjjHqxjthq15hFIOge4IyJevMxzHwL2R8Qt2f0Hge0R8Vi/MZuaR5AKpkkF0aSCZlLjVx0Ws3j5bjcK1V61qscf92Ads+WMLJgm0QiuAN4KvBZ4GXB9RGxNjdnURmBm1mT9GsH6Gjd6C7AdOEPSo8C7gA0AEbEH2EevCTwEHAHeVFctZma2stoaQUS8IfF8ANfWtX0zMxuMryw2M2s5NwIzs5ZzIzAzazk3AjOzlqv19NE6SJoFvjHqOjJnAN8bdRF9NLm+JtcGza6vybVBs+trcm1Qb30vjIhlr8hdc42gSSTNrHRebhM0ub4m1wbNrq/JtUGz62tybTC6+vzRkJlZy7kRmJm1nBtBOTeOuoCEJtfX5Nqg2fU1uTZodn1Nrg1GVJ+/IzAzazm/IzAzazk3AjOzlnMjGJCk50u6U9JBSQ9Iui57/DRJn5P0D9mfzxlBbc+Q9HeS7slqe3dTaltU44Skr0q6o4G1PSLpPkl3S5ppYH2nSvpLSYey19/Lm1CfpJ/OjtnC7YeS3t6E2hbV+J+zvxP3S7ol+7vSiPokXZfV9YCkt2ePjaQ2N4LBdYDfiIjzgYuBayVtAf4r8PmIOA/4fHZ/2J4GXhkRFwAXAq+RdHFDaltwHXBw0f0m1QZwWURcuOgc7ibV9z7g0xHxM8AF9I7jyOuLiAezY3Yh8FJ608l/ogm1AUjaDLwNmMoyUSaAq5tQn6QXA78ObKX3O71S0nkjqy0ifFvFDfgr4F8DDwKbssc2AQ+OuK6NwF30wn4aURtwdvaifiW9oCKaUlu2/UeAM3KPNaI+4NnAP5Kd2NG0+hbV82rgb5tUG/+ci34avSn378jqHHl9wK8AH1l0/3eAd4yqNr8jWIUsee0lwFeAsyKL18z+PHNENU1Iuht4HPhcRDSmNmA3vRf5/KLHmlIb9LKyPyvpgKRd2WNNqe9cYBb4v9lHax+RdHKD6ltwNXBL9nMjaouIw8AfAN8EHgOejIjPNqS++4FLJZ0uaSO9kK7nj6o2N4KCJD0T+Djw9oj44ajrWRAR3ei9RT8b2Jq99Rw5SVcCj0fEgVHX0scrIuIi4HJ6H/ldOuqCFlkPXAR8MCJeAvyY0X+MtoSkE4AdwF+MupbFss/XrwL+BfA84GRJvzraqnoi4iDw+8DngE8D99D7+Hkk3AgKkLSBXhP404i4LXv4u5I2Zc9vovc/8pGJiB8A+4HX0IzaXgHskPQIsBd4paQ/aUhtAETEt7M/H6f3GffWBtX3KPBo9g4P4C/pNYam1Ae9BnpXRHw3u9+U2l4F/GNEzEbEMeA24JKm1BcRH42IiyLiUuD7wD+MqjY3ggFJEvBR4GBEvHfRU58Efi37+dfofXcw7NomJZ2a/XwSvb8Ah5pQW0T8VkScHRHn0Pv44AsR8atNqA1A0smSnrXwM73PkO9vSn0R8R3gW5J+OnvoF4Cv0ZD6Mm/gnz8WgubU9k3gYkkbs7+/v0Dvi/ZG1CfpzOzPFwC/TO8Yjqa2YX9JslZvwM/T+yz5XuDu7PZa4HR6X4T+Q/bnaSOo7eeAr2a13Q+8M3t85LXl6tzOP39Z3Ija6H0Gf092ewD4b02qL6vlQmAm+/3eDjynKfXROznhCeCURY81oraslnfT+0/R/cAfAyc2pT7gi/Sa+j3AL4zy2HmKCTOzlvNHQ2ZmLedGYGbWcm4EZmYt50ZgZtZybgRmZi3nRmBm1nJuBGZmLedGYFaApNuzyekeWJigTtKbJX1d0n5JH5Z0Q/b4pKSPS/r77PaK0VZvtjxfUGZWgKTTIuL72VQefw/8IvC39Ob/+RHwBeCeiHirpD8DPhARX8qmEfhM9PIszBpl/agLMFtj3ibpl7Kfnw/sBP46Ir4PIOkvgJ/Knn8VsKU3zQ0Az5b0rIj40TALNktxIzAbkKTt9P5xf3lEHJG0n16QyEr/y1+XLfuToRRotkr+jsBscKcA/5Q1gZ+hF1m6EfhXkp4jaT3wukXLfxZ468IdSRcOs1izQbkRmA3u08B6SfcC/wP4MnAY+J/00ur+H73ZJJ/Mln8bMCXpXklfA64Zfslmaf6y2KwkSc+MiKeydwSfAD4WEZ8YdV1mg/I7ArPyfjfLi76fXtD87SOtxqwgvyMwM2s5vyMwM2s5NwIzs5ZzIzAzazk3AjOzlnMjMDNruf8Pnb3VER4LhbUAAAAASUVORK5CYII=\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = daten.plot.scatter(x = 'age', y = 'hardrock', c = 'DarkBlue', s = 5)\n",
"\n",
"# c steht für color (der Punkte im Diagramm)\n",
"# s steht für size, also die Größe der Punkte"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Wir können auch eine dritte Variable ins Streudiagramm aufnehmen. Dem Parameter *color* (*c*) wird in diesem Fall keine Farbe übergeben, sondern eine Variable (*wohnort* in unserem Beispiel). Die Punkte werden sodann entsprechend den Ausprägungen dieser Variable eingefärbt. Welche Farben das sein sollen, können wir durch die Auswahl einer *colormap* ([Choosing Colormaps in Matplotlib](https://matplotlib.org/stable/tutorials/colors/colormaps.html)) entscheiden. Die Colormap weist zwar eine kontinuierliche Farbskala auf, im Streudiagramm finden sich aber natürlich nur die Farben an den Stellen '1', '2' und '3' wieder (was den Ausprägungen der Variable *wohnort* entspricht)."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = daten.plot.scatter(x = 'age', y = 'volksmusik', c = 'wohnort', colormap = 'viridis', s = 10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Hexbin Plot\n",
"\n",
"Ähnlich einem Streudiagramm stellt ein Hexbin Plot 2 Variablen gegenüber. Liegen sehr viele Werte vor, was ein Streudiagramm ev. unübersichtlich machen würde (da sich bspw. viele Punkte überlagern), kann ein Hexbin Plot Abhilfe schaffen, da hierbei durch farbliche Codierung die Häufigkeit der Werte in einem bestimmten Bereich hervorgehoben wird.\n",
"\n",
"[pandas.DataFrame.plot.hexbin](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.hexbin.html#pandas.DataFrame.plot.hexbin)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = daten.plot.hexbin(x = 'volksmusik', y = 'hardrock', gridsize = 12, colormap = \"hot_r\")\n",
"\n",
"# gridsize: Die Anzahl an Hexagonen entlang der X-Achse"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Gruppierung der Daten für nachfolgende Beispielgrafiken\n",
"\n",
"Für die folgenden Grafiken nehmen wir anhand unseres Dataframes manche Berechnungen oder Gruppierungen vor.\n",
"\n",
"Erstellen wir nun ein neues Dataframe, in welchem *wohnort* der neue Index ist und für jeden Wohnort der Mittelwert der übrigen 4 Variablen wiedergegeben wird."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
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"\n",
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" \n",
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sex
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age
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"
volksmusik
\n",
"
hardrock
\n",
"
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"
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"
wohnort
\n",
"
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"
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"
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"
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"
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" \n",
" \n",
"
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"
1
\n",
"
1.39
\n",
"
40.37
\n",
"
3.39
\n",
"
2.99
\n",
"
\n",
"
\n",
"
2
\n",
"
1.42
\n",
"
40.43
\n",
"
3.68
\n",
"
3.06
\n",
"
\n",
"
\n",
"
3
\n",
"
1.47
\n",
"
36.42
\n",
"
4.07
\n",
"
2.93
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" sex age volksmusik hardrock\n",
"wohnort \n",
"1 1.39 40.37 3.39 2.99\n",
"2 1.42 40.43 3.68 3.06\n",
"3 1.47 36.42 4.07 2.93"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grafik = daten.groupby(['wohnort']).mean()\n",
"\n",
"grafik.round(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Säulendiagramm\n",
"\n",
"Anhand dieser Daten bilden wir nun das mittlere Alter der Befragten pro Wohnort mit einem Säulendiagramm ab.\n",
"\n",
"[pandas.DataFrame.plot.bar](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.bar.html#pandas.DataFrame.plot.bar)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = grafik.plot.barh(y = 'volksmusik', xlim = (1, 5), title = \"Volksmusikpräferenz\",\n",
" xlabel = \"Wohnort\", legend = False)\n",
"\n",
"# title: Diagrammtitel\n",
"# mit 'legend = False' wird die Anzeige der Legende verhindert"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Liniendiagramm\n",
"\n",
"Dies eignet sich auch gut, um Verläufe von Werten über die Zeit darzustellen. In unserem Dataframe haben wir keine solchen Variablen, daher begnügen wir uns mit einem anderen einfachen Beispiel.\n",
"\n",
"[pandas.DataFrame.plot.line](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.line.html#pandas.DataFrame.plot.line)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = grafik.plot.line(y = 'volksmusik', ylim = (1, 5), ylabel = \"Volksmusikpräferenz\",\n",
" grid = True, legend = False, xticks = (1, 2, 3))\n",
"\n",
"# 'grid = True' zur Anzeige der Bezugslinien, um die Werte für die 3 Wohnorte besser ablesen zu können (auf der Y-Achse)\n",
"# xticks: Vorgabe der Werte für die X-Achse, um Dezimalwerte zu vermeiden (wir haben ja nur 3 Wohnorte)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Area Plot\n",
"\n",
"Ähnlich dem Liniendiagramm, nur dass die Fläche unter der Linie farblich ausgefüllt ist. Bilden wir im Folgenden 2 Variablen ab. Man beachte, dass jede der beiden Variablen eine Skalenbandbreite von 1 bis 5 aufweist, beide Variablen zusammen somit eine Bandbreite von 1 bis 10!\n",
"\n",
"[pandas.DataFrame.plot.area](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.area.html#pandas.DataFrame.plot.area)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = grafik.plot.area(y = ['volksmusik', 'hardrock'], ylim = (1, 10), grid = True, xticks = (1, 2, 3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Summenbildung bei einer Variable für die nachfolgende Beispielgrafik\n",
"\n",
"Berechnen wir nun die Summe (Anzahl) der befragten Personen pro Wohnort."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3 139\n",
"1 95\n",
"2 60\n",
"Name: wohnort, dtype: int64"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grafik1 = daten['wohnort'].value_counts()\n",
"\n",
"grafik1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Tortendiagramm\n",
"\n",
"Die vorhin berechneten Summen können nun in Form eines Tortendiagramms dargestellt werden.\n",
"\n",
"[pandas.DataFrame.plot.pie](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.pie.html#pandas.DataFrame.plot.pie)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
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