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aki_prj23_transparenzregister/documentations/seminararbeiten/Datenvisualisierung/Diagramme_plotly/Visualisierung_plotly.ipynb
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Feature/visualisierung (#33) 
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{
"cells": [
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"cell_type": "markdown",
"metadata": {},
"source": [
"# Erstellung verschiedener Diagrammarten mit Plotly"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Installiere Plotly, beispielsweise mit pip:"
]
},
{
"cell_type": "code",
"execution_count": 417,
"metadata": {},
"outputs": [
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"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: plotly in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (5.14.1)\n",
"Requirement already satisfied: tenacity>=6.2.0 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from plotly) (8.2.2)\n",
"Requirement already satisfied: packaging in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from plotly) (21.3)\n",
"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from packaging->plotly) (3.0.6)\n"
]
}
],
"source": [
"# install plotly using pip\n",
"!pip3.10 install plotly"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Zum statischen Speichern der Plotly Figuren, installiere Kaleido:"
]
},
{
"cell_type": "code",
"execution_count": 418,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: kaleido in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (0.2.1)\n"
]
}
],
"source": [
"!pip3.10 install -U kaleido"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generierung von Beispieldaten und Anlegen eines Pandas-Dataframes: "
]
},
{
"cell_type": "code",
"execution_count": 419,
"metadata": {},
"outputs": [],
"source": [
"# import libraries\n",
"import plotly.express as px\n",
"import plotly.graph_objs as go\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"# create sample data for one company\n",
"data = {'Jahr': ['2017', '2018', '2019', '2020', '2021', '2022'],\n",
" 'Umsatz': [19, 23, 30, 42, 37, 45]}\n",
"# save as pandas dataframe\n",
"df = pd.DataFrame.from_dict(data)\n",
"\n",
"# create sample data for a second company\n",
"data2 = {'Jahr': ['2017', '2018', '2019', '2020', '2021', '2022'],\n",
" 'Umsatz': [15, 21, 33, 28, 27, 30]}\n",
"# save as pandas dataframe\n",
"df2 = pd.DataFrame.from_dict(data2)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Säulendiagramme"
]
},
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"Erstellung eines \"einfachen\" Säulendiagramms:"
]
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"source": [
"\n",
"# sreate bar plot with named labels and title\n",
"fig_saeule_titel = px.bar(df, x = 'Jahr', y = 'Umsatz', labels = {'Umsatz': 'Umsatz in Mio.€'}, title = 'Umsatzentwicklung von Unternehmen A')\n",
"# set color\n",
"fig_saeule_titel.update_traces(marker_color = '#00509b')\n",
"\n",
"# save as image\n",
"fig_saeule_titel.write_image('Saeule_Titel.png')\n",
"# show in notebook\n",
"fig_saeule_titel.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vergleich zweier Datensätze in einem Säulendiagramm:"
]
},
{
"cell_type": "code",
"execution_count": 423,
"metadata": {},
"outputs": [
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},
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},
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"xaxis": {
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"gridcolor": "white",
"linecolor": "white",
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},
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},
"xaxis": {
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"text": "Jahr"
}
},
"yaxis": {
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},
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],
"source": [
"# create figure\n",
"fig_saeule_zwei= go.Figure()\n",
"\n",
"# add trace for company 1\n",
"fig_saeule_zwei.add_trace(go.Bar(x = df['Jahr'], y = df['Umsatz'], name = 'A', marker_color=\"#00509b\"))\n",
"\n",
"# add trace for company 2\n",
"fig_saeule_zwei.add_trace(go.Bar(x = df2['Jahr'], y = df2['Umsatz'], name = 'B', marker_color = \"#6f7072\"))\n",
"\n",
"# update layout to grouped\n",
"fig_saeule_zwei.update_layout(barmode='group')\n",
"\n",
"# set title and labels\n",
"fig_saeule_zwei.update_layout(\n",
" title = \"Vergleich der Umsatzentwicklung\",\n",
" xaxis_title = \"Jahr\",\n",
" yaxis_title = \"Umsatz in Mio.€\",\n",
" legend_title = \"Unternehmen\",\n",
")\n",
"\n",
"# save as image\n",
"fig_saeule_zwei.write_image('Saeule_Zwei.png')\n",
"# show in notebook\n",
"fig_saeule_zwei.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Liniendiagramme\n",
"\n",
"Erstellen eines \"einfachen\" Liniendiagramms:"
]
},
{
"cell_type": "code",
"execution_count": 424,
"metadata": {},
"outputs": [
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"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
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"linecolor": "white",
"ticks": ""
}
},
"title": {
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"linecolor": "white",
"ticks": "",
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"ticks": "",
"title": {
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},
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}
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],
"source": [
"# create figure\n",
"fig_line = go.Figure()\n",
"# add trace for company 1\n",
"fig_line.add_trace(go.Scatter(x = df['Jahr'], y = df['Umsatz'], name = 'A', line_color = '#00509b', marker_color = '#00509b'))\n",
"# add trace for company 2\n",
"fig_line.add_trace(go.Scatter(x = df2['Jahr'], y = df2['Umsatz'], name = 'B', line_color = '#6f7072', marker_color = '#6f7072'))\n",
"# set title and labels\n",
"fig_line.update_layout(\n",
" title = \"Vergleich der Umsatzentwicklung\",\n",
" xaxis_title = \"Jahr\",\n",
" yaxis_title = \"Umsatz in Mio.€\",\n",
" legend_title = \"Unternehmen\",\n",
")\n",
"# save as image\n",
"fig_line.write_image(\"Linie_Vergleich.png\")\n",
"# show in notebook\n",
"fig_line.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Streudiagramm"
]
},
{
"cell_type": "code",
"execution_count": 426,
"metadata": {},
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"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# create sample data\n",
"x = [1,2,2,3,5,5,6,6,6,7,8,8,8,10,10,10,7,4,3,4,9,6]\n",
"y = [1,2,3,3,3,4,5,5,6,6,7,7,8,8,9,10,2,10,8,6,6,4]\n",
"\n",
"# create scatter plot\n",
"scatter = go.Figure(data=go.Scatter(x=x, y=y, mode='markers', marker = {'color': '#00509b'}))\n",
"# save as image\n",
"scatter.write_image('Streudiagramm.png')\n",
"# show in netbook\n",
"scatter.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Kreisdiagramm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Erstellung eines \"einfachen\" Kreisdiagramms:"
]
},
{
"cell_type": "code",
"execution_count": 427,
"metadata": {},
"outputs": [
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0.6
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"showland": true,
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},
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}
],
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"sentiment = [0.1, 0.3, 0.6]\n",
"scores = ['negativ', 'neutral', 'positiv']\n",
"# create pie chart\n",
"fig_kreis = px.pie(values=sentiment, color = scores, color_discrete_map={'negativ':'black',\n",
" 'neutral':'#6f7072',\n",
" 'positiv':'#00509b'})\n",
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"# show in notebook\n",
"fig_kreis.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Erweiterung um geeignete Farben, Beschriftungen und einen Titel:"
]
},
{
"cell_type": "code",
"execution_count": 428,
"metadata": {},
"outputs": [
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],
"type": "heatmapgl"
}
],
"histogram": [
{
"marker": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "histogram"
}
],
"histogram2d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
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"#46039f"
],
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0.2222222222222222,
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0.3333333333333333,
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],
"type": "histogram2d"
}
],
"histogram2dcontour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
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"type": "histogram2dcontour"
}
],
"mesh3d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "mesh3d"
}
],
"parcoords": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "parcoords"
}
],
"pie": [
{
"automargin": true,
"type": "pie"
}
],
"scatter": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"type": "scatter"
}
],
"scatter3d": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatter3d"
}
],
"scattercarpet": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattercarpet"
}
],
"scattergeo": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergeo"
}
],
"scattergl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergl"
}
],
"scattermapbox": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattermapbox"
}
],
"scatterpolar": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolar"
}
],
"scatterpolargl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolargl"
}
],
"scatterternary": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterternary"
}
],
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],
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],
"type": "surface"
}
],
"table": [
{
"cells": {
"fill": {
"color": "#EBF0F8"
},
"line": {
"color": "white"
}
},
"header": {
"fill": {
"color": "#C8D4E3"
},
"line": {
"color": "white"
}
},
"type": "table"
}
]
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"layout": {
"annotationdefaults": {
"arrowcolor": "#2a3f5f",
"arrowhead": 0,
"arrowwidth": 1
},
"autotypenumbers": "strict",
"coloraxis": {
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"diverging": [
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"sequential": [
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"colorway": [
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"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "Stimmungsanalyse basierend auf Nachrichtenartikel X"
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# create figure \n",
"fig_sentiment = px.pie(values=sentiment, names=scores, color = scores, color_discrete_map={'negativ':'lightcoral',\n",
" 'neutral':'moccasin',\n",
" 'positiv':'darkseagreen'}, title = 'Stimmungsanalyse basierend auf Nachrichtenartikel X')\n",
"# change line color\n",
"fig_sentiment.update_traces(marker = dict(line=dict(color='#000000', width=2)))\n",
"# save as image\n",
"fig_sentiment.write_image('Kreis_Sentiment.png')\n",
"# show in notebook\n",
"fig_sentiment.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pfeile "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Achtung: Berechnungen stimmen noch nicht, Beispiel funktioniert bisher nur für 0.75 und 0.25 einigermaßen"
]
},
{
"cell_type": "code",
"execution_count": 429,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.patches as patches\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"compound = 0.75\n",
"angle = (compound * 100 - 50) * 1.8\n",
"\n",
"x_length = (np.sin(np.radians(angle))*1.2)/2\n",
"y_length = (np.cos(np.radians(angle))*1.2)/2\n",
"\n",
"if compound < 0.5:\n",
" y_tail = y_length\n",
" y_head = -y_length\n",
" x_tail = x_length\n",
" x_head = - x_length\n",
"else:\n",
" y_tail = -y_length\n",
" y_head = y_length\n",
" x_tail = - x_length\n",
" x_head = x_length\n",
"\n",
"\n",
"dx = x_head - x_tail\n",
"dy = y_head - y_tail\n",
"\n",
"fig, ax = plt.subplots() \n",
"arrow = patches.FancyArrowPatch((x_tail, y_tail), (x_head, y_head),\n",
" mutation_scale=100, ec = 'darkseagreen', fc = 'darkseagreen')\n",
"ax.add_patch(arrow)\n",
"plt.axis('off')\n",
"ax.set_xlim([-1, 1])\n",
"ax.set_ylim([-1, 1])\n",
"ax.set_title('Aktueller Stimmungstrend', fontsize=20)\n",
"fig.suptitle(' ', fontsize=24)\n",
"fig.savefig('Pfeil.png')\n"
]
}
],
"metadata": {
"interpreter": {
"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
},
"kernelspec": {
"display_name": "Python 3.10.1 64-bit",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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