Executing black over all jupyter notebook (#190)

Reverting black for the jupyter notebooks gets old. Can we just run
black over all of them?

documentations/seminararbeiten/Datenvisualisierung/Diagramme_plotly/Visualisierung_plotly.ipynb

@@ -79,16 +79,20 @@
     "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",
+    "data = {\n",
+    "    \"Jahr\": [\"2017\", \"2018\", \"2019\", \"2020\", \"2021\", \"2022\"],\n",
+    "    \"Umsatz\": [19, 23, 30, 42, 37, 45],\n",
+    "}\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",
+    "data2 = {\n",
+    "    \"Jahr\": [\"2017\", \"2018\", \"2019\", \"2020\", \"2021\", \"2022\"],\n",
+    "    \"Umsatz\": [15, 21, 33, 28, 27, 30],\n",
+    "}\n",
     "# save as pandas dataframe\n",
-    "df2 = pd.DataFrame.from_dict(data2)\n"
+    "df2 = pd.DataFrame.from_dict(data2)"
    ]
   },
   {
@@ -1006,12 +1010,12 @@
    ],
    "source": [
     "# create bar plot\n",
-    "fig_saeule = px.bar(df, y = 'Umsatz', labels = {'index': '', 'Umsatz': ''})\n",
+    "fig_saeule = px.bar(df, y=\"Umsatz\", labels={\"index\": \"\", \"Umsatz\": \"\"})\n",
     "# set color\n",
-    "fig_saeule.update_traces(marker_color = '#00509b')\n",
+    "fig_saeule.update_traces(marker_color=\"#00509b\")\n",
     "\n",
     "# save as image\n",
-    "fig_saeule.write_image('Saeule.png')\n",
+    "fig_saeule.write_image(\"Saeule.png\")\n",
     "# show in notebook\n",
     "fig_saeule.show()"
    ]
@@ -1924,12 +1928,12 @@
    ],
    "source": [
     "# create horizontal bar plot\n",
-    "fig_balken = px.bar(df, x = 'Umsatz', labels = {'index': '', 'Umsatz': ''}, orientation='h')\n",
+    "fig_balken = px.bar(df, x=\"Umsatz\", labels={\"index\": \"\", \"Umsatz\": \"\"}, orientation=\"h\")\n",
     "# set color\n",
-    "fig_balken.update_traces(marker_color = '#00509b')\n",
+    "fig_balken.update_traces(marker_color=\"#00509b\")\n",
     "\n",
     "# save as image\n",
-    "fig_balken.write_image('Balken.png')\n",
+    "fig_balken.write_image(\"Balken.png\")\n",
     "# show in notebook\n",
     "fig_balken.show()"
    ]
@@ -2841,14 +2845,19 @@
     }
    ],
    "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",
+    "fig_saeule_titel = px.bar(\n",
+    "    df,\n",
+    "    x=\"Jahr\",\n",
+    "    y=\"Umsatz\",\n",
+    "    labels={\"Umsatz\": \"Umsatz in Mio.€\"},\n",
+    "    title=\"Umsatzentwicklung von Unternehmen A\",\n",
+    ")\n",
     "# set color\n",
-    "fig_saeule_titel.update_traces(marker_color = '#00509b')\n",
+    "fig_saeule_titel.update_traces(marker_color=\"#00509b\")\n",
     "\n",
     "# save as image\n",
-    "fig_saeule_titel.write_image('Saeule_Titel.png')\n",
+    "fig_saeule_titel.write_image(\"Saeule_Titel.png\")\n",
     "# show in notebook\n",
     "fig_saeule_titel.show()"
    ]
@@ -3764,27 +3773,31 @@
    ],
    "source": [
     "# create figure\n",
-    "fig_saeule_zwei= go.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",
+    "fig_saeule_zwei.add_trace(\n",
+    "    go.Bar(x=df[\"Jahr\"], y=df[\"Umsatz\"], name=\"A\", marker_color=\"#00509b\")\n",
+    ")\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",
+    "fig_saeule_zwei.add_trace(\n",
+    "    go.Bar(x=df2[\"Jahr\"], y=df2[\"Umsatz\"], name=\"B\", marker_color=\"#6f7072\")\n",
+    ")\n",
     "\n",
     "# update layout to grouped\n",
-    "fig_saeule_zwei.update_layout(barmode='group')\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",
+    "    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",
+    "fig_saeule_zwei.write_image(\"Saeule_Zwei.png\")\n",
     "# show in notebook\n",
     "fig_saeule_zwei.show()"
    ]
@@ -4697,9 +4710,9 @@
    ],
    "source": [
     "# create line plot\n",
-    "fig_line = px.line(df, y = df['Umsatz'], labels = {'index':'', 'Umsatz': ''})\n",
+    "fig_line = px.line(df, y=df[\"Umsatz\"], labels={\"index\": \"\", \"Umsatz\": \"\"})\n",
     "# set color\n",
-    "fig_line.update_traces(line_color = '#00509b')\n",
+    "fig_line.update_traces(line_color=\"#00509b\")\n",
     "# save as image\n",
     "fig_line.write_image(\"Linie.png\")\n",
     "# show in network\n",
@@ -5617,15 +5630,31 @@
     "# 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",
+    "fig_line.add_trace(\n",
+    "    go.Scatter(\n",
+    "        x=df[\"Jahr\"],\n",
+    "        y=df[\"Umsatz\"],\n",
+    "        name=\"A\",\n",
+    "        line_color=\"#00509b\",\n",
+    "        marker_color=\"#00509b\",\n",
+    "    )\n",
+    ")\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",
+    "fig_line.add_trace(\n",
+    "    go.Scatter(\n",
+    "        x=df2[\"Jahr\"],\n",
+    "        y=df2[\"Umsatz\"],\n",
+    "        name=\"B\",\n",
+    "        line_color=\"#6f7072\",\n",
+    "        marker_color=\"#6f7072\",\n",
+    "    )\n",
+    ")\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",
+    "    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",
@@ -6534,13 +6563,15 @@
    ],
    "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",
+    "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",
+    "scatter = go.Figure(\n",
+    "    data=go.Scatter(x=x, y=y, mode=\"markers\", marker={\"color\": \"#00509b\"})\n",
+    ")\n",
     "# save as image\n",
-    "scatter.write_image('Streudiagramm.png')\n",
+    "scatter.write_image(\"Streudiagramm.png\")\n",
     "# show in netbook\n",
     "scatter.show()"
    ]
@@ -7445,13 +7476,15 @@
    "source": [
     "# create sample data\n",
     "sentiment = [0.1, 0.3, 0.6]\n",
-    "scores = ['negativ', 'neutral', 'positiv']\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",
+    "fig_kreis = px.pie(\n",
+    "    values=sentiment,\n",
+    "    color=scores,\n",
+    "    color_discrete_map={\"negativ\": \"black\", \"neutral\": \"#6f7072\", \"positiv\": \"#00509b\"},\n",
+    ")\n",
     "# save as image\n",
-    "fig_kreis.write_image('Kreis.png')\n",
+    "fig_kreis.write_image(\"Kreis.png\")\n",
     "# show in notebook\n",
     "fig_kreis.show()"
    ]
@@ -8356,14 +8389,22 @@
     }
    ],
    "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",
+    "# create figure\n",
+    "fig_sentiment = px.pie(\n",
+    "    values=sentiment,\n",
+    "    names=scores,\n",
+    "    color=scores,\n",
+    "    color_discrete_map={\n",
+    "        \"negativ\": \"lightcoral\",\n",
+    "        \"neutral\": \"moccasin\",\n",
+    "        \"positiv\": \"darkseagreen\",\n",
+    "    },\n",
+    "    title=\"Stimmungsanalyse basierend auf Nachrichtenartikel X\",\n",
+    ")\n",
     "# change line color\n",
-    "fig_sentiment.update_traces(marker = dict(line=dict(color='#000000', width=2)))\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",
+    "fig_sentiment.write_image(\"Kreis_Sentiment.png\")\n",
     "# show in notebook\n",
     "fig_sentiment.show()"
    ]
@@ -8408,34 +8449,39 @@
     "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",
+    "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",
+    "    x_head = -x_length\n",
     "else:\n",
     "    y_tail = -y_length\n",
     "    y_head = y_length\n",
-    "    x_tail = - x_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",
+    "fig, ax = plt.subplots()\n",
+    "arrow = patches.FancyArrowPatch(\n",
+    "    (x_tail, y_tail),\n",
+    "    (x_head, y_head),\n",
+    "    mutation_scale=100,\n",
+    "    ec=\"darkseagreen\",\n",
+    "    fc=\"darkseagreen\",\n",
+    ")\n",
     "ax.add_patch(arrow)\n",
-    "plt.axis('off')\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"
+    "ax.set_title(\"Aktueller Stimmungstrend\", fontsize=20)\n",
+    "fig.suptitle(\" \", fontsize=24)\n",
+    "fig.savefig(\"Pfeil.png\")"
    ]
   }
  ],

documentations/seminararbeiten/Datenvisualisierung/Diagramme_pyvis/Visualisierung_networkx_pyvis.ipynb

@@ -92,39 +92,40 @@
     "G = nx.MultiGraph()\n",
     "\n",
     "# create list of nodes with attributes as a dictionary\n",
-    "nodes = [(1, {'label': 'Firma 1', 'branche': 'Branche 1', 'land': 'Land 1'}), \n",
-    "         (2, {'label': 'Firma 2', 'branche': 'Branche 1', 'land': 'Land 2'}),\n",
-    "         (3, {'label': 'Firma 3', 'branche': 'Branche 1', 'land': 'Land 3'}),\n",
-    "         (4, {'label': 'Firma 4', 'branche': 'Branche 2', 'land': 'Land 4'}),\n",
-    "         (5, {'label': 'Firma 5', 'branche': 'Branche 2', 'land': 'Land 1'}),\n",
-    "         (6, {'label': 'Firma 6', 'branche': 'Branche 2', 'land': 'Land 3'}),\n",
-    "         (7, {'label': 'Firma 7', 'branche': 'Branche 3', 'land': 'Land 3'}),\n",
-    "         (8, {'label': 'Firma 8', 'branche': 'Branche 3', 'land': 'Land 2'}),\n",
-    "         (9, {'label': 'Firma 9', 'branche': 'Branche 4', 'land': 'Land 1'}),\n",
-    "         (10, {'label': 'Firma 10', 'branche': 'Branche 4', 'land': 'Land 4'}),\n",
-    "        ]\n",
+    "nodes = [\n",
+    "    (1, {\"label\": \"Firma 1\", \"branche\": \"Branche 1\", \"land\": \"Land 1\"}),\n",
+    "    (2, {\"label\": \"Firma 2\", \"branche\": \"Branche 1\", \"land\": \"Land 2\"}),\n",
+    "    (3, {\"label\": \"Firma 3\", \"branche\": \"Branche 1\", \"land\": \"Land 3\"}),\n",
+    "    (4, {\"label\": \"Firma 4\", \"branche\": \"Branche 2\", \"land\": \"Land 4\"}),\n",
+    "    (5, {\"label\": \"Firma 5\", \"branche\": \"Branche 2\", \"land\": \"Land 1\"}),\n",
+    "    (6, {\"label\": \"Firma 6\", \"branche\": \"Branche 2\", \"land\": \"Land 3\"}),\n",
+    "    (7, {\"label\": \"Firma 7\", \"branche\": \"Branche 3\", \"land\": \"Land 3\"}),\n",
+    "    (8, {\"label\": \"Firma 8\", \"branche\": \"Branche 3\", \"land\": \"Land 2\"}),\n",
+    "    (9, {\"label\": \"Firma 9\", \"branche\": \"Branche 4\", \"land\": \"Land 1\"}),\n",
+    "    (10, {\"label\": \"Firma 10\", \"branche\": \"Branche 4\", \"land\": \"Land 4\"}),\n",
+    "]\n",
     "\n",
     "# create list of edges with attributes as a dictionary\n",
     "edges = [\n",
-    "                (1, 2, {'label': 'beziehung1'}), \n",
-    "                (5, 2, {'label': 'beziehung2'}), \n",
-    "                (1, 3, {'label': 'beziehung3'}), \n",
-    "                (2, 4, {'label': 'beziehung3'}), \n",
-    "                (2, 6, {'label': 'beziehung4'}), \n",
-    "                (2, 5, {'label': 'beziehung4'}),\n",
-    "                (8, 10, {'label': 'beziehung4'}),\n",
-    "                (9, 10, {'label': 'beziehung3'}), \n",
-    "                (3, 7, {'label': 'beziehung2'}), \n",
-    "                (6, 8, {'label': 'beziehung1'}), \n",
-    "                (6, 9, {'label': 'beziehung1'}), \n",
-    "                (1, 6, {'label': 'beziehung2'})\n",
-    "        ]\n",
+    "    (1, 2, {\"label\": \"beziehung1\"}),\n",
+    "    (5, 2, {\"label\": \"beziehung2\"}),\n",
+    "    (1, 3, {\"label\": \"beziehung3\"}),\n",
+    "    (2, 4, {\"label\": \"beziehung3\"}),\n",
+    "    (2, 6, {\"label\": \"beziehung4\"}),\n",
+    "    (2, 5, {\"label\": \"beziehung4\"}),\n",
+    "    (8, 10, {\"label\": \"beziehung4\"}),\n",
+    "    (9, 10, {\"label\": \"beziehung3\"}),\n",
+    "    (3, 7, {\"label\": \"beziehung2\"}),\n",
+    "    (6, 8, {\"label\": \"beziehung1\"}),\n",
+    "    (6, 9, {\"label\": \"beziehung1\"}),\n",
+    "    (1, 6, {\"label\": \"beziehung2\"}),\n",
+    "]\n",
     "\n",
     "# add nodes to the graph\n",
     "G.add_nodes_from(nodes)\n",
     "\n",
     "# add edges to the graph, to hide arrow heads of the edges use option arrows = 'false'\n",
-    "G.add_edges_from(edges, arrows = 'false')"
+    "G.add_edges_from(edges, arrows=\"false\")"
    ]
   },
   {
@@ -147,7 +148,9 @@
    "outputs": [],
    "source": [
     "for node in G.nodes:\n",
-    "   G.nodes[node]['title'] = G.nodes[node]['label'] + '\\n' + 'Anzahl Verbindungen: ' + str(G.degree[node])"
+    "    G.nodes[node][\"title\"] = (\n",
+    "        G.nodes[node][\"label\"] + \"\\n\" + \"Anzahl Verbindungen: \" + str(G.degree[node])\n",
+    "    )"
    ]
   },
   {
@@ -206,16 +209,16 @@
    "outputs": [],
    "source": [
     "# scaling the size of the nodes by 5*degree\n",
-    "scale = 5 \n",
+    "scale = 5\n",
     "\n",
     "# getting all nodes and their number of connections\n",
     "d = dict(G.degree)\n",
     "\n",
     "# updating dict\n",
-    "d.update((x, scale*(y+1)) for x, y in d.items())\n",
+    "d.update((x, scale * (y + 1)) for x, y in d.items())\n",
     "\n",
     "# setting size attribute according to created dictionary\n",
-    "nx.set_node_attributes(G,d,'size')"
+    "nx.set_node_attributes(G, d, \"size\")"
    ]
   },
   {
@@ -236,10 +239,17 @@
     "from pyvis.network import Network\n",
     "\n",
     "# create network, 'directed = true' allows multiple edges between nodes\n",
-    "nt = Network('1000px', '1000px', neighborhood_highlight=True, notebook=True, cdn_resources='in_line', directed=True)\n",
+    "nt = Network(\n",
+    "    \"1000px\",\n",
+    "    \"1000px\",\n",
+    "    neighborhood_highlight=True,\n",
+    "    notebook=True,\n",
+    "    cdn_resources=\"in_line\",\n",
+    "    directed=True,\n",
+    ")\n",
     "\n",
     "# populates the nodes and edges data structures\n",
-    "nt.from_nx(G)\n"
+    "nt.from_nx(G)"
    ]
   },
   {
@@ -275,25 +285,28 @@
    "outputs": [],
    "source": [
     "# define new function that sets the color of the nodes\n",
-    "def color_type (net, type):\n",
-    "    ''' color_type sets the color of a network depending on an attribute of the nodes\n",
-    "        net: network\n",
-    "        type: 'branche' or 'land' '''\n",
+    "def color_type(net, type):\n",
+    "    \"\"\"color_type sets the color of a network depending on an attribute of the nodes\n",
+    "    net: network\n",
+    "    type: 'branche' or 'land'\"\"\"\n",
     "\n",
-    "    colormap = {'Branche 1': '#87CEEB',\n",
-    "                'Branche 2': '#0f4c81',\n",
-    "                'Branche 3': '#B2FFFF', \n",
-    "                'Branche 4': '#191970',\n",
-    "                'Land 1': '#F8D568', \n",
-    "                'Land 2': '#F58025', \n",
-    "                'Land 3': '#CC5500', \n",
-    "                'Land 4': '#C0362C'}\n",
+    "    colormap = {\n",
+    "        \"Branche 1\": \"#87CEEB\",\n",
+    "        \"Branche 2\": \"#0f4c81\",\n",
+    "        \"Branche 3\": \"#B2FFFF\",\n",
+    "        \"Branche 4\": \"#191970\",\n",
+    "        \"Land 1\": \"#F8D568\",\n",
+    "        \"Land 2\": \"#F58025\",\n",
+    "        \"Land 3\": \"#CC5500\",\n",
+    "        \"Land 4\": \"#C0362C\",\n",
+    "    }\n",
     "    for node in net.nodes:\n",
-    "        node['color'] = colormap[node[type]]\n",
+    "        node[\"color\"] = colormap[node[type]]\n",
     "    return net\n",
     "\n",
+    "\n",
     "# set color based on attribute\n",
-    "nt = color_type(nt, 'branche')"
+    "nt = color_type(nt, \"branche\")"
    ]
   },
   {
@@ -310,8 +323,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# set all edge colors \n",
-    "nt.options.edges.color = 'grey'"
+    "# set all edge colors\n",
+    "nt.options.edges.color = \"grey\""
    ]
   },
   {
@@ -360,13 +373,20 @@
    ],
    "source": [
     "# activate physics options to try out different solver\n",
-    "#nt.show_buttons(filter_=['physics'])\n",
+    "# nt.show_buttons(filter_=['physics'])\n",
     "\n",
     "# set physics options\n",
-    "nt.barnes_hut(gravity=-8000, central_gravity=0.3, spring_length=200, spring_strength=0.1, damping=0.09, overlap=0)\n",
+    "nt.barnes_hut(\n",
+    "    gravity=-8000,\n",
+    "    central_gravity=0.3,\n",
+    "    spring_length=200,\n",
+    "    spring_strength=0.1,\n",
+    "    damping=0.09,\n",
+    "    overlap=0,\n",
+    ")\n",
     "\n",
     "# create html and save in same folder\n",
-    "nt.show('Netzwerk_Verflechtungsanalyse.html')"
+    "nt.show(\"Netzwerk_Verflechtungsanalyse.html\")"
    ]
   },
   {
@@ -416,16 +436,16 @@
     "from pyvis.network import Network\n",
     "\n",
     "sn = nx.Graph()\n",
-    "sn_nodes = [1,2,3,4,5,6,7]\n",
-    "sn_edges = [(1,4),(2,4),(3,4),(4,5),(5,6),(5,7)]\n",
+    "sn_nodes = [1, 2, 3, 4, 5, 6, 7]\n",
+    "sn_edges = [(1, 4), (2, 4), (3, 4), (4, 5), (5, 6), (5, 7)]\n",
     "\n",
-    "sn.add_nodes_from(sn_nodes, color = '#00509b')\n",
+    "sn.add_nodes_from(sn_nodes, color=\"#00509b\")\n",
     "sn.add_edges_from(sn_edges)\n",
     "\n",
-    "net = Network('1000px', '1000px', notebook=True, cdn_resources='in_line')\n",
+    "net = Network(\"1000px\", \"1000px\", notebook=True, cdn_resources=\"in_line\")\n",
     "\n",
     "net.from_nx(sn)\n",
-    "net.show('Netzwerk.html')\n"
+    "net.show(\"Netzwerk.html\")"
    ]
   }
  ],

documentations/seminararbeiten/Datenvisualisierung/Erstes_Beispiel_Netzwerk/networkx_pyvis.ipynb

@@ -82,19 +82,25 @@
     "import pandas as pd\n",
     "\n",
     "# create dataframe based on the sample data\n",
-    "df_nodes = pd.read_csv('nodes.csv', sep = ';')\n",
+    "df_nodes = pd.read_csv(\"nodes.csv\", sep=\";\")\n",
     "\n",
     "# define shape based on the type\n",
-    "node_shape = {'Company': 'dot', 'Person': 'triangle'}\n",
-    "df_nodes['shape'] = df_nodes['type'].map(node_shape)\n",
+    "node_shape = {\"Company\": \"dot\", \"Person\": \"triangle\"}\n",
+    "df_nodes[\"shape\"] = df_nodes[\"type\"].map(node_shape)\n",
     "\n",
     "# define color based on branche\n",
-    "node_color = {'Branche 1': ' #f3e8eeff', 'Branche 2': '#bacdb0ff', 'Branche 3': '#729b79ff', 'Branche 4': '#475b63ff', 'Branche 5': '#2e2c2fff'}\n",
-    "df_nodes['color'] = df_nodes['branche'].map(node_color)\n",
+    "node_color = {\n",
+    "    \"Branche 1\": \" #f3e8eeff\",\n",
+    "    \"Branche 2\": \"#bacdb0ff\",\n",
+    "    \"Branche 3\": \"#729b79ff\",\n",
+    "    \"Branche 4\": \"#475b63ff\",\n",
+    "    \"Branche 5\": \"#2e2c2fff\",\n",
+    "}\n",
+    "df_nodes[\"color\"] = df_nodes[\"branche\"].map(node_color)\n",
     "\n",
     "# add information column that can be used for the mouse over in the graph\n",
-    "df_nodes = df_nodes.fillna('')\n",
-    "df_nodes['title'] = df_nodes['label'] + '\\n' + df_nodes['branche']\n",
+    "df_nodes = df_nodes.fillna(\"\")\n",
+    "df_nodes[\"title\"] = df_nodes[\"label\"] + \"\\n\" + df_nodes[\"branche\"]\n",
     "\n",
     "# show first five entries of the dataframe\n",
     "print(df_nodes.head())"
@@ -127,7 +133,7 @@
    ],
    "source": [
     "# create dataframe based on the sample data\n",
-    "df_edges = pd.read_csv('edges.csv', sep = ';')\n",
+    "df_edges = pd.read_csv(\"edges.csv\", sep=\";\")\n",
     "\n",
     "# show first five entries of the dataframe\n",
     "print(df_edges.head())"
@@ -157,10 +163,10 @@
     "graph = nx.MultiGraph()\n",
     "\n",
     "# create edges from dataframe\n",
-    "graph = nx.from_pandas_edgelist(df_edges, source = 'from', target = 'to', edge_attr= 'label')\n",
+    "graph = nx.from_pandas_edgelist(df_edges, source=\"from\", target=\"to\", edge_attr=\"label\")\n",
     "\n",
     "# update node attributes from dataframe\n",
-    "nodes_attr = df_nodes.set_index('id').to_dict(orient = 'index')\n",
+    "nodes_attr = df_nodes.set_index(\"id\").to_dict(orient=\"index\")\n",
     "nx.set_node_attributes(graph, nodes_attr)"
    ]
   },
@@ -193,11 +199,11 @@
     "    # create empty dictionary\n",
     "    dict = {}\n",
     "    # get node id\n",
-    "    dict['id'] = node\n",
+    "    dict[\"id\"] = node\n",
     "    # get k-neighbours for k=1,2,3, subtract -1 since output of single_source_shortest_path_length contains node itself\n",
-    "    dict['k=1'] = len(nx.single_source_shortest_path_length(graph, node, cutoff=1))-1\n",
-    "    dict['k=2'] = len(nx.single_source_shortest_path_length(graph, node, cutoff=2))-1\n",
-    "    dict['k=3'] = len(nx.single_source_shortest_path_length(graph, node, cutoff=3))-1\n",
+    "    dict[\"k=1\"] = len(nx.single_source_shortest_path_length(graph, node, cutoff=1)) - 1\n",
+    "    dict[\"k=2\"] = len(nx.single_source_shortest_path_length(graph, node, cutoff=2)) - 1\n",
+    "    dict[\"k=3\"] = len(nx.single_source_shortest_path_length(graph, node, cutoff=3)) - 1\n",
     "    # append list for each node\n",
     "    k_neighbours.append(dict)\n",
     "\n",
@@ -225,40 +231,45 @@
     "from pyvis.network import Network\n",
     "\n",
     "# initiate network\n",
-    "net = Network(directed=False, neighborhood_highlight=True, bgcolor = \"white\", font_color=\"black\")\n",
+    "net = Network(\n",
+    "    directed=False, neighborhood_highlight=True, bgcolor=\"white\", font_color=\"black\"\n",
+    ")\n",
     "\n",
     "# pass networkx graph to pyvis\n",
     "net.from_nx(graph)\n",
     "\n",
-    "# set edge options \n",
+    "# set edge options\n",
     "net.inherit_edge_colors(False)\n",
-    "net.set_edge_smooth('dynamic')\n",
+    "net.set_edge_smooth(\"dynamic\")\n",
     "\n",
     "# chose size format\n",
-    "size_type = 'edges'  # select 'edges' or 'eigen'\n",
+    "size_type = \"edges\"  # select 'edges' or 'eigen'\n",
     "\n",
     "adj_list = net.get_adj_list()\n",
     "\n",
-    "if size_type == 'eigen':\n",
+    "if size_type == \"eigen\":\n",
     "    eigenvector = nx.eigenvector_centrality(graph)\n",
     "\n",
     "# calculate and update size of the nodes depending on their number of edges\n",
     "for node_id, neighbors in adj_list.items():\n",
-    "    if size_type == 'edges':\n",
-    "        size = len(neighbors)*5\n",
-    "    if size_type == 'eigen':\n",
-    "        size = eigenvector[node_id]*200\n",
-    "    next((node.update({'size': size}) for node in net.nodes if node['id'] == node_id), None)\n",
+    "    if size_type == \"edges\":\n",
+    "        size = len(neighbors) * 5\n",
+    "    if size_type == \"eigen\":\n",
+    "        size = eigenvector[node_id] * 200\n",
+    "    next(\n",
+    "        (node.update({\"size\": size}) for node in net.nodes if node[\"id\"] == node_id),\n",
+    "        None,\n",
+    "    )\n",
     "\n",
     "# set the node distance and spring lenght using repulsion\n",
     "net.repulsion(node_distance=250, spring_length=150)\n",
     "\n",
     "# activate physics buttons to further explore the available solvers:\n",
     "# barnesHut, forceAtlas2Based, repulsion, hierarchicalRepulsion\n",
-    "net.show_buttons(filter_=['physics'])\n",
+    "net.show_buttons(filter_=[\"physics\"])\n",
     "\n",
     "# save graph as HTML\n",
-    "net.save_graph('networkx_pyvis.html')\n"
+    "net.save_graph(\"networkx_pyvis.html\")"
    ]
   },
   {