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Used black on jupyter. (#312)

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Philipp Horstenkamp
2023-11-02 21:52:46 +01:00
committed by GitHub
parent 8d9981d967
commit d40746c69b
1 changed files with 47 additions and 31 deletions
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78
documentations/research/networkx_pyvis_pandas.ipynb
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@ -122,19 +122,25 @@
"import pandas as pd\n", "import pandas as pd\n",
"\n", "\n",
"# create dataframe based on the sample data\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", "\n",
"# define shape based on the type\n", "# define shape based on the type\n",
"node_shape = {'Company': 'dot', 'Person': 'triangle'}\n", "node_shape = {\"Company\": \"dot\", \"Person\": \"triangle\"}\n",
"df_nodes['shape'] = df_nodes['type'].map(node_shape)\n", "df_nodes[\"shape\"] = df_nodes[\"type\"].map(node_shape)\n",
"\n", "\n",
"# define color based on branche\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", "node_color = {\n",
"df_nodes['color'] = df_nodes['branche'].map(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", "\n",
"# add information column that can be used for the mouse over in the graph\n", "# add information column that can be used for the mouse over in the graph\n",
"df_nodes = df_nodes.fillna('')\n", "df_nodes = df_nodes.fillna(\"\")\n",
"df_nodes['title'] = df_nodes['label'] + '\\n' + df_nodes['branche']\n", "df_nodes[\"title\"] = df_nodes[\"label\"] + \"\\n\" + df_nodes[\"branche\"]\n",
"\n", "\n",
"# show first five entries of the dataframe\n", "# show first five entries of the dataframe\n",
"print(df_nodes.head())" "print(df_nodes.head())"
@ -167,7 +173,7 @@
], ],
"source": [ "source": [
"# create dataframe based on the sample data\n", "# 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", "\n",
"# show first five entries of the dataframe\n", "# show first five entries of the dataframe\n",
"print(df_edges.head())" "print(df_edges.head())"
@ -197,15 +203,17 @@
"graph = nx.MultiGraph()\n", "graph = nx.MultiGraph()\n",
"\n", "\n",
"# create edges from dataframe\n", "# create edges from dataframe\n",
"graph = nx.from_pandas_edgelist(df_edges, source = 'from', target = 'to', edge_attr= ['label']) #, 'weight'])\n", "graph = nx.from_pandas_edgelist(\n",
" df_edges, source=\"from\", target=\"to\", edge_attr=[\"label\"]\n",
") # , 'weight'])\n",
"\n", "\n",
"#pos = nx.spring_layout(graph, weight = 'weight')\n", "# pos = nx.spring_layout(graph, weight = 'weight')\n",
"#df_nodes['x'] = df_nodes['id'].map(lambda x: pos[x][0])\n", "# df_nodes['x'] = df_nodes['id'].map(lambda x: pos[x][0])\n",
"#df_nodes['y'] = df_nodes['id'].map(lambda x: pos[x][1])\n", "# df_nodes['y'] = df_nodes['id'].map(lambda x: pos[x][1])\n",
"\n", "\n",
"# update node attributes from dataframe\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)\n" "nx.set_node_attributes(graph, nodes_attr)"
] ]
}, },
{ {
@ -237,11 +245,11 @@
" # create empty dictionary\n", " # create empty dictionary\n",
" dict = {}\n", " dict = {}\n",
" # get node id\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", " # 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=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=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=3\"] = len(nx.single_source_shortest_path_length(graph, node, cutoff=3)) - 1\n",
" # append list for each node\n", " # append list for each node\n",
" k_neighbours.append(dict)\n", " k_neighbours.append(dict)\n",
"\n", "\n",
@ -269,41 +277,49 @@
"from pyvis.network import Network\n", "from pyvis.network import Network\n",
"\n", "\n",
"# initiate network\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", "\n",
"# pass networkx graph to pyvis\n", "# pass networkx graph to pyvis\n",
"net.from_nx(graph)\n", "net.from_nx(graph)\n",
"\n", "\n",
"# set edge options \n", "# set edge options\n",
"net.inherit_edge_colors(False)\n", "net.inherit_edge_colors(False)\n",
"net.set_edge_smooth('dynamic')\n", "net.set_edge_smooth(\"dynamic\")\n",
"\n", "\n",
"# chose size format\n", "# chose size format\n",
"size_type = 'edges' # select 'edges' or 'eigen'\n", "size_type = \"edges\" # select 'edges' or 'eigen'\n",
"\n", "\n",
"adj_list = net.get_adj_list()\n", "adj_list = net.get_adj_list()\n",
"\n", "\n",
"if size_type == 'eigen':\n", "if size_type == \"eigen\":\n",
" eigenvector = nx.eigenvector_centrality(graph)\n", " eigenvector = nx.eigenvector_centrality(graph)\n",
"\n", "\n",
"# calculate and update size of the nodes depending on their number of edges\n", "# calculate and update size of the nodes depending on their number of edges\n",
"for node_id, neighbors in adj_list.items():\n", "for node_id, neighbors in adj_list.items():\n",
" if size_type == 'edges':\n", " if size_type == \"edges\":\n",
" size = len(neighbors)*5\n", " size = len(neighbors) * 5\n",
" if size_type == 'eigen':\n", " if size_type == \"eigen\":\n",
" size = eigenvector[node_id]*900\n", " size = eigenvector[node_id] * 900\n",
" next((node.update({'value': size}) for node in net.nodes if node['id'] == node_id), None)\n", " next(\n",
" next((node.update({'size': size}) for node in net.nodes if node['id'] == node_id), None)\n", " (node.update({\"value\": size}) for node in net.nodes if node[\"id\"] == node_id),\n",
" None,\n",
" )\n",
" next(\n",
" (node.update({\"size\": size}) for node in net.nodes if node[\"id\"] == node_id),\n",
" None,\n",
" )\n",
"\n", "\n",
"# set the node distance and spring lenght using repulsion\n", "# set the node distance and spring lenght using repulsion\n",
"net.repulsion(node_distance=250, spring_length=150)\n", "net.repulsion(node_distance=250, spring_length=150)\n",
"\n", "\n",
"# activate physics buttons to further explore the available solvers:\n", "# activate physics buttons to further explore the available solvers:\n",
"# barnesHut, forceAtlas2Based, repulsion, hierarchicalRepulsion\n", "# barnesHut, forceAtlas2Based, repulsion, hierarchicalRepulsion\n",
"net.show_buttons(filter_=['physics'])\n", "net.show_buttons(filter_=[\"physics\"])\n",
"\n", "\n",
"# save graph as HTML\n", "# save graph as HTML\n",
"net.save_graph('networkx_pyvis.html')\n" "net.save_graph(\"networkx_pyvis.html\")"
] ]
}, },
{ {