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This article mainly introduces the relevant knowledge of "how Python uses networkx to draw Les Mi é rables character relationship". The editor shows you the operation process through an actual case. The operation method is simple, fast and practical. I hope this article "how to use networkx to draw Les Mi é rables character relationship" can help you solve the problem.

Dataset introduction

The figure diagram in Les Miserables has 77 nodes and 254 edges.

Screenshot of the dataset:

Open the README file:

Les Mi é rables network, part of the Koblenz Network Collection====This directory contains the TSV and related files of the moreno_lesmis network: This undirected network contains co-occurances of characters in Victor Hugo's novel 'Les Mi é rables'. A node represents a character and an edge between two nodes shows that these two characters appeared in the same chapter of the the book. The weight of each link indicates how often such a co-appearance occured.More information about the network is provided here: http://konect.cc/networks/moreno_lesmisFiles: meta.moreno_lesmis-Metadata about the network out.moreno_lesmis-The adjacency matrix of the network in whitespace-separated values format With one edge per line The meaning of the columns in out.moreno_lesmis are: First column: ID of from node Second column: ID of to node Third column (if present): weight or multiplicity of edge Fourth column (if present): timestamp of edges Unix time Third column: edge weightUse the following References for citation:@MISC {konect:2017:moreno_lesmis, title = {Les Mi é rables network dataset-- {KONECT}}, month = oct, year = {2017} Url = {http://konect.cc/networks/moreno_lesmis}}@book{konect:knuth2993, title = {The {Stanford} {GraphBase}: A Platform for Combinatorial Computing}, author = {Knuth, Donald Ervin}, volume = {37}, year = {1993}, publisher = {Addison-Wesley Reading},} @ book {konect:knuth2993, title = {The {Stanford} {GraphBase}: A Platform for Combinatorial Computing} Author = {Knuth, Donald Ervin}, volume = {37}, year = {1993}, publisher = {Addison-Wesley Reading},} @ inproceedings {konect, title = {KONECT}-- {The} {Koblenz} {Network} {Collection}}, author = {J é r ô me Kunegis}, year = {2013}, booktitle = {Proc. Int. Conf. On World Wide Web Companion}, pages = {1343 Mustang 1350}, url = {http://dl.acm.org/citation.cfm?id=2488173}, url_presentation = {https://www.slideshare.net/kunegis/presentationwow}, url_web = {http://konect.cc/}, url_citations = {https://scholar.google.com/scholar?cites=7174338004474749050},}@inproceedings{konect, Title = {{KONECT}-{The} {Koblenz} {Network} {Collection}}, author = {J é r ô me Kunegis}, year = {2013}, booktitle = {Proc. Int. Conf. On World Wide Web Companion}, pages = {1343 Mustang 1350}, url = {http://dl.acm.org/citation.cfm?id=2488173}, url_presentation = {https://www.slideshare.net/kunegis/presentationwow}, url_web = {http://konect.cc/}, url_citations = {https://scholar.google.com/scholar?cites=7174338004474749050},}

It can be seen that the graph is an undirected graph, the nodes represent the characters in Les Miserables, the edges between the two nodes indicate that the two characters appear in the same chapter of the book, and the weight of the edges represents the frequency of the two characters (nodes) appearing in the same chapter.

The real data is in out.moreno_lesmis_lesmis, open and save as a csv file:

Data processing.

The initialization code for undirected graph in networkx is:

G = nx.Graph () g.add_nodes_from ([i for i in range (1,78)]) g.add_edges_from ([(1,2, {'weight': 1})])

Node initialization is easy to solve, we mainly deal with edge initialization: first turn dataframe into a list, and then turn each element into a tuple.

Df = pd.read_csv ('out.csv') res = df.values.tolist () for i in range (len (res)): res [I] [2] = dict ({' weight': res [I] [2]}) res = [tuple (x) for x in res] print (res)

The output of res is as follows (in part):

[(1,2, {'weight': 1}), (2, 3, {' weight': 8}), (2, 4, {'weight': 10}), (2, 5, {' weight': 1}), (2, 6, {'weight': 1}), (2, 7, {' weight': 1}), (2, 8, {'weight': 1}).]

So the initialization code for the figure is:

G = nx.Graph () g.add_nodes_from ([i for i in range (1,78)]) g.add_edges_from (res) drawing nx.draw (g) plt.show ()

The dataset that comes with networkx

After working for a long time, I found that networkx has its own dataset, including the character diagram of Les Miserables:

G = nx.les_miserables_graph () nx.draw (g, with_labels=True) plt.show ()

Complete code #-*-coding: utf-8-*-import networkx as nximport matplotlib.pyplot as pltimport pandas as pd# 77 254df = pd.read_csv ('out.csv') res = df.values.tolist () for i in range (len (res)): res [I] [2] = dict ({' weight': res [I] [2]}) res = [tuple (x) for x in res] print (res) # initialization graph g = nx.Graph () G.add_nodes_from ([i for i in range (1) 78)]) g.add_edges_from (res) g = nx.les_miserables_graph () nx.draw (g, with_labels=True) plt.show () this is the end of the introduction on "how Python draws Les Mi é rables with networkx drawing". Thank you for your reading. If you want to know more about the industry, you can follow the industry information channel. The editor will update different knowledge points for you every day.

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