SciPyJapan2019: Let's Enjoy the Python World Using Network Analysis

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3FGFSFODF3FMBUJPOTIJQPG1&1T 1&1 Style Guide for Python Code

3FGFSFODF3FMBUJPOTIJQPG1&1T 1&1 1&1 Style Guide for Python Code The Zen
of Python As PEP 20 says, “Readability counts".

3FGFSFODF3FMBUJPOTIJQPG1&1T 1&1 1&1 1&1 Style Guide for Python Code The
Zen of Python Docstring Conventions The "Specification" text comes mostly verbatim from the Python Style Guide [4] essay by Guido van Rossum.

3FGFSFODF3FMBUJPOTIJQPG1&1T 1&1 1&1 1&1 Style Guide for Python Code The
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MJOLJOGPSNBUJPO OYGSPN@QBOEBT@BEKBDFODZ import pandas as pd import networkx
as nx adj_df = pd.read_csv(‘adjacency_matrix.csv’) # NetworkX 2.2 pep_graph = nx.from_pandas_adjacency(adj_df, create_using=nx.DiGraph)

MJOLJOGPSNBUJPO $POpSN print(nx.info(pep_graph)) Name: Type: DiGraph Number of
nodes: 486 Number of edges: 964 Average in degree: 1.9835 Average out degree: 1.9835 pep_graph[‘0001'] # Confirm about PEP 1 AtlasView({'0001': {'weight': 1.0}, '0002': {'weight': 1.0}, '0007': {'weight': 1.0}, …

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for column_name, series in header_df.iteritems(): nx.set_node_attributes(pep_graph, dict(series), column_name) # Confirm
pep_graph.nodes[‘0001'] {'Created': '13-Jun-2000', 'Status': 'Active', 'Title': 'PEP Purpose and Guidelines', 'Type': 'Process', } 1&1`T)FBEFS OYTFU@OPEF@BUUSJCVUFT

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plt.figure(figsize=(110, 110)) # Calc Layout position_dict = nx.nx_agraph.graphviz_layout(pep_graph, prog='fdp') #
Draw nx.draw_networkx_nodes(pep_graph, pos=position_dict, node_color="#ffa1a1", node_size=1200) nx.draw_networkx_labels(pep_graph, pos=position_dict, font_size=30, alpha=0.7) nx.draw_networkx_edges(pep_graph, pos=position_dict, alpha=0.7, arrowsize=30) plt.show() %SBXJOH IUUQTOFUXPSLYHJUIVCJPEPDVNFOUBUJPOTUBCMFSFGFSFODFESBXJOHIUNM /FUXPSL9͸؆୯ͳඳըͷػೳΛඋ͍͑ͯΔ

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for group_nodes in nx.weakly_connected_components(sample_graph): print(group_nodes) {'A'} {'C', 'B'} {'D', 'F',
'E'} {'G', 'H', 'K', 'L'} OYXFBLMZ@DPOOFDUFE@DPNQPOFOUT

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One day, an island appeared. PEP 80xx

……The island was connected to the continent. PEP 1
PEP 80xx

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4PNFUIJOHMJLFBNBQ • ݁ہͷͱ͜Ζɺ࣮ࡍͲ͏ͳͷ͔͸ݱ஍ʹ଍ΛӡͿʢ= PEPͷத਎ ΛͪΌΜͱಡΉʣ͜ͱൈ͖Ͱ͸Θ͔Βͳ͍ • Ͱ΋ཱྀ׳Ε͍ͯ͠ͳ͍΍͕ͭ஍ਤແ͠Ͱา͘ͷ͸ͭΒ͍ • ͳΜͱͳ͋͘ͷลʹࢁ΍઒ʢಛ௃తͳPEPʣ͕͋Δ •
͋ͷล͸ཱྀͷ೉౓ߴͦ͏͔ͩΒࠓ͸ආ͚Α͏ʢඃࢀর਺ଟ͍ͷ ʹԘ௮͚ʹͳͬͨPEPͱ͔ʣ • ͬ͘͟Γ͜͏͍͏஍Ҭʢαϒάϧʔϓʣ͕͋ΔΜͩͳ͊ • ʢ೔ʑߋ৽͞Ε͍ͯΔͷͰʣͨ·ʹ஍ܗͷେن໛มԽ͕ى͜Δ

%BUB$PEF • PEP Map • https://github.com/komo-fr/pep_map_site • Network • https://komo-fr.github.io/pep_map_site/network.html
• Clustering Result • https://komo-fr.github.io/pep_map_site/louvain.html • Jupyter Notebook • https://github.com/komo-fr/SciPyJapan_2019_talk

SciPyJapan2019: Let's Enjoy the Python World Using Network Analysis

SciPyJapan2019: Let's Enjoy the Python World Using Network Analysis

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