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

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3FGFSFODF3FMBUJPOTIJQPG1&1T !12 link to other PEP https://www.python.org/dev/peps/pep-0008/

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

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

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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.

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3FGFSFODF3FMBUJPOTIJQPG1&1T 1&1 1&1 1&1 Style Guide for Python Code The Zen of Python Docstring Conventions

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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)

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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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1&1`T)FBEFS https://www.python.org/dev/peps/pep-0008/

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4UBUVT"QSJM'PPM 1&1#%'-3FUJSFNFOU KPLF1&1 Guido leaves Python in the good hands of its new leader and its vibrant community, in order to train for his lifelong dream of climbing Mount Everest.

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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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3FKFDUFE4XJUDI4UBUFNFOU • [PEP 275] Switching on Multiple Values • [PEP 3103] A Switch/Case Statement

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One day, an island appeared. PEP 80xx

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

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/FUXPSL9JTTVF -FUTNFSHFQZUIPOMPVWBJOJOUP/FUXPSL9 *UJTTUJMMPQFO IUUQTHJUIVCDPNOFUXPSLYOFUXPSLYJTTVFT

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4BNQMF LBSBUFDMVCOFUXPSL

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4BNQMF LBSBUFDMVCOFUXPSL

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"QQMZUP1&1(SBQI -BSHF(SPVQ 1&1T ˣ (SPVQT 1&1T

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*OUFSBDUJWF7JTVBMJ[BUJPO IUUQTLPNPGSHJUIVCJPQFQ@NBQ@TJUFMPVWBJOIUNM /FUXPSL9#PLFI

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(SPVQ" 1&1T 1&1 (PWFSOBODFʜʜ

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(SPVQ" 1&1T .FUBEBUB 1BDLBHF 8IFFMʜʜ

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(SPVQ" 1&1T 4USJOHʜʜ

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(SPVQ" 1&1T 1Z1*ʜʜ

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*NQSFTTJPO

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*NQSFTTJPO ❌/FUXPSL9Ͱ࢖͑͹όγοͱ໌֬ͳߏ଄͕Θ͔Δ ⭕ʮԿͰ͜͏͍͏݁ՌʹͳΔΜͩΖ͏ʁʯͱ୳ࡧ͠ ͍ͯ͘͏ͪʹɺ΅Μ΍Γ֓؍Λ௫ΜͰ͍͘ײ͡ ϦϯΫషͬͯͳ͍͚Ͳ࣮͸͜Εؔ܎ͯ͠ΔΜ͡Ό ͳ͍ͷʁ ͦ΋ͦ΋ʮؔ܎ʯͷఆٛͬͯԿͩʁʁ

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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

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%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