Terms by Cluster FrameΒΆ

Example

>>> from techminer2.packages.networks.co_occurrence.descriptors import TermsByClusterDataFrame
>>> from techminer2.thesaurus.descriptors import ApplyThesaurus, CreateThesaurus
>>> # Restore the column values to initial values
>>> CreateThesaurus(root_directory="example/", quiet=True).run()
>>> ApplyThesaurus(root_directory="example/", quiet=True).run()
>>> # Generate terms by cluster data frame
>>> df = (
...     TermsByClusterDataFrame()
...     #
...     # FIELD:
...     .having_terms_in_top(20)
...     .having_terms_ordered_by("OCC")
...     .having_term_occurrences_between(None, None)
...     .having_term_citations_between(None, None)
...     .having_terms_in(None)
...     #
...     # NETWORK:
...     .using_clustering_algorithm_or_dict("louvain")
...     .using_association_index("association")
...     #
...     # DATABASE:
...     .where_root_directory_is("example/")
...     .where_database_is("main")
...     .where_record_years_range_is(None, None)
...     .where_record_citations_range_is(None, None)
...     .where_records_match(None)
...     #
...     .run()
... )
>>> # Display the resulting data frame
>>> print(df.to_string()) 
                                          0                               1
0                        THIS_PAPER 14:2240                 FINTECH 46:7183
1                 FINANCIAL_SERVICE 12:2100                 FINANCE 21:3481
2                          SERVICES 09:1527  FINANCIAL_TECHNOLOGIES 18:2455
3                             BANKS 09:1133              INNOVATION 16:2845
4                   THE_DEVELOPMENT 08:1173            TECHNOLOGIES 15:1810
5                        REGULATORS 08:0974              THIS_STUDY 14:1737
6                              DATA 07:1086  THE_FINANCIAL_INDUSTRY 09:2006
7                           BANKING 07:0851            THIS_ARTICLE 06:1360
8                        THE_AUTHOR 07:0828
9                        INVESTMENT 06:1294
10  THE_FINANCIAL_SERVICES_INDUSTRY 06:1237
11                      THE_PURPOSE 06:1046