MetricsΒΆ

Example

>>> from techminer2.packages.networks.co_occurrence.descriptors import NetworkMetrics
>>> df = (
...     NetworkMetrics()
...     #
...     # 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)
...     #
...     # COUNTERS:
...     .using_term_counters(True)
...     #
...     # NETWORK:
...     .using_association_index("association")
...     #
...     # DATABASE:
...     .where_root_directory_is("examples/fintech/")
...     .where_database_is("main")
...     .where_record_years_range_is(None, None)
...     .where_record_citations_range_is(None, None)
...     .where_records_match(None)
...     #
...     .run()
... )
>>> df.head(15) 
                                         Degree  ...  PageRank
FINTECH 38:6131                              19  ...  0.149865
REGULATORS 08:0974                           18  ...  0.062888
THE_DEVELOPMENT 09:1293                      16  ...  0.059116
PRACTITIONER 06:1194                         16  ...  0.041293
TECHNOLOGIES 15:1633                         15  ...  0.080583
FINANCIAL_TECHNOLOGIES 12:1615               15  ...  0.070783
INNOVATION 08:1816                           15  ...  0.047284
FINANCE 10:1188                              14  ...  0.043346
BANKS 08:1049                                14  ...  0.048601
DATA 07:1086                                 14  ...  0.035925
SERVICES 06:1089                             14  ...  0.041161
CONSUMERS 07:0925                            13  ...  0.040264
THE_FINANCIAL_SERVICES_INDUSTRY 06:1237      13  ...  0.046934
FINANCIAL_SERVICES 06:1116                   13  ...  0.043357
FINTECH_COMPANIES 05:1072                    13  ...  0.031250

[15 rows x 4 columns]

Example

>>> from techminer2.packages.networks.co_occurrence.descriptors import NetworkMetrics
>>> df = (
...     NetworkMetrics()
...     #
...     # 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)
...     #
...     # COUNTERS:
...     .using_term_counters(False)
...     #
...     # NETWORK:
...     .using_association_index("association")
...     #
...     # DATABASE:
...     .where_root_directory_is("examples/fintech/")
...     .where_database_is("main")
...     .where_record_years_range_is(None, None)
...     .where_record_citations_range_is(None, None)
...     .where_records_match(None)
...     #
...     .run()
... )
>>> df.head(15) 
                                 Degree  Betweenness  Closeness  PageRank
FINTECH                              19     0.040223   1.000000  0.149865
REGULATORS                           18     0.036134   0.950000  0.062888
THE_DEVELOPMENT                      16     0.024057   0.863636  0.059116
PRACTITIONER                         16     0.024195   0.863636  0.041293
TECHNOLOGIES                         15     0.014720   0.826087  0.080583
FINANCIAL_TECHNOLOGIES               15     0.017453   0.826087  0.070783
INNOVATION                           15     0.015292   0.826087  0.047284
FINANCE                              14     0.018000   0.791667  0.043346
BANKS                                14     0.010319   0.791667  0.048601
DATA                                 14     0.017852   0.791667  0.035925
SERVICES                             14     0.010865   0.791667  0.041161
CONSUMERS                            13     0.010131   0.760000  0.040264
THE_FINANCIAL_SERVICES_INDUSTRY      13     0.006532   0.760000  0.046934
FINANCIAL_SERVICES                   13     0.008387   0.760000  0.043357
FINTECH_COMPANIES                    13     0.017973   0.760000  0.031250