demo_compact.py

import scattertext as st

df = st.SampleCorpora.ConventionData2012.get_data().assign(
    parse=lambda df: df.text.apply(st.whitespace_nlp_with_sentences)
)

corpus = st.CorpusFromParsedDocuments(
    df, category_col='party', parsed_col='parse'
).build().get_unigram_corpus().compact(st.AssociationCompactor(2000))

html = st.produce_scattertext_explorer(
    corpus,
    category='democrat',
    category_name='Democratic',
    not_category_name='Republican',
    minimum_term_frequency=0,
    pmi_threshold_coefficient=0,
    width_in_pixels=1000,
    metadata=corpus.get_df()['speaker'],
    transform=st.Scalers.dense_rank,
    max_overlapping=3,
    include_gradient=True,
    left_gradient_term='More Republican',
    right_gradient_term='More Democratic',
    middle_gradient_term='Metric: Dense Rank Difference',
    d3_scale_chromatic_url='scattertext/data/viz/scripts/d3-scale-chromatic.v1.min.js',
    d3_url='scattertext/data/viz/scripts/d3.min.js'
)
open('./demo_compact.html', 'w').write(html)
print('open ./demo_compact.html in Chrome')