<jupyter_start><jupyter_text>Here are some examples to show how bizy works.<jupyter_code>import match
import compare
# Compare a focal and an alter list of firm names

fname_focal = '_edgar_biznames.csv'
fname_alter = '_sdc_biznames.csv'

match.match_lists(fname_focal, fname_alter)

# view result in '~matched.000'
# Are two firm names referring to the same firm?

focal = 'Facebook, Inc.'
alter = 'Facebook'

result = compare.compare_biznames(focal, alter)
result<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.08888888888888889, 'fraction_numerical': 0.006060606060606061, 'mean_word_length': 6.085714285714285, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '297293', 'license': 'MIT', 'n_tokens_mistral': 182, 'n_tokens_neox': 172, 'n_words': 50, 'path': 'example.ipynb', 'repo_name': 'KenYounge/bizmatch', 'size': '495'}