<jupyter_start><jupyter_text>Research Papers<jupyter_code>%load_ext autoreload
%autoreload 2
from cord import ResearchPapers
research_papers = ResearchPapers.load()
research_papers
metadata = research_papers.metadata
research_papers.covid_related()
import pandas as pd
df = pd.DataFrame({'Papers': [len(metadata)], 
              'Covid19 Papers': [metadata.covid_related.sum()],
              'Newest': [metadata.published.max()],
              'Oldest': [metadata.published.min()],
              'With Antivirals' : [metadata.antivirals.apply(lambda a: len(a) > 0).sum()]},
                 index=[''])
df   
research_papers.before('2001-01-01')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13884785819793205, 'fraction_numerical': 0.01772525849335303, 'mean_word_length': 3.985294117647059, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 6, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '436017', 'license': 'Apache-2.0', 'n_tokens_mistral': 236, 'n_tokens_neox': 213, 'n_words': 38, 'path': 'notebooks/research-papers.ipynb', 'repo_name': 'zyberguy/cord19', 'size': '677'}