<jupyter_start><jupyter_text>Análisis: Co-ocurrencia<jupyter_code>import pandas as pd
from techminer import RecordsDataFrame

rdf = RecordsDataFrame(
    pd.read_json(
        'part-07.json', 
        orient='records', 
        lines=True))<jupyter_output><empty_output><jupyter_text>Authors vs Source Title<jupyter_code>rdf.co_ocurrence(
    column_r='Authors', 
    column_c='Source title', 
    sep_r=',', 
    sep_c=None,
    top_n=20).tomatrix()
rdf.co_ocurrence(
    column_r='Authors', 
    column_c='Source title', 
    sep_r=',', 
    sep_c=None,
    top_n=20).heatmap(figsize=(10,7))<jupyter_output><empty_output><jupyter_text>Authors vs Document Type<jupyter_code>rdf.co_ocurrence(
    column_r='Authors', 
    column_c='Document Type', 
    sep_r=',', 
    sep_c=None,
    top_n=20).tomatrix()
rdf.co_ocurrence(
    column_r='Authors', 
    column_c='Document Type', 
    sep_r=',', 
    sep_c=None,
    top_n=20).heatmap(figsize=(10,3))<jupyter_output><empty_output><jupyter_text>Authors vs Keywords<jupyter_code>rdf.co_ocurrence(
    column_r='Authors', 
    column_c='keywords (cleaned)', 
    sep_r=',', 
    sep_c=';',
    top_n=20).tomatrix()
rdf.co_ocurrence(
    column_r='Authors', 
    column_c='keywords (cleaned)', 
    sep_r=',', 
    sep_c=';',
    top_n=10).heatmap(figsize=(9,6))
rdf.co_ocurrence(
    column_r='Authors', 
    column_c='keywords (cleaned)', 
    sep_r=',', 
    sep_c=';',
    top_n=20).heatmap(figsize=(10,7))
rdf.co_ocurrence(
    column_r='Authors', 
    column_c='keywords (cleaned)', 
    sep_r=',', 
    sep_c=';',
    top_n=30).heatmap(figsize=(11,8))<jupyter_output><empty_output><jupyter_text>Document Type vs Keywords<jupyter_code>rdf.co_ocurrence(
    column_r='Document Type', 
    column_c='keywords (cleaned)', 
    sep_r=None, 
    sep_c=';',
    top_n=20).tomatrix()
rdf.co_ocurrence(
    column_r='Document Type', 
    column_c='keywords (cleaned)', 
    sep_r=None, 
    sep_c=';',
    top_n=10).heatmap(figsize=(9,8))
rdf.co_ocurrence(
    column_r='Document Type', 
    column_c='keywords (cleaned)', 
    sep_r=None, 
    sep_c=';',
    top_n=20).heatmap(figsize=(6,9))
rdf.co_ocurrence(
    column_r='Document Type', 
    column_c='keywords (cleaned)', 
    sep_r=None, 
    sep_c=';',
    top_n=30).heatmap(figsize=(6,9))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16558861578266496, 'fraction_numerical': 0.019836136265631736, 'mean_word_length': 4.201793721973094, 'pattern_counts': {'":': 0, '<': 21, '<?xml version=': 0, '>': 21, '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': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '321797', 'license': 'MIT', 'n_tokens_mistral': 1017, 'n_tokens_neox': 933, 'n_words': 110, 'path': 'part-13-co_ocurrence.ipynb', 'repo_name': 'juancamilo1203unal/CFIE-2019-charla', 'size': '2319'}