<jupyter_start><jupyter_text>Analysis: Co-ocurrence<jupyter_code>import pandas as pd
from techminer import RecordsDataFrame

rdf = RecordsDataFrame(pd.read_json("step-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': 0.18080149114631874, 'fraction_numerical': 0.021435228331780055, 'mean_word_length': 6.951851851851852, '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': '311506', 'license': 'MIT', 'n_tokens_mistral': 926, 'n_tokens_neox': 872, 'n_words': 128, 'path': 'guide/tutorial/14-co_ocurrence.ipynb', 'repo_name': 'jdvelasq/tech-miner', 'size': '2146'}