<jupyter_start><jupyter_text>Average time until the first response on a PR<jupyter_code>first_comments.days.mean()


fig1, ax1 = plt.subplots()
ax1.set_title('Average time until the first response (in days)')
ax1.boxplot(first_comments.days)
plt.show()


util.create_mosaic(util.create_pivot(first_comments,"createdat", "days", "mean"),"Reds")

plt.title("Days until the first answer on a PR")
plt.show()<jupyter_output><empty_output><jupyter_text>Top first responder users (excluding bots)<jupyter_code>first_comments[["identifier"]].groupby(first_comments.author).count().sort_values("identifier", ascending=False).head(10)<jupyter_output><empty_output><jupyter_text>Users who had to wait the most for the first answer<jupyter_code>first_comments[["days"]].groupby(first_comments.prauthor).mean().sort_values("days", ascending=False).head(20)<jupyter_output><empty_output><jupyter_text>Users who had to wait the less for the first answer<jupyter_code>first_comments[["days"]].groupby(first_comments.prauthor).mean().sort_values("days", ascending=True).head(20)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14102564102564102, 'fraction_numerical': 0.009157509157509158, 'mean_word_length': 10.627659574468085, 'pattern_counts': {'":': 0, '<': 17, '<?xml version=': 0, '>': 17, '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': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '279746', 'license': 'Apache-2.0', 'n_tokens_mistral': 392, 'n_tokens_neox': 371, 'n_words': 62, 'path': 'github-pr-firstanswer.ipynb', 'repo_name': 'elek/ozone-stat', 'size': '1092'}