<jupyter_start><jupyter_text>Let's stalk authors!!<jupyter_code>author_list = list(chain(*[map(lambda n: n.strip(), a.split('·')) 
                           for a in events_frame['authors']]))
# Ooooo... so many papers with repeated authors...
# Or authors with repeated names...
len(author_list), len(set(author_list))<jupyter_output><empty_output><jupyter_text>Only 6 Machine Translation papers?!<jupyter_code>len([i for i in events_frame['abstract'] if 'machine translation' in i.strip()])<jupyter_output><empty_output><jupyter_text>Date munging<jupyter_code>def munge_time(s):
    time, space = s.split(' @ ')
    try: # Don't cross AM -- PM
        day, month, date, start, _, end, ampm2 = time.split()
        ampm1 = ampm2
    except ValueError: # When crossing AM -- PM
        day, month, date, start, ampm1, _, end, ampm2 = time.split()
    date = re.findall(r'\d+',date)[0]
    start_time = ' '.join([month, date, '2016', start+ampm1])
    end_time = ' '.join([month, date, '2016', end+ampm2])
    return day, start_time, end_time
s = 'Tue Dec 6th 03:00 -- 03:50 PM @ Area 1 + 2'
day, start, end = munge_time(s)
datetime.strptime(start, '%b %d %Y %I:%M%p')
print (datetime.strptime(start, '%b %d %Y %I:%M%p'))
for i, row in events_frame.iterrows():
    ##print (row.datetime)
    day, start, end = munge_time(row.datetime)
    events_frame.set_value(i,'day',day)
    events_frame.set_value(i,'start_time',start)
    events_frame.set_value(i,'end_time',end)
events_frame<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16479152878888154, 'fraction_numerical': 0.018530774321641297, 'mean_word_length': 4.286713286713287, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, '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': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '508205', 'license': 'MIT', 'n_tokens_mistral': 572, 'n_tokens_neox': 550, 'n_words': 127, 'path': 'NIPS2016.ipynb', 'repo_name': 'alvations/ann', 'size': '1511'}