<jupyter_start><jupyter_text>Sample output[[('Bolivian', '^', '0,9677'),  ('news', 'N', '0,8951'),  ('stills', 'N', '0,9608'),  ('pics', 'N', '0,9922'),  ('Air', '^', '0,9132'),  ('France', '^', '0,9988'),  ('disaster', 'N', '0,9832')],     [('Air', '^', '0,6493'),  ('France', '^', '0,9990'),  ('jet', 'N', '0,9638'),  ('crash', 'N', '0,8447'),  ('Search', 'N', '0,8679'),  ('teams', 'N', '0,9963'),  ('black', 'A', '0,9503'),  ('box', 'N', '0,9922'),  ('signals', 'N', '0,5163')],     [('News', 'N', '0,5745'),  ('Outlets', 'N', '0,9900'),  ('Shocking', 'A', '0,9778'),  ('Air', '^', '0,7680'),  ('France', '^', '0,9988'),  ('Crash', 'N', '0,7106'),  ('Pictures', 'N', '0,9732'),  ('Stills', 'N', '0,6595')]    ]<jupyter_code>tags_meaning={
  "N":"common noun",
  "O":"pronoun (personal/WH; not possessive)",  
  "^":"proper noun",  
  "S":"nominal + possessive(lady's)",
  "Z":"proper noun + possessive (Palin's, Alaska's)",  

  "V":"verb incl. copula, auxiliaries",
  "A":"adjective",
  "R":"adverb",
  "!":"interjection",

  "D":"determiner",
  "P":"pre- or postposition, or subordinating conjunction",
  "&":"coordinating conjunction",
  "T":"verb particle",
  "X":"existential _there_, predeterminers",

  "#":"hashtag (indicates topic/category for tweet)", 
  "@":"at-mention (indicates another user as a recipient of a tweet)",
  "~":"discourse marker, indications of continuation of a message across multiple tweets",
  "U":"URL or email address",
  "E":"emoticon",
 
  "$":"numeral",
  ",":"punctuation",
  "G":"other abbreviations, foreign words, possessive endings, symbols, garbage",
 
  "L":"nominal + verbal (e.g. _i'm_), verbal + nominal (_let's_, _lemme_)",
  "M":"proper noun + verbal",
  "Y":" 'X' + verbal"
}
tags_to_keep = [ 'N','^','S','Z','A','#','$']
from prettytable import PrettyTable
x = PrettyTable()

x.field_names = ["Symbol","Meaning"]
for t in tags_meaning.items():
    #print t[0],t[1]
    #x.append()
    x.add_row([t[0],t[1]])
print x
from prettytable import PrettyTable
x = PrettyTable()

x.field_names = ["Symbol","Meaning"]
for t in tags_meaning.items():
    if t[0] in tags_to_keep:
        x.add_row([t[0],t[1]])
print x<jupyter_output>+--------+----------------------------------------------+
| Symbol |                   Meaning                    |
+--------+----------------------------------------------+
|   #    | hashtag (indicates topic/category for tweet) |
|   $    |                   numeral                    |
|   A    |                  adjective                   |
|   N    |                 common noun                  |
|   S    |         nominal + possessive(lady's)         |
|   Z    | proper noun + possessive (Palin's, Alaska's) |
|   ^    |                 proper noun                  |
+--------+----------------------------------------------+
{'fraction_non_alphanumeric': 0.2905861456483126, 'fraction_numerical': 0.045115452930728245, 'mean_word_length': 2.533249686323714, 'pattern_counts': {'":': 25, '<': 4, '<?xml version=': 0, '>': 4, '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': '646410', 'license': 'Apache-2.0', 'n_tokens_mistral': 1102, 'n_tokens_neox': 993, 'n_words': 222, 'path': 'TweetParser.ipynb', 'repo_name': 'BaCk7/rumor-tracker', 'size': '2815'}