It would seem that I have far too much time on my hands. After the post about a Star Trek “test”, I started wondering if there could be any data to back it up and… well here we go:
Those Old Scientists
Name |
Total Lines |
Percentage of Lines |
KIRK |
8257 |
32.89 |
SPOCK |
3985 |
15.87 |
MCCOY |
2334 |
9.3 |
SCOTT |
912 |
3.63 |
SULU |
634 |
2.53 |
UHURA |
575 |
2.29 |
CHEKOV |
417 |
1.66 |
The Next Generation
Name |
Total Lines |
Percentage of Lines |
PICARD |
11175 |
20.16 |
RIKER |
6453 |
11.64 |
DATA |
5599 |
10.1 |
LAFORGE |
3843 |
6.93 |
WORF |
3402 |
6.14 |
TROI |
2992 |
5.4 |
CRUSHER |
2833 |
5.11 |
WESLEY |
1285 |
2.32 |
Deep Space Nine
Name |
Total Lines |
Percentage of Lines |
SISKO |
8073 |
13.0 |
KIRA |
5112 |
8.23 |
BASHIR |
4836 |
7.79 |
O’BRIEN |
4540 |
7.31 |
ODO |
4509 |
7.26 |
QUARK |
4331 |
6.98 |
DAX |
3559 |
5.73 |
WORF |
1976 |
3.18 |
JAKE |
1434 |
2.31 |
GARAK |
1420 |
2.29 |
NOG |
1247 |
2.01 |
ROM |
1172 |
1.89 |
DUKAT |
1091 |
1.76 |
EZRI |
953 |
1.53 |
Voyager
Name |
Total Lines |
Percentage of Lines |
JANEWAY |
10238 |
17.7 |
CHAKOTAY |
5066 |
8.76 |
EMH |
4823 |
8.34 |
PARIS |
4416 |
7.63 |
TUVOK |
3993 |
6.9 |
KIM |
3801 |
6.57 |
TORRES |
3733 |
6.45 |
SEVEN |
3527 |
6.1 |
NEELIX |
2887 |
4.99 |
KES |
1189 |
2.06 |
Enterprise
Name |
Total Lines |
Percentage of Lines |
ARCHER |
6959 |
24.52 |
T’POL |
3715 |
13.09 |
TUCKER |
3610 |
12.72 |
REED |
2083 |
7.34 |
PHLOX |
1621 |
5.71 |
HOSHI |
1313 |
4.63 |
TRAVIS |
1087 |
3.83 |
SHRAN |
358 |
1.26 |
Discovery
Important Note: As the source material is incomplete for Discovery, the following table only includes line counts from seasons 1 and 4 along with a single episode of season 2.
Name |
Total Lines |
Percentage of Lines |
BURNHAM |
2162 |
22.92 |
SARU |
773 |
8.2 |
BOOK |
586 |
6.21 |
STAMETS |
513 |
5.44 |
TILLY |
488 |
5.17 |
LORCA |
471 |
4.99 |
TARKA |
313 |
3.32 |
TYLER |
300 |
3.18 |
GEORGIOU |
279 |
2.96 |
CULBER |
267 |
2.83 |
RILLAK |
205 |
2.17 |
DETMER |
186 |
1.97 |
OWOSEKUN |
169 |
1.79 |
ADIRA |
154 |
1.63 |
COMPUTER |
152 |
1.61 |
ZORA |
151 |
1.6 |
VANCE |
101 |
1.07 |
CORNWELL |
101 |
1.07 |
SAREK |
100 |
1.06 |
T’RINA |
96 |
1.02 |
If anyone is interested, here’s the (rather hurried, don’t judge me) Python used:
import re
from collections import defaultdict
from pathlib import Path
EPISODE_REGEX = re.compile(r"^\d+\.html?$")
LINE_REGEX = re.compile(r"^(?P<name>[A-Z']+): ")
EPISODES = Path("www.chakoteya.net")
DISCO = EPISODES / "STDisco17"
ENT = EPISODES / "Enterprise"
TNG = EPISODES / "NextGen"
TOS = EPISODES / "StarTrek"
DS9 = EPISODES / "DS9"
VOY = EPISODES / "Voyager"
NAMES = {
TOS.name: "Those Old Scientists",
TNG.name: "The Next Generation",
DS9.name: "Deep Space Nine",
VOY.name: "Voyager",
ENT.name: "Enterprise",
DISCO.name: "Discovery",
}
class CharacterLines:
def __init__(self, path: Path) -> None:
self.path = path
self.line_count = defaultdict(int)
def collect(self) -> None:
for episode in self.path.glob("*.htm*"):
if EPISODE_REGEX.match(episode.name):
for line in episode.read_text().split("\n"):
if m := LINE_REGEX.match(line):
self.line_count[m.group("name")] += 1
@property
def as_tablular_data(self) -> tuple[tuple[str, int, float], ...]:
total = sum(self.line_count.values())
r = []
for k, v in self.line_count.items():
percentage = round(v * 100 / total, 2)
if percentage > 1:
r.append((str(k), v, percentage))
return tuple(reversed(sorted(r, key=lambda _: _[2])))
def render(self) -> None:
print(f"\n\n# {NAMES[self.path.name]}\n")
print("| Name | Total Lines | Percentage of Lines |")
print("| ---------------- | :---------: | ------------------: |")
for character, total, pct in self.as_tablular_data:
print(f"| {character:16} | {total:11} | {pct:19} |")
if __name__ == "__main__":
for series in (TOS, TNG, DS9, VOY, ENT, DISCO):
counter = CharacterLines(series)
counter.collect()
counter.render()
Fascinating stuff I love that you did this. I’m surprised Morn didn’t rank higher considering how chatty he is in every scene.
Number of lines vs number of words spoken vs length of time speaking probably would have a lot of variation in results.