First coverage statistics with Python example

[LinguList]

Name	Words	Proportion
AmamiAsama	252	0.992126
AmamiYamatohama	252	0.992126
AmamiYoron	250	0.984252
Azeri	252	0.992126
Baoan	250	0.984252
BarabaTatar	210	0.826772
Bashkir	254	1
Buriat	254	1
Chuvash	248	0.976378
CodexCumanicus	214	0.84252
CrimeanTatar	248	0.976378
Dagur	251	0.988189
Dolgan	235	0.925197
Dongxian	246	0.968504
EasternEvenki	100	0.393701
Even	253	0.996063
EvenkiKamnigan	209	0.822835
Fukuoka	230	0.905512
Gagauz	250	0.984252
Gangwon	253	0.996063
Gyeonggi	254	1
Hachijo	230	0.905512
Hezhe	244	0.96063
Huzhu	250	0.984252
Hwanghae	254	1
Japanese	254	1
Jeju	252	0.992126
Jurchen	180	0.708661
Kagoshima	238	0.937008
Kalmyck	253	0.996063
Kamnigan	249	0.980315
Kangjia	239	0.940945
KarachayBalkar	250	0.984252
Karaim	252	0.992126
KaraKalpak	250	0.984252
Kazakh	250	0.984252
KazanTatar	252	0.992126
Khakas	252	0.992126
Khalaj	228	0.897638
Khalkha	253	0.996063
Kirghiz	252	0.992126
Korean	245	0.964567
Koshikiislands	236	0.929134
Kumamoto	227	0.893701
Kumyk	251	0.988189
KurUrmi	251	0.988189
LateMiddleKorean	251	0.988189
Manchu	251	0.988189
MiddleChulym	237	0.933071
MiddleMongolianMuqaddimataladab	216	0.850394
MiddleMongolianSecretHistory	196	0.771654
Minhe	241	0.948819
MiyakoIrabu	249	0.980315
Moghol	152	0.598425
NanaiBikin	254	1
NanaiMiddleAmur	253	0.996063
Negidal	250	0.984252
Nogai	252	0.992126
NorthAltai	242	0.952756
NorthernChungcheong	253	0.996063
NorthernEvenkiTura	247	0.972441
NorthernEvenkiTutonchany	217	0.854331
NorthernGyeongsang	253	0.996063
NorthernHamgyong	254	1
NorthernJeolla	253	0.996063
NorthernPyongan	254	1
Oirat	248	0.976378
OkinawaShuri	250	0.984252
OkinawaYonamine	248	0.976378
OldJapanese	250	0.984252
OldTurkic	232	0.913386
Oroch	253	0.996063
Orok	254	1
Oroqen	249	0.980315
Salar	226	0.889764
ShiraYughur	252	0.992126
Shor	249	0.980315
Solon	252	0.992126
SouthAltai	249	0.980315
SouthernChungcheong	253	0.996063
SouthernEvenkiChiringda	204	0.80315
SouthernEvenkiVershinaTuturyBaikal	54	0.212598
SouthernGyeongsang	253	0.996063
SouthernHamgyong	254	1
SouthernJeolla	253	0.996063
SouthernPyongan	254	1
StonyEvenkiPTPodkamennayaTunguska	252	0.992126
Tofa	246	0.968504
Turkish	253	0.996063
Turkmen	251	0.988189
Tuvan	247	0.972441
Udihe	253	0.996063
Ulcha	254	1
Uyghur	251	0.988189
Uzbek	248	0.976378
WestYugur	238	0.937008
Xibe	250	0.984252
YaeyamaHatoma	251	0.988189
YaeyamaIshigaki	250	0.984252
Yakut	250	0.984252
Yonaguni	251	0.988189

[LinguList]

The language coverate looks good, as far as I can tell. But there are outliers.

[LinguList]

@tpellard, the code I used for this with lingpy and tabulate (both on pip):

from lingpy import *
from tabulate import tabulate

wl = Wordlist.from_cldf('cldf/cldf-metadata.json', columns=["language_id", "parameter_name", "value", "form", "segments", "cognacy"])

table = []

for language, forms in wl.coverage().items():
    table += [[language, forms, forms/wl.height]] 

print(tabulate(table, headers=["Name", "Words", "Proportion"], tablefmt="pipe"))