belarus-osm-name-statistic.md

Аналіз дадзеных тэгу name у ОСМ Беларусі

Зьмест

• Праблематыка
• Спампуем дамп ОСМ
• Усталёўваем залежанасьцьі
• Пошук сувязей дзеля падтрыманьня спасылачнай цэласнасьці

Праблематыка

У беларускім ОСМ шырока выкарыстоўваюцца беларуская і расейская мова, для іх ёсьць адпаведнікі name:be і name:ru, таксама мовы выкарыстоўваюцца ў агульных тэгах як name, addr:* і іншых. Праблематка выкарыстоўваньня аднае, ці іншае, ці абедзьвух моваў апісанае тут https://wiki.openstreetmap.org/wiki/BE:Belarus_language_issues. Незалежна ад варыянту выкарыстоўваньня мовы павінны вытрымлівацца наступныя правілы: пошук на любое мове мусіць працаваць, павінна быць магчымасьць паказываць подпісы на любой мове (ці ў арыгінале, але гэтае правіла зараз не выконваецца), павінна захоўвацца спасылкавая цэласнасьць (што можа ўплываць на папярэднія два пункты).

Гэты аналіз ставіць мэтаю знайсьці адпаведныя катэгорыі і тэгі якія ўтрымліваюць кірылічныя значэньні тэгу name і падлічыць запаўняльнасьць тэгаў name:be, name:ru.

Спампуем дамп ОСМ

# !rm belarus-latest.osm.pbf
!wget --backups=1 -N https://download.geofabrik.de/europe/belarus-latest.osm.pbf
!cp belarus-latest.osm.pbf belarus-updated.osm.pbf
#!osmium fileinfo -e belarus-latest.osm.pbf
info = !osmium fileinfo -e belarus-latest.osm.pbf

--2022-06-19 11:39:53--  https://download.geofabrik.de/europe/belarus-latest.osm.pbf
Resolving download.geofabrik.de (download.geofabrik.de)... 116.202.112.212, 95.216.28.113
Connecting to download.geofabrik.de (download.geofabrik.de)|116.202.112.212|:443... connected.
HTTP request sent, awaiting response... 304 Not Modified
File ‘belarus-latest.osm.pbf’ not modified on server. Omitting download.

largest_node_id = [int(l.split(':')[-1].strip()) for l in info if 'Largest node ID' in l][0]
largest_way_id = [int(l.split(':')[-1].strip()) for l in info if 'Largest way ID' in l][0]
largest_rel_id = [int(l.split(':')[-1].strip()) for l in info if 'Largest relation ID' in l][0]

try:
    from belarus_utils import OverpassApiSearchEnigne
    overpass_api = OverpassApiSearchEnigne(cache=True)
    osc = overpass_api.get_updates_osc(largest_node_id, largest_way_id, largest_rel_id)
    with open('belarus-latest.osc', 'w') as h:
        h.write(osc)
except Exception as err:
#     print(err)
    raise err

!osmium apply-changes --overwrite -o belarus-updated.osm.pbf belarus-latest.osm.pbf belarus-latest.osc

[======================================================================] 100% =================================================>                    ]  70% 

Загрузім дамп у postgis

• патрэбна толькі калі хочам атрымаць больш дакладныя дадзеныя, але можа не ўтрымліваць некаторыя дачыненьні

!PGPASSWORD=$POSTGRES_PASSWORD psql -h $POSTGRES_HOST -p $POSTGRES_POST -U $POSTGRES_USER -d $POSTGRES_DB  -c "CREATE EXTENSION IF NOT EXISTS hstore"
!PGPASSWORD=$POSTGRES_PASSWORD psql -h $POSTGRES_HOST -p $POSTGRES_POST -U $POSTGRES_USER -d $POSTGRES_DB  -c "CREATE EXTENSION IF NOT EXISTS postgis"
!PGPASSWORD=$POSTGRES_PASSWORD psql -h $POSTGRES_HOST -p $POSTGRES_POST -U $POSTGRES_USER -d $POSTGRES_DB  -c "DROP MATERIALIZED VIEW IF EXISTS planet_osm_region CASCADE"

NOTICE:  extension "hstore" already exists, skipping
CREATE EXTENSION
NOTICE:  extension "postgis" already exists, skipping
CREATE EXTENSION
NOTICE:  drop cascades to materialized view planet_osm_named_data
DROP MATERIALIZED VIEW

!PGPASSWORD=$POSTGRES_PASSWORD osm2pgsql -H $POSTGRES_HOST -P $POSTGRES_POST -U $POSTGRES_USER -d $POSTGRES_DB -v -l -j -G -x --hstore-add-index -C $OSM2PGSQL_CACHE -S /usr/share/osm2pgsql/default.style belarus-updated.osm.pbf

2022-06-19 11:40:23  osm2pgsql version 1.6.0
2022-06-19 11:40:23  [0] Database version: 14.3
2022-06-19 11:40:23  [0] PostGIS version: 3.2
2022-06-19 11:40:23  [0] Reading file: belarus-updated.osm.pbf
2022-06-19 11:40:23  [0] Started pool with 4 threads.
2022-06-19 11:40:23  [0] Using projection SRS 4326 (Latlong)
2022-06-19 11:40:23  [0] Using built-in tag transformations
2022-06-19 11:40:23  [0] Middle 'ram' options:
2022-06-19 11:40:23  [0]   locations: true
2022-06-19 11:40:23  [0]   way_nodes: true
2022-06-19 11:40:23  [0]   nodes: false
2022-06-19 11:40:23  [0]   untagged_nodes: true
2022-06-19 11:40:23  [0]   ways: false
2022-06-19 11:40:23  [0]   relations: false
2022-06-19 11:40:23  [0] Setting up table 'planet_osm_point'
2022-06-19 11:40:23  [0] Setting up table 'planet_osm_line'
2022-06-19 11:40:23  [0] Setting up table 'planet_osm_polygon'
2022-06-19 11:40:23  [0] Setting up table 'planet_osm_roads'
2022-06-19 11:41:50  [0] Reading input files done in 87s (1m 27s).                        
2022-06-19 11:41:50  [0]   Processed 31134348 nodes in 29s - 1074k/s
2022-06-19 11:41:50  [0]   Processed 4395479 ways in 52s - 85k/s
2022-06-19 11:41:50  [0]   Processed 65014 relations in 6s - 11k/s
2022-06-19 11:41:50  [0] Overall memory usage: peak=1420MByte current=1380MByte
2022-06-19 11:41:51  [0] Middle 'ram': Node locations: size=31134348 bytes=296M
2022-06-19 11:41:51  [0] Middle 'ram': Way nodes data: size=80795694 capacity=125829120 bytes=120M
2022-06-19 11:41:51  [0] Middle 'ram': Way nodes index: size=4395479 capacity=7340032 bytes=56M
2022-06-19 11:41:51  [0] Middle 'ram': Object data: size=0 capacity=1048576 bytes=1M
2022-06-19 11:41:51  [0] Middle 'ram': Object indexes: size=0 capacity=0 bytes=0M
2022-06-19 11:41:51  [0] Middle 'ram': Memory used overall: 473MBytes
2022-06-19 11:41:51  [1] Starting task...
2022-06-19 11:41:51  [3] Starting task...
2022-06-19 11:41:51  [2] Starting task...
2022-06-19 11:41:51  [4] Starting task...
2022-06-19 11:41:51  [1] Clustering table 'planet_osm_point' by geometry...
2022-06-19 11:41:51  [3] Clustering table 'planet_osm_polygon' by geometry...
2022-06-19 11:41:51  [2] Clustering table 'planet_osm_line' by geometry...
2022-06-19 11:41:51  [4] Clustering table 'planet_osm_roads' by geometry...
2022-06-19 11:41:51  [3] Using native order for clustering table 'planet_osm_polygon'
2022-06-19 11:41:51  [2] Using native order for clustering table 'planet_osm_line'
2022-06-19 11:41:51  [1] Using native order for clustering table 'planet_osm_point'
2022-06-19 11:41:51  [4] Using native order for clustering table 'planet_osm_roads'
2022-06-19 11:41:57  [4] Creating geometry index on table 'planet_osm_roads'...
2022-06-19 11:42:00  [4] Creating hstore indexes on table 'planet_osm_roads'...
2022-06-19 11:42:08  [1] Creating geometry index on table 'planet_osm_point'...
2022-06-19 11:42:11  [4] Analyzing table 'planet_osm_roads'...
2022-06-19 11:42:14  [4] Done task in 22402ms.
2022-06-19 11:42:24  [2] Creating geometry index on table 'planet_osm_line'...
2022-06-19 11:42:26  [1] Creating hstore indexes on table 'planet_osm_point'...
2022-06-19 11:42:51  [3] Creating geometry index on table 'planet_osm_polygon'...
2022-06-19 11:42:55  [1] Analyzing table 'planet_osm_point'...
2022-06-19 11:42:57  [1] Done task in 66251ms.
2022-06-19 11:42:57  [0] All postprocessing on table 'planet_osm_point' done in 66s (1m 6s).
2022-06-19 11:42:58  [2] Creating hstore indexes on table 'planet_osm_line'...
2022-06-19 11:43:37  [2] Analyzing table 'planet_osm_line'...
2022-06-19 11:43:38  [2] Done task in 107218ms.
2022-06-19 11:43:38  [0] All postprocessing on table 'planet_osm_line' done in 107s (1m 47s).
2022-06-19 11:44:05  [3] Creating hstore indexes on table 'planet_osm_polygon'...
2022-06-19 11:45:31  [3] Analyzing table 'planet_osm_polygon'...
2022-06-19 11:45:33  [3] Done task in 221919ms.
2022-06-19 11:45:33  [0] All postprocessing on table 'planet_osm_polygon' done in 221s (3m 41s).
2022-06-19 11:45:33  [0] All postprocessing on table 'planet_osm_roads' done in 22s.
2022-06-19 11:45:33  [0] Overall memory usage: peak=1420MByte current=744MByte
2022-06-19 11:45:33  [0] osm2pgsql took 310s (5m 10s) overall.

Усталюем залежнасьці

!pip install pandas matplotlib psycopg2-binary https://github.com/lechup/imposm-parser/archive/python3.zip

Вызначым катэгорыі

import os
import re
from collections import defaultdict, Counter

from imposm.parser import OSMParser
import psycopg2

import pandas as pd

pd.set_option('display.max_rows', None)
pd.set_option('display.float_format', '{:.3f}'.format)


cirylic_chars = frozenset('абвгдеёжзіийклмнопрстуўфхцчшщьыъэюяАБВГДЕЁЖЗІИІЙКЛМНОПРСТУФХЦЧШЩЬЫЪЭЮЯ')

categories_rules = {
    'admin': [
        ['admin_level', '2'],
        ['admin_level', '4'],
        ['admin_level', '6'],
        ['admin_level', '8'],
        ['admin_level', '9'],
    ],
    'place': [
        ['place', 'city'],
        ['place', 'town'],
        ['place', 'village'],
        ['place', 'hamlet'],
        ['place', 'isolated_dwelling'],
        ['admin_level', None],
        ['boundary', 'administrative'],
        ['traffic_sign', 'city_limit'],
    ],
    'allotments': [
        ['place', 'allotments'],
        ['landuse', 'allotments'],
    ],
    'locality': [
        ['place', 'locality'],
        ['abandoned:place', None],
    ],
    'suburb': [
        ['landuse', 'commercial'],
        ['landuse', 'construction'],
        ['landuse', 'industrial'],
        ['landuse', 'residential'],
        ['landuse', 'retail'],
        ['place', None],
        ['residential', None],
        ['industrial', None],
    ],
    'highway': [
        ['highway', 'motorway'],
        ['highway', 'trunk'],
        ['highway', 'primary'],
        ['highway', 'secondary'],
        ['highway', 'tertiary'],
        ['highway', 'unclassified'],
        ['highway', 'residential'],
        ['highway', 'service'],
        ['highway', 'track'],
        ['highway', None],
        ['type', 'associatedStreet'],
        ['type', 'street'],
    ],
    'public_transport': [
        ['highway', 'bus_stop'],
        ['public_transport', None],
        ['route', None],
        ['type', 'route'],
        ['railway', None],
        ['type', 'route_master'],
        ['route_master', None],
    ],
    'infrastructure': [
        ['tunnel', None],
        ['barrier', None],
        ['power', None],
        ['bridge', None],
        ['substation', None],
        ['emergency', None],
        ['ele', None],
        ['man_made', None],
        ['embankment', None],
    ],
    'religion': [
        ['religion', None],
        ['amenity', 'place_of_worship'],
        ['amenity', 'monastery'],
        ['building', 'church'],
        ['building', 'cathedral'],
        ['building', 'chapel'],
    ],
    'education': [
        ['landuse', 'education'],
        ['amenity', 'university'],
        ['amenity', 'college'],
        ['amenity', 'school'],
        ['amenity', 'kindergarten'],
        ['building', 'university'],
        ['building', 'college'],
        ['building', 'school'],
        ['building', 'kindergarten'],
    ],
    'healthcare': [
        ['healthcare', None],
        ['amenity', 'hospital'],
        ['amenity', 'pharmacy'],
        ['amenity', 'clinic'],
        ['amenity', 'doctors'],
        ['amenity', 'dentist'],
        ['building', 'hospital'],
        ['building', 'clinic'],
    ],
    'government': [
        ['amenity', 'post_office'],
        ['amenity', 'police'],
        ['amenity', 'library'],
        ['office', 'government'],
        ['government', None],
        ['landuse', 'military'],
        ['military', None],
    ],
    'office': [
        ['office', None],
    ],
    'tourism': [
        ['tourism', None],
        ['historic', None],
        ['memorial', None],
        ['ruins', None],
        ['information', None],
        ['attraction', None],
        ['resort', None],
        ['artwork_type', None],
    ],
    'amenity': [
        ['amenity', 'cafe'],
        ['amenity', 'atm'],
        ['amenity', 'bank'],
        ['amenity', 'fast_food'],
        ['amenity', 'fuel'],
        ['amenity', 'community_centre'],
        ['amenity', 'restaurant'],
        ['amenity', 'bar'],
        ['amenity', None],
        ['shop', 'convenience'],
        ['shop', 'clothes'],
        ['shop', 'car_repair'],
        ['shop', 'hairdresser'],
        ['shop', 'chemist'],
        ['shop', 'supermarket'],
        ['shop', 'car_parts'],
        ['shop', 'furniture'],
        ['shop', 'hardware'],
        ['shop', 'kiosk'],
        ['shop', 'doityourself'],
        ['shop', 'pet'],
        ['shop', 'florist'],
        ['shop', 'beauty'],
        ['shop', 'mobile_phone'],
        ['shop', 'shoes'],
        ['shop', 'newsagent'],
        ['shop', 'electronics'],
        ['shop', 'alcohol'],
        ['shop', 'jewelry'],
        ['shop', 'mall'],
        ['shop', 'butcher'],
        ['shop', 'cosmetics'],
        ['shop', None],
        ['leisure', None],
        ['sport', None],
        ['craft', 'shoemaker'],
        ['clothes', None],
    ],
    'building': [
        ['building', 'industrial'],
        ['building', 'service'],
        ['building', 'retail'],
        ['building', 'commercial'],
        ['building', 'warehouse'],
        ['building', 'public'],
        ['building', 'dormitory'],
        ['building', 'warehouse'],
        ['building', None],
    ],
    'water': [
        ['waterway', 'drain'],
        ['waterway', 'ditch'],
        ['waterway', 'stream'],
        ['waterway', 'river'],
        ['waterway', 'canal'],
        ['waterway', None],
        ['type', 'waterway'],
        ['water', None],
        ['natural', 'water'],
        ['natural', 'spring'],
    ],
    'natural': [
        ['boundary', None],
        ['natural', None],
        ['place', 'island'],
        ['place', 'islet'],
        ['landuse', None],
    ],
}

dependants = [
    'addr:region',
    'addr:district',
    'addr:subdistrict',
    'addr:city',
    'addr:place',
    'addr:street',
    'addr2:street',
    
    'from',
    'to',
    'via',
    'destination',
    'destination:backward',
    'destination:forward',
    
    'water_tank:city',
]

usage = defaultdict(set)
categories_rules2 = {}
for category, group in categories_rules.items():
    if category not in categories_rules2:
        categories_rules2[category] = []
    for tag, value in group:
        if value is not None:
            categories_rules2[category].append([tag, True, {value}])
            usage[tag].add(value)                          
for category, group in categories_rules.items():
    if category not in categories_rules2:
        categories_rules2[category] = []
    for tag, value in group:
        if value is None:
            categories_rules2[category].append([tag, False, usage[tag]])

Падлічам статыстыку для дампу

• дамп падліча ўсе дадзеныя, але можа быць трошкі недакладным таму што не ўлічвае грубую абрэзку Беларусі

key_counter = defaultdict(lambda: defaultdict(list))

categories_tags = {}
categories_rules_tags_set = {}
for category, group in categories_rules2.items():
    for tag, eq, values in group:
        if tag not in categories_tags:
            categories_tags[tag] = {category}
        else:
            categories_tags[tag].add(category)

          
def process(params):
    for _, tags, _ in params:
        if 'name' not in tags:
            continue
        if not (frozenset(tags['name']) & cirylic_chars):
            continue
        categories = {category for tag in categories_tags.keys() & tags.keys() for category in categories_tags[tag]}
        for tag in ['name', 'name:be', 'name:ru']:
            if tag not in tags:
                continue
            value = tags[tag]
            cyr = frozenset(value) & cirylic_chars
            if not cyr:
                continue
            match = False
            for category in categories:
                group = categories_rules2[category]
                category_match = False
                for i, (k, eq, vv) in enumerate(group):
                    if k not in tags:
                        continue
                    if eq:
                        if tags[k] in vv:
                            if not category_match:
                                key_counter[(category,)][tag].append(value)
                                match = category_match = True
                            key_counter[(category, i)][tag].append(value)   
                    else:
                        if tags[k] not in vv:
                            if not category_match:
                                key_counter[(category,)][tag].append(value)
                                match = category_match = True
                            key_counter[(category, i)][tag].append(value)   
            if not match:
                key_counter[('other',)][tag].append(value)
                

OSMParser(
    nodes_callback=process,
    ways_callback=process,
    relations_callback=process,
).parse('belarus-latest.osm.pbf')

data = []
for c in list(categories_rules) + ['other']:
    name_cnt = len(key_counter[(c,)]['name'])
    name_uniq = len(set(key_counter[(c,)]['name']))
    name_be_cnt = len(key_counter[(c,)]['name:be'])
    name_be_uniq = len(set(key_counter[(c,)]['name:be']))
    name_ru_cnt = len(key_counter[(c,)]['name:ru'])
    name_ru_uniq = len(set(key_counter[(c,)]['name:ru']))
    data.append([
        '#', c, 
        name_cnt, name_be_cnt, name_ru_cnt, name_be_cnt/(name_cnt or 1), name_ru_cnt/(name_cnt or 1),
        name_uniq, name_be_uniq, name_ru_uniq, name_be_uniq/(name_uniq or 1), name_ru_uniq/(name_uniq or 1),
    ])
    if c == 'other':
        continue
    for i, (k, eq, vv) in enumerate(categories_rules2[c]):
        if eq:
            tag = f'{k} = {list(vv)[0]}'
        else:
            tag = f'{k} = *'
        name_cnt = len(key_counter[(c, i)]['name'])
        name_uniq = len(set(key_counter[(c, i)]['name']))
        name_be_cnt = len(key_counter[(c, i)]['name:be'])
        name_be_uniq = len(set(key_counter[(c, i)]['name:be']))
        name_ru_cnt = len(key_counter[(c, i)]['name:ru'])
        name_ru_uniq = len(set(key_counter[(c, i)]['name:ru']))
        data.append([
            '', tag, 
            name_cnt, name_be_cnt, name_ru_cnt, name_be_cnt/(name_cnt or 1), name_ru_cnt/(name_cnt or 1),
            name_uniq, name_be_uniq, name_ru_uniq, name_be_uniq/(name_uniq or 1), name_ru_uniq/(name_uniq or 1),
        ])


df = pd.DataFrame(data, columns=[
    'lvl', 'category', 
    'all name', 'all name:be', 'all name:ru', 'all name:be%', 'all name:ru%',
    'uniq name', 'uniq name:be', 'uniq name:ru', 'uniq name:be%', 'uniq name:ru%',
])
(
    df
    .style
    .set_properties(subset=['category'], **{'text-align': 'left'})
    .background_gradient('YlOrRd', subset=[c for c in df.columns if c.endswith('%')], vmin=0, vmax=1)
    .format({f: '{:.3f}' for f in [c for c in df.columns if c.endswith('%')]})
    .apply(lambda row: [("font-weight: bold" if row.loc['lvl'] == '#' else '') for _ in row], axis=1)
)

	lvl	category	all name	all name:be	all name:ru	all name:be%	all name:ru%	uniq name	uniq name:be	uniq name:ru	uniq name:be%	uniq name:ru%
0	#	admin	2389	2260	2107	0.946	0.882	1571	1482	1487	0.943	0.947
1		admin_level = 2	703	640	479	0.910	0.681	109	84	86	0.771	0.789
2		admin_level = 4	73	73	73	1.000	1.000	40	39	39	0.975	0.975
3		admin_level = 6	234	215	222	0.919	0.949	189	170	177	0.899	0.937
4		admin_level = 8	1352	1306	1307	0.966	0.967	1234	1195	1192	0.968	0.966
5		admin_level = 9	27	26	26	0.963	0.963	14	13	13	0.929	0.929
6	#	place	49941	49526	49404	0.992	0.989	17290	17069	16934	0.987	0.979
7		place = city	31	31	31	1.000	1.000	16	16	16	1.000	1.000
8		place = town	274	273	274	0.996	1.000	138	138	138	1.000	1.000
9		place = village	5030	4990	5013	0.992	0.997	2229	2209	2214	0.991	0.993
10		place = hamlet	38814	38633	38638	0.995	0.995	13589	13643	13513	1.004	0.994
11		place = isolated_dwelling	1272	1260	1262	0.991	0.992	644	638	639	0.991	0.992
12		boundary = administrative	25046	24834	24681	0.992	0.985	16981	16965	16823	0.999	0.991
13		traffic_sign = city_limit	2064	2052	2052	0.994	0.994	1003	932	926	0.929	0.923
14		admin_level = *	22653	22571	22571	0.996	0.996	15487	15568	15420	1.005	0.996
15	#	allotments	2872	2469	2865	0.860	0.998	1969	1560	1961	0.792	0.996
16		place = allotments	1834	1833	1834	0.999	1.000	1326	1292	1324	0.974	0.998
17		landuse = allotments	2698	2295	2691	0.851	0.997	1920	1510	1913	0.786	0.996
18	#	locality	12026	11894	11946	0.989	0.993	8437	8250	8384	0.978	0.994
19		place = locality	11966	11834	11886	0.989	0.993	8403	8212	8350	0.977	0.994
20		abandoned:place = *	3038	3028	3031	0.997	0.998	2206	2204	2201	0.999	0.998
21	#	suburb	7959	3963	7824	0.498	0.983	6322	2674	6200	0.423	0.981
22		landuse = commercial	179	96	177	0.536	0.989	166	84	164	0.506	0.988
23		landuse = construction	162	30	158	0.185	0.975	154	28	150	0.182	0.974
24		landuse = industrial	4383	1050	4331	0.240	0.988	3778	696	3741	0.184	0.990
25		landuse = residential	875	662	835	0.757	0.954	794	585	751	0.737	0.946
26		landuse = retail	233	140	230	0.601	0.987	148	59	146	0.399	0.986
27		place = *	2146	2108	2113	0.982	0.985	1502	1486	1471	0.989	0.979
28		residential = *	701	568	677	0.810	0.966	637	498	609	0.782	0.956
29		industrial = *	474	78	466	0.165	0.983	383	56	376	0.146	0.982
30	#	highway	94598	92170	94294	0.974	0.997	12175	10665	12070	0.876	0.991
31		highway = motorway	76	76	76	1.000	1.000	1	1	1	1.000	1.000
32		highway = trunk	1327	1285	1286	0.968	0.969	140	134	135	0.957	0.964
33		highway = primary	7670	7576	7631	0.988	0.995	532	509	522	0.957	0.981
34		highway = secondary	7261	7233	7261	0.996	1.000	748	729	747	0.975	0.999
35		highway = tertiary	10684	10434	10631	0.977	0.995	1684	1527	1653	0.907	0.982
36		highway = unclassified	3018	2893	2947	0.959	0.976	931	859	912	0.923	0.980
37		highway = residential	56704	55671	56666	0.982	0.999	9718	8937	9692	0.920	0.997
38		highway = service	3189	2926	3181	0.918	0.997	1572	1346	1564	0.856	0.995
39		highway = track	792	688	781	0.869	0.986	214	152	204	0.710	0.953
40		type = associatedStreet	1821	1819	1821	0.999	1.000	1174	1167	1173	0.994	0.999
41		type = street	37	37	37	1.000	1.000	35	35	35	1.000	1.000
42		highway = *	2019	1532	1976	0.759	0.979	1004	750	997	0.747	0.993
43	#	public_transport	34834	29185	34023	0.838	0.977	14348	9813	13105	0.684	0.913
44		highway = bus_stop	17464	15418	17367	0.883	0.994	8493	6686	7918	0.787	0.932
45		type = route	3995	2096	3920	0.525	0.981	3966	2086	3893	0.526	0.982
46		type = route_master	591	100	122	0.169	0.206	542	98	119	0.181	0.220
47		public_transport = *	24880	22610	24676	0.909	0.992	7884	6289	7269	0.798	0.922
48		route = *	4047	2126	3954	0.525	0.977	4004	2103	3915	0.525	0.978
49		railway = *	2886	2491	2866	0.863	0.993	1358	1199	1314	0.883	0.968
50		route_master = *	598	100	124	0.167	0.207	549	98	121	0.179	0.220
51	#	infrastructure	18752	11072	18405	0.590	0.981	10412	5336	10105	0.512	0.971
52		tunnel = *	5843	4529	5826	0.775	0.997	1660	1221	1644	0.736	0.990
53		barrier = *	4477	2935	4424	0.656	0.988	3735	2252	3679	0.603	0.985
54		power = *	4864	2326	4731	0.478	0.973	3972	1851	3867	0.466	0.974
55		bridge = *	1363	1198	1357	0.879	0.996	497	409	491	0.823	0.988
56		substation = *	2566	1481	2532	0.577	0.987	2404	1349	2377	0.561	0.989
57		emergency = *	1155	159	1153	0.138	0.998	288	122	285	0.424	0.990
58		ele = *	311	161	308	0.518	0.990	285	138	282	0.484	0.989
59		man_made = *	1679	549	1549	0.327	0.923	1217	328	1100	0.270	0.904
60		embankment = *	213	138	213	0.648	1.000	90	72	90	0.800	1.000
61	#	religion	3825	2703	3706	0.707	0.969	2319	1209	2176	0.521	0.938
62		amenity = place_of_worship	2686	1769	2642	0.659	0.984	1886	988	1828	0.524	0.969
63		amenity = monastery	21	13	18	0.619	0.857	20	12	17	0.600	0.850
64		building = church	1434	987	1398	0.688	0.975	1107	633	1064	0.572	0.961
65		building = cathedral	25	21	25	0.840	1.000	24	21	24	0.875	1.000
66		building = chapel	267	197	261	0.738	0.978	163	96	153	0.589	0.939
67		religion = *	3656	2610	3564	0.714	0.975	2211	1171	2095	0.530	0.948
68	#	education	5654	3236	4737	0.572	0.838	4048	1853	3123	0.458	0.771
69		landuse = education	805	792	805	0.984	1.000	798	783	798	0.981	1.000
70		amenity = university	200	145	128	0.725	0.640	188	134	119	0.713	0.633
71		amenity = college	321	153	180	0.477	0.561	295	139	159	0.471	0.539
72		amenity = school	1995	1173	1957	0.588	0.981	1438	625	1377	0.435	0.958
73		amenity = kindergarten	1903	1281	1884	0.673	0.990	1367	829	1327	0.606	0.971
74		building = university	165	65	55	0.394	0.333	157	61	53	0.389	0.338
75		building = college	116	49	54	0.422	0.466	103	41	43	0.398	0.417
76		building = school	672	231	327	0.344	0.487	569	136	228	0.239	0.401
77		building = kindergarten	369	171	226	0.463	0.612	274	96	136	0.350	0.496
78	#	healthcare	5663	2314	4684	0.409	0.827	2999	801	2112	0.267	0.704
79		amenity = hospital	653	295	501	0.452	0.767	500	182	350	0.364	0.700
80		amenity = pharmacy	2502	1243	2172	0.497	0.868	1308	353	1045	0.270	0.799
81		amenity = clinic	734	361	600	0.492	0.817	445	159	316	0.357	0.710
82		amenity = doctors	931	112	831	0.120	0.893	257	60	162	0.233	0.630
83		amenity = dentist	373	111	261	0.298	0.700	291	37	177	0.127	0.608
84		building = hospital	405	177	237	0.437	0.585	297	93	134	0.313	0.451
85		building = clinic	31	16	22	0.516	0.710	29	14	20	0.483	0.690
86		healthcare = *	2713	992	2669	0.366	0.984	1419	456	1400	0.321	0.987
87	#	government	4980	1936	3936	0.389	0.790	3401	878	2609	0.258	0.767
88		amenity = post_office	1083	538	593	0.497	0.548	405	115	132	0.284	0.326
89		amenity = police	710	308	418	0.434	0.589	518	157	249	0.303	0.481
90		amenity = library	499	249	261	0.499	0.523	336	116	111	0.345	0.330
91		office = government	1888	592	1872	0.314	0.992	1565	368	1547	0.235	0.988
92		landuse = military	366	139	363	0.380	0.992	320	108	317	0.338	0.991
93		government = *	733	321	726	0.438	0.990	579	195	570	0.337	0.984
94		military = *	566	174	560	0.307	0.989	382	68	376	0.178	0.984
95	#	office	5276	1471	5096	0.279	0.966	3872	521	3713	0.135	0.959
96		office = *	5276	1471	5096	0.279	0.966	3872	521	3713	0.135	0.959
97	#	tourism	15315	7732	14262	0.505	0.931	10936	4279	9942	0.391	0.909
98		tourism = *	9781	5548	9247	0.567	0.945	7045	3196	6523	0.454	0.926
99		historic = *	6028	2580	5509	0.428	0.914	4444	1505	3962	0.339	0.892
100		memorial = *	2313	641	1965	0.277	0.850	1859	405	1558	0.218	0.838
101		ruins = *	263	86	263	0.327	1.000	208	75	207	0.361	0.995
102		information = *	999	782	970	0.783	0.971	297	120	270	0.404	0.909
103		attraction = *	173	79	164	0.457	0.948	151	64	139	0.424	0.921
104		resort = *	142	29	140	0.204	0.986	134	27	132	0.201	0.985
105		artwork_type = *	753	278	630	0.369	0.837	661	239	547	0.362	0.828
106	#	amenity	48078	22613	29658	0.470	0.617	21567	4629	7719	0.215	0.358
107		amenity = cafe	2463	1058	1347	0.430	0.547	1702	497	714	0.292	0.420
108		amenity = atm	1454	1366	1404	0.939	0.966	109	61	78	0.560	0.716
109		amenity = bank	2075	1817	1912	0.876	0.921	324	115	213	0.355	0.657
110		amenity = fast_food	758	354	420	0.467	0.554	425	98	148	0.231	0.348
111		amenity = fuel	1137	635	764	0.558	0.672	626	244	313	0.390	0.500
112		amenity = community_centre	742	343	457	0.462	0.616	353	92	133	0.261	0.377
113		amenity = restaurant	603	297	338	0.493	0.561	515	229	267	0.445	0.518
114		amenity = bar	471	161	221	0.342	0.469	422	127	183	0.301	0.434
115		shop = convenience	7311	4100	5387	0.561	0.737	2471	836	1152	0.338	0.466
116		shop = clothes	1120	302	509	0.270	0.454	673	113	217	0.168	0.322
117		shop = car_repair	1133	313	432	0.276	0.381	882	90	198	0.102	0.224
118		shop = hairdresser	1075	403	550	0.375	0.512	718	151	254	0.210	0.354
119		shop = chemist	1478	1074	1308	0.727	0.885	87	42	40	0.483	0.460
120		shop = supermarket	1250	861	1018	0.689	0.814	377	177	226	0.469	0.599
121		shop = car_parts	911	248	454	0.272	0.498	486	44	133	0.091	0.274
122		shop = furniture	745	248	408	0.333	0.548	398	59	119	0.148	0.299
123		shop = hardware	724	263	399	0.363	0.551	519	117	211	0.225	0.407
124		shop = kiosk	395	223	258	0.565	0.653	205	82	92	0.400	0.449
125		shop = doityourself	723	226	328	0.313	0.454	493	85	144	0.172	0.292
126		shop = pet	475	119	178	0.251	0.375	210	31	61	0.148	0.290
127		shop = florist	525	211	267	0.402	0.509	262	52	87	0.198	0.332
128		shop = beauty	481	70	157	0.146	0.326	434	64	134	0.147	0.309
129		shop = mobile_phone	503	226	374	0.449	0.744	112	19	40	0.170	0.357
130		shop = shoes	258	108	133	0.419	0.516	152	28	53	0.184	0.349
131		shop = newsagent	535	378	433	0.707	0.809	65	24	29	0.369	0.446
132		shop = electronics	449	150	246	0.334	0.548	304	57	118	0.188	0.388
133		shop = alcohol	426	123	185	0.289	0.434	194	44	65	0.227	0.335
134		shop = jewelry	342	133	151	0.389	0.442	81	25	33	0.309	0.407
135		shop = mall	369	180	211	0.488	0.572	304	133	154	0.438	0.507
136		shop = butcher	390	133	205	0.341	0.526	219	59	76	0.269	0.347
137		shop = cosmetics	280	97	147	0.346	0.525	148	44	67	0.297	0.453
138		craft = shoemaker	225	160	162	0.711	0.720	64	11	24	0.172	0.375
139		amenity = *	6436	2698	3801	0.419	0.591	4328	1334	2077	0.308	0.480
140		shop = *	7097	2470	3495	0.348	0.492	4016	788	1285	0.196	0.320
141		leisure = *	3252	1369	2012	0.421	0.619	2386	772	1267	0.324	0.531
142		sport = *	779	317	364	0.407	0.467	589	170	212	0.289	0.360
143		clothes = *	228	91	123	0.399	0.539	130	29	46	0.223	0.354
144	#	building	30254	11353	17379	0.375	0.574	17824	4405	8422	0.247	0.473
145		building = industrial	1617	287	779	0.177	0.482	1230	146	470	0.119	0.382
146		building = service	1380	690	1086	0.500	0.787	1098	540	848	0.492	0.772
147		building = retail	1947	1152	1369	0.592	0.703	1132	501	642	0.443	0.567
148		building = commercial	593	189	285	0.319	0.481	541	154	244	0.285	0.451
149		building = warehouse	242	52	83	0.215	0.343	172	17	35	0.099	0.203
150		building = public	367	209	258	0.569	0.703	302	156	199	0.517	0.659
151		building = dormitory	606	167	185	0.276	0.305	449	75	100	0.167	0.223
152		building = warehouse	242	52	83	0.215	0.343	172	17	35	0.099	0.203
153		building = *	23502	8607	13334	0.366	0.567	13766	3347	6551	0.243	0.476
154	#	water	24019	18587	23862	0.774	0.993	5527	3379	5335	0.611	0.965
155		waterway = drain	430	124	425	0.288	0.988	119	45	117	0.378	0.983
156		waterway = ditch	645	183	644	0.284	0.998	130	39	125	0.300	0.962
157		waterway = stream	6330	4396	6312	0.694	0.997	1133	724	1105	0.639	0.975
158		waterway = river	12240	11206	12161	0.916	0.994	1805	1420	1699	0.787	0.941
159		waterway = canal	584	336	578	0.575	0.990	124	55	120	0.444	0.968
160		type = waterway	2093	1708	2083	0.816	0.995	1846	1467	1829	0.795	0.991
161		natural = water	3152	2085	3107	0.661	0.986	2563	1590	2516	0.620	0.982
162		natural = spring	591	232	589	0.393	0.997	530	179	527	0.338	0.994
163		waterway = *	39	18	38	0.462	0.974	31	13	30	0.419	0.968
164		water = *	3036	2040	2997	0.672	0.987	2467	1555	2424	0.630	0.983
165	#	natural	5442	3109	5192	0.571	0.954	3908	1830	3665	0.468	0.938
166		place = island	12	12	12	1.000	1.000	12	12	12	1.000	1.000
167		place = islet	88	68	88	0.773	1.000	78	60	77	0.769	0.987
168		boundary = *	878	685	763	0.780	0.869	839	657	726	0.783	0.865
169		natural = *	1488	803	1415	0.540	0.951	1219	584	1159	0.479	0.951
170		landuse = *	3137	1669	3074	0.532	0.980	1908	623	1838	0.327	0.963
171	#	other	2675	797	1127	0.298	0.421	2157	567	755	0.263	0.350

Падлічам статыстыку для выгрузкі ў postgis

• вынік будзе больш дакладным, але можа ня ўлічываць дачыненьні што не пераносяцца ў postgis

query_template = """
SELECT '{category}' AS category, {num} AS num, g.tags->'name' AS name, g.tags->'name:be' AS name_be, g.tags->'name:ru' AS name_ru
FROM {table} g
WHERE {condition}
"""
dependant_query_template = """
WITH tagged_table AS (
    SELECT field, COUNT(*) AS count
    FROM (
        SELECT DISTINCT osm_type, osm_id, field
        FROM (
            SELECT 'node' AS osm_type, g.osm_id, g.tags->'{field}' AS field 
            FROM planet_osm_point g
            INNER JOIN planet_osm_region p
            ON ST_Intersects(p.way, g.way)
            WHERE p.osm_id = -59065
            AND tags->'{field}' ~ '({cyr_regexp})'

            UNION ALL

            SELECT 'w+r' AS osm_type, g.osm_id, g.tags->'{field}' AS field
            FROM planet_osm_line g
            INNER JOIN planet_osm_region p
            ON ST_Intersects(p.way, g.way)
            WHERE p.osm_id = -59065
            AND tags->'{field}' ~ '({cyr_regexp})'
            
            UNION ALL

            SELECT 'w+r' AS osm_type, g.osm_id, g.tags->'{field}' AS field
            FROM planet_osm_roads g
            INNER JOIN planet_osm_region p
            ON ST_Intersects(p.way, g.way)
            WHERE p.osm_id = -59065
            AND tags->'{field}' ~ '({cyr_regexp})'

            UNION ALL

            SELECT 'w+r' AS osm_type, g.osm_id, g.tags->'{field}' AS field
            FROM planet_osm_polygon g
            INNER JOIN planet_osm_region p
            ON ST_Intersects(p.way, g.way)
            WHERE p.osm_id = -59065
            AND tags->'{field}' ~ '({cyr_regexp})'
        ) t1
    ) t2
    GROUP BY field
)
SELECT field, num, name, name_be, name_ru, SUM(count) AS count
FROM (
    SELECT '{field}' AS field, {num} AS num, c.count, 
        bool_or(p.name IS NOT NULL) AS name, 
        bool_or(p.name_be IS NOT NULL) AS name_be,
        bool_or(p.name_ru IS NOT NULL) AS name_ru
    FROM tagged_table c 
    LEFT JOIN {table} p 
    ON (c.field = p.name OR c.field = p.name_be OR c.field = p.name_ru)
    GROUP BY c.field, c.count
) t
GROUP BY field, num, name, name_be, name_ru
ORDER BY num
"""
tables = ['planet_osm_named_data']
cyr_regexp = '|'.join(cirylic_chars)

queries = []
exclude = defaultdict(lambda: [[], []])
for category, group in categories_rules2.items():
    conditions = []
    for i, (k, eq, vv) in enumerate(group):
        if vv:
            eq_str = 'IN' if eq else 'NOT IN'
            vv_str = ','.join(f"'{v}'" for v in vv)
            condition = f"g.tags->'{k}' {eq_str} ({vv_str})"
        elif not eq:
            condition = f"g.tags->'{k}' IS NOT NULL"
        else:
            raise ValueError()
        conditions.append(condition)
        exclude[k][eq].append(condition)
        for table in tables:
            query = query_template.format(category=category, num=i, table=table, condition=condition)
            queries.append(query)
    condition = ' OR '.join(f'({c})' for c in conditions)
    for table in tables:
        query = query_template.format(category=category, num=-1, table=table, condition=condition)
        queries.append(query)
condition = ' OR '.join(f"(g.tags->'{k}' IS NOT NULL)" for k, eq_c in exclude.items())
for table in tables:
    query = query_template.format(category='other', num=-1, table=table, condition=f'NOT ({condition})')
    queries.append(query)
for table in tables:
    query = query_template.format(category='TOTAL', num=-1, table=table, condition='TRUE')
    queries.append(query)
query = ' UNION ALL '.join(queries)

dependant_queries = []
for i, dependant in enumerate(dependants):
    for table in tables:
        query = dependant_query_template.format(num=i, table=table, field=dependant, cyr_regexp=cyr_regexp)
        dependant_queries.append(query)
dependant_query = ' UNION ALL '.join(dependant_queries)

print(len(queries), len(dependant_queries))

173 14

key_counter = defaultdict(lambda: defaultdict(list))
dependant_key_counter = defaultdict(Counter)

REGION_VIEW_SQL = """
CREATE MATERIALIZED VIEW IF NOT EXISTS planet_osm_region AS

SELECT
    osm_id,
    tags->'name' AS name,
    tags->'name:be' AS name_be,
    tags->'name:ru' AS name_ru,
    tags->'admin_level' AS admin_level,
    ST_Buffer(way, -0.000000001) AS way
FROM planet_osm_polygon
WHERE tags->'admin_level' IN ('2', '4', '6', '8', '9')
"""
REGION_WAY_INDEX_SQL = """
CREATE INDEX IF NOT EXISTS "planet_osm_region_way_idx" ON planet_osm_region USING GIST (way)
"""
REGION_ANALYZE_SQL = "ANALYZE planet_osm_region"

OSM_DATA_VIEW_SQL = f"""
CREATE MATERIALIZED VIEW IF NOT EXISTS planet_osm_named_data AS

SELECT
    g.osm_id AS osm_id,
    'node' AS osm_type,
    'point' AS kind,
    g.tags->'name' AS name,
    g.tags->'name:be' AS name_be,
    g.tags->'name:ru' AS name_ru,
    g.tags AS tags
FROM planet_osm_point g
INNER JOIN planet_osm_region p
ON ST_Intersects(p.way, g.way)
WHERE p.osm_id = -59065
AND g.tags->'name' ~ '({cyr_regexp})'

UNION ALL

SELECT
    ABS(g.osm_id) AS osm_id,
    CASE WHEN g.osm_id < 0 THEN 'relation' ELSE 'way' END AS osm_type,
    'line' AS kind,
    g.tags->'name' AS name,
    g.tags->'name:be' AS name_be,
    g.tags->'name:ru' AS name_ru,
    g.tags AS tags
FROM planet_osm_line g
INNER JOIN planet_osm_region p
ON ST_Intersects(p.way, g.way)
WHERE p.osm_id = -59065
AND g.tags->'name' ~ '({cyr_regexp})'

UNION ALL

SELECT
    ABS(g.osm_id) AS osm_id,
    CASE WHEN g.osm_id < 0 THEN 'relation' ELSE 'way' END AS osm_type,
    'road' AS kind,
    g.tags->'name' AS name,
    g.tags->'name:be' AS name_be,
    g.tags->'name:ru' AS name_ru,
    g.tags AS tags
FROM planet_osm_roads g
INNER JOIN planet_osm_region p
ON ST_Intersects(p.way, g.way)
WHERE p.osm_id = -59065
AND g.tags->'name' ~ '({cyr_regexp})'

UNION ALL

SELECT
    ABS(g.osm_id) AS osm_id,
    CASE WHEN g.osm_id < 0 THEN 'relation' ELSE 'way' END AS osm_type,
    'poly' AS kind,
    g.tags->'name' AS name,
    g.tags->'name:be' AS name_be,
    g.tags->'name:ru' AS name_ru,
    g.tags AS tags
FROM planet_osm_polygon g
INNER JOIN planet_osm_region p
ON ST_Intersects(p.way, g.way)
WHERE p.osm_id = -59065
AND g.tags->'name' ~ '({cyr_regexp})'
"""
OSM_DATA_OSM_ID_TYPE_INDEX_SQL = """
CREATE INDEX IF NOT EXISTS "planet_osm_named_data_osm_id_type_idx" ON planet_osm_named_data (osm_id, osm_type)
"""
OSM_DATA_TAGS_INDEX_SQL = """
CREATE INDEX IF NOT EXISTS "planet_osm_named_data_tags_idx" ON planet_osm_named_data USING GIN (tags)
"""
OSM_DATA_NAME_INDEX_SQL = """
CREATE INDEX IF NOT EXISTS "planet_osm_named_data_name_idx" ON planet_osm_named_data (name)
"""
OSM_DATA_NAME_BE_INDEX_SQL = """
CREATE INDEX IF NOT EXISTS "planet_osm_named_data_name_be_idx" ON planet_osm_named_data (name_be)
"""
OSM_DATA_NAME_RU_INDEX_SQL = """
CREATE INDEX IF NOT EXISTS "planet_osm_named_data_name_ru_idx" ON planet_osm_named_data (name_ru)
"""
OSM_DATA_ANALYZE_SQL = "ANALYZE planet_osm_named_data"


postgres_params = {
    'host': os.environ['POSTGRES_HOST'],
    'dbname': os.environ['POSTGRES_DB'],
    'user': os.environ['POSTGRES_USER'],
    'password': os.environ['POSTGRES_PASSWORD'],
}

try:
    from belarus_utils import PostgisSearchReadEngine, OverpassApiSearchEnigne
    postgis_api = PostgisSearchReadEngine(**postgres_params)
    overpass_api = OverpassApiSearchEnigne(cache=True)
    
    print('type = street')
    postgis_api.insert_extra_relations(overpass_api.search({'type': ['street']}))
    
    print('type = associatedStreet')
    postgis_api.insert_extra_relations(overpass_api.search({'type': ['associatedStreet']}))
    
    print('type = route')
    postgis_api.insert_extra_relations(overpass_api.search({'type': ['route']}))
    
    print('type = route_master')
    postgis_api.insert_extra_relations(overpass_api.search({'type': ['route_master']}))
    
    print('type = waterway')
    postgis_api.insert_extra_relations(overpass_api.search({'type': ['waterway']}))
except Exception as err:
    print(err)

with psycopg2.connect(**postgres_params) as conn:
    conn.autocommit = True
    with conn.cursor() as cur:
        cur.execute(REGION_VIEW_SQL)
        cur.execute(REGION_WAY_INDEX_SQL)
        cur.execute(REGION_ANALYZE_SQL)

        cur.execute(OSM_DATA_VIEW_SQL)
        cur.execute(OSM_DATA_OSM_ID_TYPE_INDEX_SQL)
        cur.execute(OSM_DATA_TAGS_INDEX_SQL)
        cur.execute(OSM_DATA_NAME_INDEX_SQL)
        cur.execute(OSM_DATA_NAME_BE_INDEX_SQL)
        cur.execute(OSM_DATA_NAME_RU_INDEX_SQL)
        cur.execute(OSM_DATA_ANALYZE_SQL)

with psycopg2.connect(**postgres_params) as conn:
    with conn.cursor() as cur:
        for i, query in enumerate(queries, 1):
            cur.execute(query)
            records = cur.fetchall()
            for category, num, name, name_be, name_ru in records:
                key = (category,) if num == -1 else (category, num)
                key_counter[key]['name'].append(name)
                if name_be is not None:
                    key_counter[key]['name:be'].append(name_be)
                if name_ru is not None:
                    key_counter[key]['name:ru'].append(name_ru)
                    
                if name == name_be == name_ru:
                    key_counter[key]['res_both'].append(name)
                elif name == name_be and name_ru is not None:
                    key_counter[key]['res_be_ru'].append(name)
                elif name == name_be:
                    key_counter[key]['res_be'].append(name)
                elif name == name_ru and name_be is not None:
                    key_counter[key]['res_ru_be'].append(name)
                elif name == name_ru:
                    key_counter[key]['res_ru'].append(name)
                elif name_be is not None and name_ru is not None:
                    key_counter[key]['res_other_both'].append(name)
                elif name_be is not None:
                    key_counter[key]['res_other_be'].append(name)
                elif name_ru is not None:
                    key_counter[key]['res_other_ru'].append(name)
                else:
                    key_counter[key]['res_none'].append(name)
        for i, query in enumerate(dependant_queries, 1):
            cur.execute(query)
            records = cur.fetchall()
            for field, num, name, name_be, name_ru, count in records:
                if name and name_be and name_ru:
                    dependant_key_counter[field]['all'] += count
                elif name and name_be and not name_ru:
                    dependant_key_counter[field]['be'] += count
                elif name and not name_be and name_ru:
                    dependant_key_counter[field]['ru'] += count
                elif name:
                    dependant_key_counter[field]['name'] += count
                else:
                    dependant_key_counter[field]['not found'] += count

type = street
type = associatedStreet
type = route
type = route_master
type = waterway

data = []
for c in list(categories_rules) + ['other', 'TOTAL']:
    name_cnt = len(key_counter[(c,)]['name'])
    name_uniq = len(set(key_counter[(c,)]['name']))
    name_be_cnt = len(key_counter[(c,)]['name:be'])
    name_be_uniq = len(set(key_counter[(c,)]['name:be']))
    name_ru_cnt = len(key_counter[(c,)]['name:ru'])
    name_ru_uniq = len(set(key_counter[(c,)]['name:ru']))
    data.append([
        '#', c, 
        name_cnt, name_be_cnt, name_ru_cnt, name_be_cnt/(name_cnt or 1), name_ru_cnt/(name_cnt or 1),
        name_uniq, name_be_uniq, name_ru_uniq, name_be_uniq/(name_uniq or 1), name_ru_uniq/(name_uniq or 1),
    ])
    if c in {'other', 'TOTAL'}:
        continue
    for i, (k, eq, vv) in enumerate(categories_rules2[c]):
        if eq:
            tag = f'{k} = {list(vv)[0]}'
        else:
            tag = f'{k} = *'
        name_cnt = len(key_counter[(c, i)]['name'])
        name_uniq = len(set(key_counter[(c, i)]['name']))
        name_be_cnt = len(key_counter[(c, i)]['name:be'])
        name_be_uniq = len(set(key_counter[(c, i)]['name:be']))
        name_ru_cnt = len(key_counter[(c, i)]['name:ru'])
        name_ru_uniq = len(set(key_counter[(c, i)]['name:ru']))
        data.append([
            '', tag, 
            name_cnt, name_be_cnt, name_ru_cnt, name_be_cnt/(name_cnt or 1), name_ru_cnt/(name_cnt or 1),
            name_uniq, name_be_uniq, name_ru_uniq, name_be_uniq/(name_uniq or 1), name_ru_uniq/(name_uniq or 1),
        ])

df = pd.DataFrame(data, columns=[
    'lvl', 'category', 
    'all name', 'all name:be', 'all name:ru', 'all name:be%', 'all name:ru%',
    'uniq name', 'uniq name:be', 'uniq name:ru', 'uniq name:be%', 'uniq name:ru%',
])
(
    df
    .style
    .set_properties(subset=['category'], **{'text-align': 'left'})
    .background_gradient('YlOrRd', subset=[c for c in df.columns if c.endswith('%')], vmin=0, vmax=1)
    .format({f: '{:.3f}' for f in [c for c in df.columns if c.endswith('%')]})
    .apply(lambda row: [("font-weight: bold" if row.loc['lvl'] == '#' else '') for _ in row], axis=1)
)

	lvl	category	all name	all name:be	all name:ru	all name:be%	all name:ru%	uniq name	uniq name:be	uniq name:ru	uniq name:be%	uniq name:ru%
0	#	admin	6874	6874	6874	1.000	1.000	1393	1397	1392	1.003	0.999
1		admin_level = 2	4	4	4	1.000	1.000	3	3	3	1.000	1.000
2		admin_level = 4	323	323	323	1.000	1.000	32	31	31	0.969	0.969
3		admin_level = 6	1304	1304	1304	1.000	1.000	165	165	165	1.000	1.000
4		admin_level = 8	5139	5139	5139	1.000	1.000	1191	1195	1191	1.003	1.000
5		admin_level = 9	104	104	104	1.000	1.000	13	13	13	1.000	1.000
6	#	place	114282	114275	114277	1.000	1.000	16946	16971	16805	1.001	0.992
7		place = city	119	119	119	1.000	1.000	16	16	16	1.000	1.000
8		place = town	677	677	677	1.000	1.000	137	138	137	1.007	1.000
9		place = village	12280	12277	12277	1.000	1.000	2198	2207	2195	1.004	0.999
10		place = hamlet	89489	89488	89488	1.000	1.000	13497	13625	13492	1.009	1.000
11		place = isolated_dwelling	2892	2890	2892	0.999	1.000	633	632	633	0.998	1.000
12		boundary = administrative	89587	89587	89587	1.000	1.000	16720	16872	16719	1.009	1.000
13		traffic_sign = city_limit	2053	2052	2052	1.000	1.000	997	932	926	0.935	0.929
14		admin_level = *	82694	82694	82694	1.000	1.000	15399	15547	15399	1.010	1.000
15	#	allotments	2871	2468	2864	0.860	0.998	1969	1560	1961	0.792	0.996
16		place = allotments	1833	1832	1833	0.999	1.000	1326	1292	1324	0.974	0.998
17		landuse = allotments	2697	2294	2690	0.851	0.997	1920	1510	1913	0.786	0.996
18	#	locality	12739	12695	12734	0.997	1.000	8381	8248	8373	0.984	0.999
19		place = locality	12678	12634	12673	0.997	1.000	8347	8210	8339	0.984	0.999
20		abandoned:place = *	3762	3762	3762	1.000	1.000	2195	2202	2195	1.003	1.000
21	#	suburb	7981	4058	7908	0.508	0.991	6244	2665	6181	0.427	0.990
22		landuse = commercial	178	96	176	0.539	0.989	165	84	163	0.509	0.988
23		landuse = construction	162	30	158	0.185	0.975	154	28	150	0.182	0.974
24		landuse = industrial	4385	1052	4333	0.240	0.988	3778	696	3741	0.184	0.990
25		landuse = residential	881	707	877	0.802	0.995	755	584	747	0.774	0.989
26		landuse = retail	232	140	230	0.603	0.991	148	59	146	0.399	0.986
27		place = *	2132	2126	2123	0.997	0.996	1463	1478	1456	1.010	0.995
28		residential = *	719	608	716	0.846	0.996	616	498	608	0.808	0.987
29		industrial = *	474	78	467	0.165	0.985	383	56	377	0.146	0.984
30	#	highway	111068	108549	110777	0.977	0.997	12135	10665	12064	0.879	0.994
31		highway = motorway	152	152	152	1.000	1.000	1	1	1	1.000	1.000
32		highway = trunk	2618	2568	2570	0.981	0.982	137	133	134	0.971	0.978
33		highway = primary	15328	15152	15262	0.989	0.996	529	509	522	0.962	0.987
34		highway = secondary	14515	14465	14515	0.997	1.000	746	729	746	0.977	1.000
35		highway = tertiary	10652	10436	10632	0.980	0.998	1665	1527	1653	0.917	0.993
36		highway = unclassified	3005	2889	2941	0.961	0.979	923	859	911	0.931	0.987
37		highway = residential	56685	55665	56651	0.982	0.999	9710	8937	9688	0.920	0.998
38		highway = service	3184	2921	3176	0.917	0.997	1571	1345	1563	0.856	0.995
39		highway = track	788	688	781	0.873	0.991	212	152	204	0.717	0.962
40		type = associatedStreet	1819	1819	1819	1.000	1.000	1172	1167	1172	0.996	1.000
41		type = street	37	37	37	1.000	1.000	35	35	35	1.000	1.000
42		highway = *	2285	1757	2241	0.769	0.981	1003	749	996	0.747	0.993
43	#	public_transport	34477	29204	34309	0.847	0.995	13803	9731	13010	0.705	0.943
44		highway = bus_stop	17456	15415	17365	0.883	0.995	8492	6687	7918	0.787	0.932
45		type = route	3925	2095	3918	0.534	0.998	3896	2085	3891	0.535	0.999
46		type = route_master	18	3	18	0.167	1.000	18	3	18	0.167	1.000
47		public_transport = *	24074	21893	23960	0.909	0.995	7833	6278	7256	0.801	0.926
48		route = *	3958	2124	3951	0.537	0.998	3918	2102	3913	0.536	0.999
49		railway = *	4077	3396	4051	0.833	0.994	1344	1198	1306	0.891	0.972
50		route_master = *	20	3	20	0.150	1.000	20	3	20	0.150	1.000
51	#	infrastructure	19699	11903	19367	0.604	0.983	10362	5305	10071	0.512	0.972
52		tunnel = *	5862	4546	5846	0.776	0.997	1648	1216	1636	0.738	0.993
53		barrier = *	4433	2898	4386	0.654	0.989	3708	2228	3658	0.601	0.987
54		power = *	4918	2371	4789	0.482	0.974	3967	1850	3866	0.466	0.975
55		bridge = *	2148	1916	2141	0.892	0.997	495	409	491	0.826	0.992
56		substation = *	2572	1487	2538	0.578	0.987	2408	1353	2381	0.562	0.989
57		emergency = *	1155	160	1153	0.139	0.998	288	123	285	0.427	0.990
58		ele = *	312	163	310	0.522	0.994	282	136	280	0.482	0.993
59		man_made = *	1692	556	1566	0.329	0.926	1214	328	1098	0.270	0.904
60		embankment = *	335	218	335	0.651	1.000	90	72	90	0.800	1.000
61	#	religion	3808	2693	3697	0.707	0.971	2316	1209	2176	0.522	0.940
62		amenity = place_of_worship	2674	1759	2634	0.658	0.985	1885	988	1828	0.524	0.970
63		amenity = monastery	21	13	18	0.619	0.857	20	12	17	0.600	0.850
64		building = church	1431	985	1397	0.688	0.976	1105	632	1063	0.572	0.962
65		building = cathedral	25	21	25	0.840	1.000	24	21	24	0.875	1.000
66		building = chapel	267	197	261	0.738	0.978	163	96	153	0.589	0.939
67		religion = *	3640	2600	3555	0.714	0.977	2209	1171	2095	0.530	0.948
68	#	education	5648	3234	4735	0.573	0.838	4045	1852	3122	0.458	0.772
69		landuse = education	804	791	804	0.984	1.000	797	782	797	0.981	1.000
70		amenity = university	199	144	127	0.724	0.638	187	133	118	0.711	0.631
71		amenity = college	320	153	180	0.478	0.562	295	139	159	0.471	0.539
72		amenity = school	1993	1172	1956	0.588	0.981	1437	625	1377	0.435	0.958
73		amenity = kindergarten	1903	1281	1884	0.673	0.990	1367	829	1327	0.606	0.971
74		building = university	164	65	55	0.396	0.335	157	61	53	0.389	0.338
75		building = college	116	49	54	0.422	0.466	103	41	43	0.398	0.417
76		building = school	671	231	327	0.344	0.487	568	136	228	0.239	0.401
77		building = kindergarten	369	171	226	0.463	0.612	274	96	136	0.350	0.496
78	#	healthcare	5657	2318	4691	0.410	0.829	2994	805	2119	0.269	0.708
79		amenity = hospital	653	295	501	0.452	0.767	500	182	350	0.364	0.700
80		amenity = pharmacy	2502	1247	2179	0.498	0.871	1308	357	1052	0.273	0.804
81		amenity = clinic	734	361	600	0.492	0.817	445	159	316	0.357	0.710
82		amenity = doctors	925	112	831	0.121	0.898	252	60	162	0.238	0.643
83		amenity = dentist	373	111	261	0.298	0.700	291	37	177	0.127	0.608
84		building = hospital	405	177	237	0.437	0.585	297	93	134	0.313	0.451
85		building = clinic	31	16	22	0.516	0.710	29	14	20	0.483	0.690
86		healthcare = *	2712	994	2672	0.367	0.985	1418	458	1403	0.323	0.989
87	#	government	4965	1930	3930	0.389	0.792	3393	877	2607	0.258	0.768
88		amenity = post_office	1082	537	593	0.496	0.548	405	115	132	0.284	0.326
89		amenity = police	707	308	418	0.436	0.591	516	157	249	0.304	0.483
90		amenity = library	499	249	262	0.499	0.525	336	116	112	0.345	0.333
91		office = government	1873	581	1860	0.310	0.993	1560	367	1545	0.235	0.990
92		landuse = military	370	144	368	0.389	0.995	318	107	316	0.336	0.994
93		government = *	724	313	718	0.432	0.992	580	197	572	0.340	0.986
94		military = *	569	178	563	0.313	0.989	381	68	375	0.178	0.984
95	#	office	5272	1470	5099	0.279	0.967	3870	520	3715	0.134	0.960
96		office = *	5272	1470	5099	0.279	0.967	3870	520	3715	0.134	0.960
97	#	tourism	15241	7702	14235	0.505	0.934	10872	4258	9924	0.392	0.913
98		tourism = *	9716	5520	9223	0.568	0.949	6989	3177	6507	0.455	0.931
99		historic = *	6019	2578	5506	0.428	0.915	4436	1503	3959	0.339	0.892
100		memorial = *	2312	641	1965	0.277	0.850	1858	405	1558	0.218	0.839
101		ruins = *	262	85	262	0.324	1.000	207	74	206	0.357	0.995
102		information = *	983	778	966	0.791	0.983	284	117	267	0.412	0.940
103		attraction = *	173	79	164	0.457	0.948	151	64	139	0.424	0.921
104		resort = *	142	29	140	0.204	0.986	134	27	132	0.201	0.985
105		artwork_type = *	753	279	630	0.371	0.837	661	240	547	0.363	0.828
106	#	amenity	48289	22836	29892	0.473	0.619	21545	4632	7726	0.215	0.359
107		amenity = cafe	2461	1059	1350	0.430	0.549	1702	498	717	0.293	0.421
108		amenity = atm	1454	1368	1404	0.941	0.966	109	62	78	0.569	0.716
109		amenity = bank	2073	1817	1910	0.877	0.921	323	115	212	0.356	0.656
110		amenity = fast_food	758	355	420	0.468	0.554	425	99	148	0.233	0.348
111		amenity = fuel	1126	636	760	0.565	0.675	622	246	311	0.395	0.500
112		amenity = community_centre	742	343	457	0.462	0.616	353	92	133	0.261	0.377
113		amenity = restaurant	603	297	339	0.493	0.562	515	229	268	0.445	0.520
114		amenity = bar	471	161	221	0.342	0.469	422	127	183	0.301	0.434
115		shop = convenience	7313	4102	5390	0.561	0.737	2471	836	1153	0.338	0.467
116		shop = clothes	1120	304	510	0.271	0.455	673	114	218	0.169	0.324
117		shop = car_repair	1133	313	433	0.276	0.382	882	90	199	0.102	0.226
118		shop = hairdresser	1075	403	551	0.375	0.513	718	151	255	0.210	0.355
119		shop = chemist	1478	1074	1308	0.727	0.885	87	42	40	0.483	0.460
120		shop = supermarket	1249	860	1018	0.689	0.815	377	177	227	0.469	0.602
121		shop = car_parts	911	248	457	0.272	0.502	486	44	135	0.091	0.278
122		shop = furniture	745	248	408	0.333	0.548	398	59	119	0.148	0.299
123		shop = hardware	724	263	399	0.363	0.551	519	117	211	0.225	0.407
124		shop = kiosk	395	223	258	0.565	0.653	205	82	92	0.400	0.449
125		shop = doityourself	723	226	329	0.313	0.455	493	85	145	0.172	0.294
126		shop = pet	475	119	178	0.251	0.375	210	31	61	0.148	0.290
127		shop = florist	525	211	267	0.402	0.509	262	52	87	0.198	0.332
128		shop = beauty	481	70	159	0.146	0.331	434	64	136	0.147	0.313
129		shop = mobile_phone	503	226	375	0.449	0.746	112	19	41	0.170	0.366
130		shop = shoes	258	110	134	0.426	0.519	152	29	54	0.191	0.355
131		shop = newsagent	534	377	432	0.706	0.809	65	24	29	0.369	0.446
132		shop = electronics	449	150	246	0.334	0.548	304	57	118	0.188	0.388
133		shop = alcohol	426	123	185	0.289	0.434	194	44	65	0.227	0.335
134		shop = jewelry	342	133	151	0.389	0.442	81	25	33	0.309	0.407
135		shop = mall	369	180	211	0.488	0.572	304	133	154	0.438	0.507
136		shop = butcher	390	133	205	0.341	0.526	219	59	76	0.269	0.347
137		shop = cosmetics	280	97	147	0.346	0.525	148	44	67	0.297	0.453
138		craft = shoemaker	225	160	162	0.711	0.720	64	11	24	0.172	0.375
139		amenity = *	6433	2699	3802	0.420	0.591	4325	1334	2077	0.308	0.480
140		shop = *	7095	2468	3497	0.348	0.493	4016	787	1289	0.196	0.321
141		leisure = *	3483	1584	2232	0.455	0.641	2371	769	1257	0.324	0.530
142		sport = *	779	317	365	0.407	0.469	589	170	213	0.289	0.362
143		clothes = *	228	92	123	0.404	0.539	130	30	46	0.231	0.354
144	#	building	30223	11353	17379	0.376	0.575	17801	4402	8421	0.247	0.473
145		building = industrial	1617	287	780	0.177	0.482	1230	146	471	0.119	0.383
146		building = service	1386	696	1092	0.502	0.788	1100	542	850	0.493	0.773
147		building = retail	1947	1152	1370	0.592	0.704	1131	501	642	0.443	0.568
148		building = commercial	593	189	286	0.319	0.482	541	154	245	0.285	0.453
149		building = warehouse	242	52	83	0.215	0.343	172	17	35	0.099	0.203
150		building = public	367	209	258	0.569	0.703	302	156	199	0.517	0.659
151		building = dormitory	607	168	186	0.277	0.306	450	76	101	0.169	0.224
152		building = warehouse	242	52	83	0.215	0.343	172	17	35	0.099	0.203
153		building = *	23464	8600	13324	0.367	0.568	13741	3341	6545	0.243	0.476
154	#	water	23561	18307	23540	0.777	0.999	5356	3355	5260	0.626	0.982
155		waterway = drain	431	125	426	0.290	0.988	119	45	117	0.378	0.983
156		waterway = ditch	640	182	640	0.284	1.000	128	38	125	0.297	0.977
157		waterway = stream	6242	4332	6241	0.694	1.000	1104	718	1095	0.650	0.992
158		waterway = river	11987	11005	11984	0.918	1.000	1736	1407	1675	0.810	0.965
159		waterway = canal	579	331	573	0.572	0.990	122	53	118	0.434	0.967
160		type = waterway	2058	1692	2058	0.822	1.000	1817	1455	1809	0.801	0.996
161		natural = water	3047	2076	3043	0.681	0.999	2479	1583	2468	0.639	0.996
162		natural = spring	590	232	588	0.393	0.997	529	179	527	0.338	0.996
163		waterway = *	37	17	37	0.459	1.000	29	12	29	0.414	1.000
164		water = *	2938	2031	2935	0.691	0.999	2388	1548	2378	0.648	0.996
165	#	natural	6205	3896	5992	0.628	0.966	3784	1817	3619	0.480	0.956
166		place = island	12	12	12	1.000	1.000	12	12	12	1.000	1.000
167		place = islet	87	68	87	0.782	1.000	77	60	76	0.779	0.987
168		boundary = *	1663	1472	1567	0.885	0.942	731	644	681	0.881	0.932
169		natural = *	1488	818	1428	0.550	0.960	1204	584	1157	0.485	0.961
170		landuse = *	3134	1671	3076	0.533	0.981	1906	623	1838	0.327	0.964
171	#	other	1471	523	657	0.356	0.447	1279	409	527	0.320	0.412
172	#	TOTAL	417028	336436	383675	0.807	0.920	114459	57443	87309	0.502	0.763

data = []
for c in list(categories_rules) + ['other', 'TOTAL']:
    name_both_cnt = len(key_counter[(c,)]['res_both'])
    name_be_ru_cnt = len(key_counter[(c,)]['res_be_ru'])
    name_be_cnt = len(key_counter[(c,)]['res_be'])
    name_ru_be_cnt = len(key_counter[(c,)]['res_ru_be'])
    name_ru_cnt = len(key_counter[(c,)]['res_ru'])
    name_other_both_cnt = len(key_counter[(c,)]['res_other_both'])
    name_other_be_cnt = len(key_counter[(c,)]['res_other_be'])
    name_other_ru_cnt = len(key_counter[(c,)]['res_other_ru'])
    name_none_cnt = len(key_counter[(c,)]['res_none'])
    total = (
        name_both_cnt + name_be_ru_cnt + name_be_cnt + name_ru_be_cnt + name_ru_cnt + 
        name_other_both_cnt + name_other_be_cnt + name_other_ru_cnt + 
        name_none_cnt
    )
    data.append([
        '#', c, 
        
        name_both_cnt, 
        name_be_ru_cnt, name_be_cnt, 
        name_ru_be_cnt, name_ru_cnt, 
        name_other_both_cnt, name_other_be_cnt, name_other_ru_cnt, 
        name_none_cnt, 
        
        name_both_cnt/(total or 1), 
        name_be_ru_cnt/(total or 1), name_be_cnt/(total or 1), 
        name_ru_be_cnt/(total or 1), name_ru_cnt/(total or 1), 
        name_other_both_cnt/(total or 1), name_other_be_cnt/(total or 1), name_other_ru_cnt/(total or 1), 
        name_none_cnt/(total or 1),
    ])
    if c in {'other', 'TOTAL'}:
        continue
    for i, (k, eq, vv) in enumerate(categories_rules2[c]):
        if eq:
            tag = f'{k} = {list(vv)[0]}'
        else:
            tag = f'{k} = *'
            
        name_both_cnt = len(key_counter[(c, i)]['res_both'])
        name_be_ru_cnt = len(key_counter[(c, i)]['res_be_ru'])
        name_be_cnt = len(key_counter[(c, i)]['res_be'])
        name_ru_be_cnt = len(key_counter[(c, i)]['res_ru_be'])
        name_ru_cnt = len(key_counter[(c, i)]['res_ru'])
        name_other_both_cnt = len(key_counter[(c, i)]['res_other_both'])
        name_other_be_cnt = len(key_counter[(c, i)]['res_other_be'])
        name_other_ru_cnt = len(key_counter[(c, i)]['res_other_ru'])
        name_none_cnt = len(key_counter[(c, i)]['res_none'])
        total = (
            name_both_cnt + name_be_ru_cnt + name_be_cnt + name_ru_be_cnt + name_ru_cnt + 
            name_other_both_cnt + name_other_be_cnt + name_other_ru_cnt + 
            name_none_cnt
        )
        data.append([
            '', tag,
            
            name_both_cnt, 
            name_be_ru_cnt, name_be_cnt, 
            name_ru_be_cnt, name_ru_cnt, 
            name_other_both_cnt, name_other_be_cnt, name_other_ru_cnt, 
            name_none_cnt, 

            name_both_cnt/(total or 1), 
            name_be_ru_cnt/(total or 1), name_be_cnt/(total or 1), 
            name_ru_be_cnt/(total or 1), name_ru_cnt/(total or 1), 
            name_other_both_cnt/(total or 1), name_other_be_cnt/(total or 1), name_other_ru_cnt/(total or 1), 
            name_none_cnt/(total or 1),
        ])


df = pd.DataFrame(data, columns=[
    'lvl', 'category', 
    'name be=ru', 'name be+ru', 'name be', 'name ru+be', 'name ru', 
    'other both', 'other be', 'other ru', 'no lang',
    'name be=ru%', 'name be+ru%', 'name be%', 'name ru+be%', 'name ru%', 
    'other both%', 'other be%', 'other ru%', 'no lang%',
])
(
    df
    .style
    .set_properties(subset=['category'], **{'text-align': 'left'})
    .set_properties(subset=['name be=ru', 'name be+ru', 'name be'], **{'background-color': '#d9ead3'})
    .set_properties(subset=['name ru+be'], **{'background-color': '#fff2cc'})
    .set_properties(subset=['name ru', 'other both', 'other be', 'other ru', 'no lang'], **{'background-color': '#f4cccc'})
    .background_gradient('YlOrRd', subset=[c for c in df.columns if c.endswith('%')], vmin=0, vmax=1)
    .format({f: '{:.3f}' for f in [c for c in df.columns if c.endswith('%')]})
    .apply(lambda row: [("font-weight: bold" if row.loc['lvl'] == '#' else '') for _ in row], axis=1)
)

	lvl	category	name be=ru	name be+ru	name be	name ru+be	name ru	other both	other be	other ru	no lang	name be=ru%	name be+ru%	name be%	name ru+be%	name ru%	other both%	other be%	other ru%	no lang%
0	#	admin	146	4	0	6724	0	0	0	0	0	0.021	0.001	0.000	0.978	0.000	0.000	0.000	0.000	0.000
1		admin_level = 2	1	2	0	1	0	0	0	0	0	0.250	0.500	0.000	0.250	0.000	0.000	0.000	0.000	0.000
2		admin_level = 4	34	2	0	287	0	0	0	0	0	0.105	0.006	0.000	0.889	0.000	0.000	0.000	0.000	0.000
3		admin_level = 6	51	0	0	1253	0	0	0	0	0	0.039	0.000	0.000	0.961	0.000	0.000	0.000	0.000	0.000
4		admin_level = 8	60	0	0	5079	0	0	0	0	0	0.012	0.000	0.000	0.988	0.000	0.000	0.000	0.000	0.000
5		admin_level = 9	0	0	0	104	0	0	0	0	0	0.000	0.000	0.000	1.000	0.000	0.000	0.000	0.000	0.000
6	#	place	9364	198	0	104713	2	0	0	0	5	0.082	0.002	0.000	0.916	0.000	0.000	0.000	0.000	0.000
7		place = city	22	0	0	97	0	0	0	0	0	0.185	0.000	0.000	0.815	0.000	0.000	0.000	0.000	0.000
8		place = town	56	0	0	621	0	0	0	0	0	0.083	0.000	0.000	0.917	0.000	0.000	0.000	0.000	0.000
9		place = village	1186	0	0	11091	0	0	0	0	3	0.097	0.000	0.000	0.903	0.000	0.000	0.000	0.000	0.000
10		place = hamlet	7588	4	0	81896	0	0	0	0	1	0.085	0.000	0.000	0.915	0.000	0.000	0.000	0.000	0.000
11		place = isolated_dwelling	222	0	0	2668	2	0	0	0	0	0.077	0.000	0.000	0.923	0.001	0.000	0.000	0.000	0.000
12		boundary = administrative	7267	4	0	82316	0	0	0	0	0	0.081	0.000	0.000	0.919	0.000	0.000	0.000	0.000	0.000
13		traffic_sign = city_limit	165	190	0	1697	0	0	0	0	1	0.080	0.093	0.000	0.827	0.000	0.000	0.000	0.000	0.000
14		admin_level = *	7121	0	0	75573	0	0	0	0	0	0.086	0.000	0.000	0.914	0.000	0.000	0.000	0.000	0.000
15	#	allotments	361	3	0	2101	395	2	1	2	6	0.126	0.001	0.000	0.732	0.138	0.001	0.000	0.001	0.002
16		place = allotments	270	3	0	1559	1	0	0	0	0	0.147	0.002	0.000	0.851	0.001	0.000	0.000	0.000	0.000
17		landuse = allotments	330	2	0	1959	395	2	1	2	6	0.122	0.001	0.000	0.726	0.146	0.001	0.000	0.001	0.002
18	#	locality	1299	12	1	11380	40	3	0	0	4	0.102	0.001	0.000	0.893	0.003	0.000	0.000	0.000	0.000
19		place = locality	1298	12	1	11320	40	3	0	0	4	0.102	0.001	0.000	0.893	0.003	0.000	0.000	0.000	0.000
20		abandoned:place = *	331	0	0	3431	0	0	0	0	0	0.088	0.000	0.000	0.912	0.000	0.000	0.000	0.000	0.000
21	#	suburb	337	75	20	3614	3857	11	1	14	52	0.042	0.009	0.003	0.453	0.483	0.001	0.000	0.002	0.007
22		landuse = commercial	4	4	0	88	79	0	0	1	2	0.022	0.022	0.000	0.494	0.444	0.000	0.000	0.006	0.011
23		landuse = construction	3	0	0	26	128	1	0	0	4	0.019	0.000	0.000	0.160	0.790	0.006	0.000	0.000	0.025
24		landuse = industrial	72	8	10	956	3281	6	0	10	42	0.016	0.002	0.002	0.218	0.748	0.001	0.000	0.002	0.010
25		landuse = residential	81	43	1	581	170	1	0	1	3	0.092	0.049	0.001	0.659	0.193	0.001	0.000	0.001	0.003
26		landuse = retail	2	4	1	132	91	1	0	0	1	0.009	0.017	0.004	0.569	0.392	0.004	0.000	0.000	0.004
27		place = *	179	19	8	1917	6	2	1	0	0	0.084	0.009	0.004	0.899	0.003	0.001	0.000	0.000	0.000
28		residential = *	70	42	1	494	108	1	0	1	2	0.097	0.058	0.001	0.687	0.150	0.001	0.000	0.001	0.003
29		industrial = *	4	1	4	68	388	1	0	5	3	0.008	0.002	0.008	0.143	0.819	0.002	0.000	0.011	0.006
30	#	highway	238	195	11	107923	2239	181	1	1	279	0.002	0.002	0.000	0.972	0.020	0.002	0.000	0.000	0.003
31		highway = motorway	152	0	0	0	0	0	0	0	0	1.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
32		highway = trunk	58	88	0	2410	2	12	0	0	48	0.022	0.034	0.000	0.921	0.001	0.005	0.000	0.000	0.018
33		highway = primary	0	44	0	15084	110	24	0	0	66	0.000	0.003	0.000	0.984	0.007	0.002	0.000	0.000	0.004
34		highway = secondary	0	4	0	14461	50	0	0	0	0	0.000	0.000	0.000	0.996	0.003	0.000	0.000	0.000	0.000
35		highway = tertiary	3	7	5	10421	201	0	0	0	15	0.000	0.001	0.000	0.978	0.019	0.000	0.000	0.000	0.001
36		highway = unclassified	1	0	0	2875	52	13	0	0	64	0.000	0.000	0.000	0.957	0.017	0.004	0.000	0.000	0.021
37		highway = residential	3	0	5	55655	992	1	1	0	28	0.000	0.000	0.000	0.982	0.018	0.000	0.000	0.000	0.000
38		highway = service	0	13	0	2894	255	14	0	0	8	0.000	0.004	0.000	0.909	0.080	0.004	0.000	0.000	0.003
39		highway = track	0	0	1	570	93	117	0	1	6	0.000	0.000	0.001	0.723	0.118	0.148	0.000	0.001	0.008
40		type = associatedStreet	0	0	0	1819	0	0	0	0	0	0.000	0.000	0.000	1.000	0.000	0.000	0.000	0.000	0.000
41		type = street	0	0	0	37	0	0	0	0	0	0.000	0.000	0.000	1.000	0.000	0.000	0.000	0.000	0.000
42		highway = *	21	39	0	1697	484	0	0	0	44	0.009	0.017	0.000	0.743	0.212	0.000	0.000	0.000	0.019
43	#	public_transport	2179	13260	104	13626	5129	35	0	80	64	0.063	0.385	0.003	0.395	0.149	0.001	0.000	0.002	0.002
44		highway = bus_stop	1363	7358	62	6632	1943	0	0	69	29	0.078	0.422	0.004	0.380	0.111	0.000	0.000	0.004	0.002
45		type = route	10	52	7	2007	1829	19	0	1	0	0.003	0.013	0.002	0.511	0.466	0.005	0.000	0.000	0.000
46		type = route_master	0	0	0	2	15	1	0	0	0	0.000	0.000	0.000	0.111	0.833	0.056	0.000	0.000	0.000
47		public_transport = *	1790	12261	58	7774	2071	10	0	54	56	0.074	0.509	0.002	0.323	0.086	0.000	0.000	0.002	0.002
48		route = *	14	52	7	2031	1833	20	0	1	0	0.004	0.013	0.002	0.513	0.463	0.005	0.000	0.000	0.000
49		railway = *	173	475	20	2724	675	4	0	0	6	0.042	0.117	0.005	0.668	0.166	0.001	0.000	0.000	0.001
50		route_master = *	0	0	0	2	17	1	0	0	0	0.000	0.000	0.000	0.100	0.850	0.050	0.000	0.000	0.000
51	#	infrastructure	2071	324	27	9350	7508	92	39	22	266	0.105	0.016	0.001	0.475	0.381	0.005	0.002	0.001	0.014
52		tunnel = *	978	69	0	3481	1300	18	0	0	16	0.167	0.012	0.000	0.594	0.222	0.003	0.000	0.000	0.003
53		barrier = *	238	233	13	2380	1497	34	0	4	34	0.054	0.053	0.003	0.537	0.338	0.008	0.000	0.001	0.008
54		power = *	815	1	1	1504	2443	12	38	14	90	0.166	0.000	0.000	0.306	0.497	0.002	0.008	0.003	0.018
55		bridge = *	29	7	6	1864	231	10	0	0	1	0.014	0.003	0.003	0.868	0.108	0.005	0.000	0.000	0.000
56		substation = *	592	0	1	890	1044	2	2	10	31	0.230	0.000	0.000	0.346	0.406	0.001	0.001	0.004	0.012
57		emergency = *	1	0	2	147	992	10	0	3	0	0.001	0.000	0.002	0.127	0.859	0.009	0.000	0.003	0.000
58		ele = *	34	1	0	125	147	3	0	0	2	0.109	0.003	0.000	0.401	0.471	0.010	0.000	0.000	0.006
59		man_made = *	59	13	5	474	1014	5	0	1	121	0.035	0.008	0.003	0.280	0.599	0.003	0.000	0.001	0.072
60		embankment = *	4	0	0	212	117	2	0	0	0	0.012	0.000	0.000	0.633	0.349	0.006	0.000	0.000	0.000
61	#	religion	40	127	57	2436	1062	23	10	9	44	0.011	0.033	0.015	0.640	0.279	0.006	0.003	0.002	0.012
62		amenity = place_of_worship	36	55	30	1620	899	18	0	6	10	0.013	0.021	0.011	0.606	0.336	0.007	0.000	0.002	0.004
63		amenity = monastery	0	0	0	12	6	0	1	0	2	0.000	0.000	0.000	0.571	0.286	0.000	0.048	0.000	0.095
64		building = church	0	22	17	930	432	11	5	2	12	0.000	0.015	0.012	0.650	0.302	0.008	0.003	0.001	0.008
65		building = cathedral	0	0	0	21	4	0	0	0	0	0.000	0.000	0.000	0.840	0.160	0.000	0.000	0.000	0.000
66		building = chapel	18	6	2	170	66	1	0	0	4	0.067	0.022	0.007	0.637	0.247	0.004	0.000	0.000	0.015
67		religion = *	39	117	49	2366	1004	23	6	6	30	0.011	0.032	0.013	0.650	0.276	0.006	0.002	0.002	0.008
68	#	education	363	115	35	2611	1581	43	67	22	811	0.064	0.020	0.006	0.462	0.280	0.008	0.012	0.004	0.144
69		landuse = education	4	7	0	778	13	2	0	0	0	0.005	0.009	0.000	0.968	0.016	0.002	0.000	0.000	0.000
70		amenity = university	1	6	2	108	7	4	23	1	47	0.005	0.030	0.010	0.543	0.035	0.020	0.116	0.005	0.236
71		amenity = college	1	6	1	127	38	6	12	2	127	0.003	0.019	0.003	0.397	0.119	0.019	0.037	0.006	0.397
72		amenity = school	294	36	22	811	797	9	0	9	15	0.148	0.018	0.011	0.407	0.400	0.005	0.000	0.005	0.008
73		amenity = kindergarten	19	52	9	1182	609	19	0	3	10	0.010	0.027	0.005	0.621	0.320	0.010	0.000	0.002	0.005
74		building = university	3	0	1	42	8	1	18	1	90	0.018	0.000	0.006	0.256	0.049	0.006	0.110	0.006	0.549
75		building = college	0	2	0	37	12	3	7	0	55	0.000	0.017	0.000	0.319	0.103	0.026	0.060	0.000	0.474
76		building = school	49	10	1	161	101	1	9	5	334	0.073	0.015	0.001	0.240	0.151	0.001	0.013	0.007	0.498
77		building = kindergarten	0	3	0	166	56	0	2	1	141	0.000	0.008	0.000	0.450	0.152	0.000	0.005	0.003	0.382
78	#	healthcare	66	55	20	1954	2379	160	63	77	883	0.012	0.010	0.004	0.345	0.421	0.028	0.011	0.014	0.156
79		amenity = hospital	7	6	4	248	224	13	17	3	131	0.011	0.009	0.006	0.380	0.343	0.020	0.026	0.005	0.201
80		amenity = pharmacy	36	35	4	1040	887	124	8	57	311	0.014	0.014	0.002	0.416	0.355	0.050	0.003	0.023	0.124
81		amenity = clinic	3	5	8	317	254	14	14	7	112	0.004	0.007	0.011	0.432	0.346	0.019	0.019	0.010	0.153
82		amenity = doctors	13	1	0	88	727	0	10	2	84	0.014	0.001	0.000	0.095	0.786	0.000	0.011	0.002	0.091
83		amenity = dentist	0	5	1	95	149	5	5	7	106	0.000	0.013	0.003	0.255	0.399	0.013	0.013	0.019	0.284
84		building = hospital	6	2	3	153	73	3	10	0	155	0.015	0.005	0.007	0.378	0.180	0.007	0.025	0.000	0.383
85		building = clinic	0	1	0	14	6	1	0	0	9	0.000	0.032	0.000	0.452	0.194	0.032	0.000	0.000	0.290
86		healthcare = *	22	42	12	852	1647	65	1	44	27	0.008	0.015	0.004	0.314	0.607	0.024	0.000	0.016	0.010
87	#	government	101	237	22	1431	2075	49	90	37	923	0.020	0.048	0.004	0.288	0.418	0.010	0.018	0.007	0.186
88		amenity = post_office	1	220	4	278	77	12	22	5	463	0.001	0.203	0.004	0.257	0.071	0.011	0.020	0.005	0.428
89		amenity = police	3	1	3	254	140	10	37	10	249	0.004	0.001	0.004	0.359	0.198	0.014	0.052	0.014	0.352
90		amenity = library	2	6	4	203	46	3	31	2	202	0.004	0.012	0.008	0.407	0.092	0.006	0.062	0.004	0.405
91		office = government	59	8	10	482	1271	22	0	18	3	0.032	0.004	0.005	0.257	0.679	0.012	0.000	0.010	0.002
92		landuse = military	22	1	1	118	225	2	0	0	1	0.059	0.003	0.003	0.319	0.608	0.005	0.000	0.000	0.003
93		government = *	40	6	4	249	406	14	0	3	2	0.055	0.008	0.006	0.344	0.561	0.019	0.000	0.004	0.003
94		military = *	22	3	0	153	383	0	0	2	6	0.039	0.005	0.000	0.269	0.673	0.000	0.000	0.004	0.011
95	#	office	244	9	16	1174	3590	26	1	56	156	0.046	0.002	0.003	0.223	0.681	0.005	0.000	0.011	0.030
96		office = *	244	9	16	1174	3590	26	1	56	156	0.046	0.002	0.003	0.223	0.681	0.005	0.000	0.011	0.030
97	#	tourism	362	259	327	5993	6798	748	13	75	666	0.024	0.017	0.021	0.393	0.446	0.049	0.001	0.005	0.044
98		tourism = *	243	172	170	4208	3832	717	10	51	313	0.025	0.018	0.017	0.433	0.394	0.074	0.001	0.005	0.032
99		historic = *	118	108	159	2152	3064	39	2	25	352	0.020	0.018	0.026	0.358	0.509	0.006	0.000	0.004	0.058
100		memorial = *	27	30	43	524	1359	16	1	9	303	0.012	0.013	0.019	0.227	0.588	0.007	0.000	0.004	0.131
101		ruins = *	5	16	0	62	177	2	0	0	0	0.019	0.061	0.000	0.237	0.676	0.008	0.000	0.000	0.000
102		information = *	8	17	10	113	198	629	1	1	6	0.008	0.017	0.010	0.115	0.201	0.640	0.001	0.001	0.006
103		attraction = *	13	3	7	52	93	3	1	0	1	0.075	0.017	0.040	0.301	0.538	0.017	0.006	0.000	0.006
104		resort = *	2	0	0	26	110	1	0	1	2	0.014	0.000	0.000	0.183	0.775	0.007	0.000	0.007	0.014
105		artwork_type = *	27	18	29	174	370	27	4	14	90	0.036	0.024	0.039	0.231	0.491	0.036	0.005	0.019	0.120
106	#	amenity	4610	688	254	14982	7987	1065	1237	560	16906	0.095	0.014	0.005	0.310	0.165	0.022	0.026	0.012	0.350
107		amenity = cafe	159	20	14	746	353	32	88	40	1009	0.065	0.008	0.006	0.303	0.143	0.013	0.036	0.016	0.410
108		amenity = atm	840	2	1	512	38	12	1	0	48	0.578	0.001	0.001	0.352	0.026	0.008	0.001	0.000	0.033
109		amenity = bank	1104	19	1	615	88	74	4	10	158	0.533	0.009	0.000	0.297	0.042	0.036	0.002	0.005	0.076
110		amenity = fast_food	43	1	10	252	102	15	34	7	294	0.057	0.001	0.013	0.332	0.135	0.020	0.045	0.009	0.388
111		amenity = fuel	68	3	0	496	110	52	17	31	349	0.060	0.003	0.000	0.440	0.098	0.046	0.015	0.028	0.310
112		amenity = community_centre	171	11	2	129	138	1	29	7	254	0.230	0.015	0.003	0.174	0.186	0.001	0.039	0.009	0.342
113		amenity = restaurant	46	14	5	196	66	6	30	11	229	0.076	0.023	0.008	0.325	0.109	0.010	0.050	0.018	0.380
114		amenity = bar	36	6	4	89	80	4	22	6	224	0.076	0.013	0.008	0.189	0.170	0.008	0.047	0.013	0.476
115		shop = convenience	696	96	82	2689	1335	426	113	148	1728	0.095	0.013	0.011	0.368	0.183	0.058	0.015	0.020	0.236
116		shop = clothes	42	23	2	215	208	7	15	15	593	0.037	0.021	0.002	0.192	0.186	0.006	0.013	0.013	0.529
117		shop = car_repair	12	3	0	256	127	13	29	22	671	0.011	0.003	0.000	0.226	0.112	0.011	0.026	0.019	0.592
118		shop = hairdresser	30	13	3	315	185	6	36	2	485	0.028	0.012	0.003	0.293	0.172	0.006	0.033	0.002	0.451
119		shop = chemist	2	1	1	1013	261	26	31	5	138	0.001	0.001	0.001	0.685	0.177	0.018	0.021	0.003	0.093
120		shop = supermarket	260	18	3	410	141	164	5	25	223	0.208	0.014	0.002	0.328	0.113	0.131	0.004	0.020	0.179
121		shop = car_parts	2	1	0	199	237	6	40	12	414	0.002	0.001	0.000	0.218	0.260	0.007	0.044	0.013	0.454
122		shop = furniture	20	2	0	203	143	12	11	28	326	0.027	0.003	0.000	0.272	0.192	0.016	0.015	0.038	0.438
123		shop = hardware	23	13	5	196	150	8	18	9	302	0.032	0.018	0.007	0.271	0.207	0.011	0.025	0.012	0.417
124		shop = kiosk	9	35	4	149	54	9	17	2	116	0.023	0.089	0.010	0.377	0.137	0.023	0.043	0.005	0.294
125		shop = doityourself	22	2	8	178	113	4	12	10	374	0.030	0.003	0.011	0.246	0.156	0.006	0.017	0.014	0.517
126		shop = pet	4	0	0	94	74	2	19	4	278	0.008	0.000	0.000	0.198	0.156	0.004	0.040	0.008	0.585
127		shop = florist	18	3	1	171	64	4	14	7	243	0.034	0.006	0.002	0.326	0.122	0.008	0.027	0.013	0.463
128		shop = beauty	11	0	2	39	96	2	16	11	304	0.023	0.000	0.004	0.081	0.200	0.004	0.033	0.023	0.632
129		shop = mobile_phone	140	6	0	78	147	1	1	3	127	0.278	0.012	0.000	0.155	0.292	0.002	0.002	0.006	0.252
130		shop = shoes	19	3	0	79	29	2	7	2	117	0.074	0.012	0.000	0.306	0.112	0.008	0.027	0.008	0.453
131		shop = newsagent	30	59	17	248	71	23	0	1	85	0.056	0.110	0.032	0.464	0.133	0.043	0.000	0.002	0.159
132		shop = electronics	63	2	1	74	96	0	10	11	192	0.140	0.004	0.002	0.165	0.214	0.000	0.022	0.024	0.428
133		shop = alcohol	8	4	2	94	72	5	10	2	229	0.019	0.009	0.005	0.221	0.169	0.012	0.023	0.005	0.538
134		shop = jewelry	3	2	1	85	45	16	26	0	164	0.009	0.006	0.003	0.249	0.132	0.047	0.076	0.000	0.480
135		shop = mall	22	5	2	120	46	15	16	3	140	0.060	0.014	0.005	0.325	0.125	0.041	0.043	0.008	0.379
136		shop = butcher	16	6	6	82	92	4	19	5	160	0.041	0.015	0.015	0.210	0.236	0.010	0.049	0.013	0.410
137		shop = cosmetics	1	2	0	71	58	5	18	10	115	0.004	0.007	0.000	0.254	0.207	0.018	0.064	0.036	0.411
138		craft = shoemaker	1	2	0	137	17	5	15	0	48	0.004	0.009	0.000	0.609	0.076	0.022	0.067	0.000	0.213
139		amenity = *	368	193	25	1930	1223	37	146	51	2460	0.057	0.030	0.004	0.300	0.190	0.006	0.023	0.008	0.382
140		shop = *	245	90	39	1737	1287	86	271	52	3288	0.035	0.013	0.005	0.245	0.181	0.012	0.038	0.007	0.463
141		leisure = *	89	37	14	1330	746	11	103	19	1134	0.026	0.011	0.004	0.382	0.214	0.003	0.030	0.005	0.326
142		sport = *	15	8	4	249	86	4	37	3	373	0.019	0.010	0.005	0.320	0.110	0.005	0.047	0.004	0.479
143		clothes = *	4	9	0	75	31	1	3	3	102	0.018	0.039	0.000	0.329	0.136	0.004	0.013	0.013	0.447
144	#	building	2276	539	217	7406	6615	381	534	162	12093	0.075	0.018	0.007	0.245	0.219	0.013	0.018	0.005	0.400
145		building = industrial	32	8	2	226	506	4	15	4	820	0.020	0.005	0.001	0.140	0.313	0.002	0.009	0.002	0.507
146		building = service	618	1	12	58	413	2	5	0	277	0.446	0.001	0.009	0.042	0.298	0.001	0.004	0.000	0.200
147		building = retail	252	36	36	665	267	116	47	34	494	0.129	0.018	0.018	0.342	0.137	0.060	0.024	0.017	0.254
148		building = commercial	23	9	4	129	115	8	16	2	287	0.039	0.015	0.007	0.218	0.194	0.013	0.027	0.003	0.484
149		building = warehouse	19	1	0	30	33	0	2	0	157	0.079	0.004	0.000	0.124	0.136	0.000	0.008	0.000	0.649
150		building = public	23	10	4	158	60	5	9	2	96	0.063	0.027	0.011	0.431	0.163	0.014	0.025	0.005	0.262
151		building = dormitory	0	3	1	109	71	2	53	1	367	0.000	0.005	0.002	0.180	0.117	0.003	0.087	0.002	0.605
152		building = warehouse	19	1	0	30	33	0	2	0	157	0.079	0.004	0.000	0.124	0.136	0.000	0.008	0.000	0.649
153		building = *	1309	471	158	6031	5150	244	387	119	9595	0.056	0.020	0.007	0.257	0.219	0.010	0.016	0.005	0.409
154	#	water	3733	177	0	14246	5228	151	0	5	21	0.158	0.008	0.000	0.605	0.222	0.006	0.000	0.000	0.001
155		waterway = drain	14	2	0	109	301	0	0	0	5	0.032	0.005	0.000	0.253	0.698	0.000	0.000	0.000	0.012
156		waterway = ditch	25	0	0	153	457	4	0	1	0	0.039	0.000	0.000	0.239	0.714	0.006	0.000	0.002	0.000
157		waterway = stream	1042	46	0	3214	1909	30	0	0	1	0.167	0.007	0.000	0.515	0.306	0.005	0.000	0.000	0.000
158		waterway = river	2584	105	0	8233	978	83	0	1	3	0.216	0.009	0.000	0.687	0.082	0.007	0.000	0.000	0.000
159		waterway = canal	11	1	0	312	242	7	0	0	6	0.019	0.002	0.000	0.539	0.418	0.012	0.000	0.000	0.010
160		type = waterway	375	10	0	1248	364	59	0	2	0	0.182	0.005	0.000	0.606	0.177	0.029	0.000	0.001	0.000
161		natural = water	29	8	0	2013	965	26	0	2	4	0.010	0.003	0.000	0.661	0.317	0.009	0.000	0.001	0.001
162		natural = spring	28	10	0	193	355	1	0	1	2	0.047	0.017	0.000	0.327	0.602	0.002	0.000	0.002	0.003
163		waterway = *	0	4	0	13	20	0	0	0	0	0.000	0.108	0.000	0.351	0.541	0.000	0.000	0.000	0.000
164		water = *	22	5	0	1978	902	26	0	2	3	0.007	0.002	0.000	0.673	0.307	0.009	0.000	0.001	0.001
165	#	natural	353	110	80	3308	2177	33	12	11	121	0.057	0.018	0.013	0.533	0.351	0.005	0.002	0.002	0.020
166		place = island	1	3	0	8	0	0	0	0	0	0.083	0.250	0.000	0.667	0.000	0.000	0.000	0.000	0.000
167		place = islet	3	1	0	62	19	2	0	0	0	0.034	0.011	0.000	0.713	0.218	0.023	0.000	0.000	0.000
168		boundary = *	107	1	4	1336	106	12	12	5	80	0.064	0.001	0.002	0.803	0.064	0.007	0.007	0.003	0.048
169		natural = *	112	33	49	620	656	4	0	3	11	0.075	0.022	0.033	0.417	0.441	0.003	0.000	0.002	0.007
170		landuse = *	137	74	27	1416	1429	17	0	3	31	0.044	0.024	0.009	0.452	0.456	0.005	0.000	0.001	0.010
171	#	other	67	19	6	401	159	7	23	4	785	0.046	0.013	0.004	0.273	0.108	0.005	0.016	0.003	0.534
172	#	TOTAL	24590	15579	989	290971	49027	2537	1770	971	30594	0.059	0.037	0.002	0.698	0.118	0.006	0.004	0.002	0.073

data = []
for dependant in list(dependants):
    name_all_cnt = dependant_key_counter[dependant]['all']
    name_be_cnt = dependant_key_counter[dependant]['be']
    name_ru_cnt = dependant_key_counter[dependant]['ru']
    name_name_cnt = dependant_key_counter[dependant]['name']
    name_no_cnt = dependant_key_counter[dependant]['not found']
    total = (name_all_cnt + name_be_cnt + name_ru_cnt + name_name_cnt + name_no_cnt)
    data.append([
        '#', dependant, 
        
        name_all_cnt, 
        name_be_cnt, 
        name_ru_cnt, 
        name_name_cnt, 
        name_no_cnt, 
        
        name_all_cnt/(total or 1), 
        name_be_cnt/(total or 1),
        name_ru_cnt/(total or 1),
        name_name_cnt/(total or 1),
        name_no_cnt/(total or 1),
    ])


df = pd.DataFrame(data, columns=[
    'lvl', 'category', 
    'name all', 'name be', 'name ru', 'name only', 'not found', 
    'name all%', 'name be%', 'name ru%', 'name only%', 'not found%', 
])
(
    df
    .style
    .set_properties(subset=['category'], **{'text-align': 'left'})
    .set_properties(subset=['name all', 'name be'], **{'background-color': '#d9ead3'})
    .set_properties(subset=['name ru', 'name only'], **{'background-color': '#fff2cc'})
    .set_properties(subset=['not found'], **{'background-color': '#f4cccc'})
    .background_gradient('YlOrRd', subset=[c for c in df.columns if c.endswith('%')], vmin=0, vmax=1)
    .format({f: '{:.3f}' for f in [c for c in df.columns if c.endswith('%')]})
    .apply(lambda row: [("font-weight: bold" if row.loc['lvl'] == '#' else '') for _ in row], axis=1)
)

	lvl	category	name all	name be	name ru	name only	not found	name all%	name be%	name ru%	name only%	not found%
0	#	addr:region	54988	0	0	0	0	1.000	0.000	0.000	0.000	0.000
1	#	addr:district	54682	0	0	0	108	0.998	0.000	0.000	0.000	0.002
2	#	addr:subdistrict	12994	0	0	0	0	1.000	0.000	0.000	0.000	0.000
3	#	addr:city	114361	0	130	0	2	0.999	0.000	0.001	0.000	0.000
4	#	addr:place	36954	0	2	0	54	0.998	0.000	0.000	0.000	0.001
5	#	addr:street	860120	11	1507	81	354	0.998	0.000	0.002	0.000	0.000
6	#	addr2:street	629	0	0	0	0	1.000	0.000	0.000	0.000	0.000
7	#	from	724	0	15	2	271	0.715	0.000	0.015	0.002	0.268
8	#	to	705	0	12	3	189	0.776	0.000	0.013	0.003	0.208
9	#	via	68	0	0	0	40	0.630	0.000	0.000	0.000	0.370
10	#	destination	847	0	0	0	680	0.555	0.000	0.000	0.000	0.445
11	#	destination:backward	57	0	0	0	102	0.358	0.000	0.000	0.000	0.642
12	#	destination:forward	88	0	0	0	94	0.484	0.000	0.000	0.000	0.516
13	#	water_tank:city	1	0	0	0	0	1.000	0.000	0.000	0.000	0.000