Overview

Dataset statistics

Number of variables18
Number of observations100
Missing cells101
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.6 KiB
Average record size in memory149.3 B

Variable types

Categorical10
Text4
Numeric3
DateTime1

Alerts

BASE_YMD has constant value ""Constant
city_do_cd is highly overall correlated with city_gn_gu_cd and 10 other fieldsHigh correlation
ctprvn_jlang_nm is highly overall correlated with city_gn_gu_cd and 10 other fieldsHigh correlation
signgn_klang_nm is highly overall correlated with city_gn_gu_cd and 10 other fieldsHigh correlation
ctprvn_engl_nm is highly overall correlated with city_gn_gu_cd and 10 other fieldsHigh correlation
signgn_jlang_nm is highly overall correlated with city_gn_gu_cd and 10 other fieldsHigh correlation
signgn_chnlng_nm is highly overall correlated with city_gn_gu_cd and 10 other fieldsHigh correlation
signgn_engl_nm is highly overall correlated with city_gn_gu_cd and 10 other fieldsHigh correlation
ctprvn_chnlng_nm is highly overall correlated with city_gn_gu_cd and 10 other fieldsHigh correlation
ctprvn_klang_nm is highly overall correlated with city_gn_gu_cd and 10 other fieldsHigh correlation
city_gn_gu_cd is highly overall correlated with lo and 9 other fieldsHigh correlation
lo is highly overall correlated with city_gn_gu_cd and 9 other fieldsHigh correlation
la is highly overall correlated with signgn_klang_nm and 3 other fieldsHigh correlation
se_nm is highly overall correlated with ctprvn_klang_nm and 4 other fieldsHigh correlation
hmpg_url has 93 (93.0%) missing valuesMissing
tel_no has 8 (8.0%) missing valuesMissing

Reproduction

Analysis started2023-12-10 10:07:20.172369
Analysis finished2023-12-10 10:07:26.407581
Duration6.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

se_nm
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
생활이동지원센터
29 
장애인복지관
21 
수어통연센터
16 
수어통역센터
11 
전국교통약자이동지원센터
8 
Other values (5)
15 

Length

Max length12
Median length11
Mean length7.37
Min length5

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row장애인복지관
2nd row수어통연센터
3rd row생활이동지원센터
4th row장애인생산품판매시설
5th row전국교통약자이동지원센터

Common Values

ValueCountFrequency (%)
생활이동지원센터 29
29.0%
장애인복지관 21
21.0%
수어통연센터 16
16.0%
수어통역센터 11
 
11.0%
전국교통약자이동지원센터 8
 
8.0%
관광약자 숙박시설 7
 
7.0%
장애인체육시설 3
 
3.0%
장애인생산품판매시설 2
 
2.0%
재활치료시설 2
 
2.0%
점자도서관 1
 
1.0%

Length

2023-12-10T19:07:26.554945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:26.798823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활이동지원센터 29
27.1%
장애인복지관 21
19.6%
수어통연센터 16
15.0%
수어통역센터 11
 
10.3%
전국교통약자이동지원센터 8
 
7.5%
관광약자 7
 
6.5%
숙박시설 7
 
6.5%
장애인체육시설 3
 
2.8%
장애인생산품판매시설 2
 
1.9%
재활치료시설 2
 
1.9%

entrp_nm
Text

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:07:27.233058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length11.06
Min length4

Characters and Unicode

Total characters1106
Distinct characters117
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)94.0%

Sample

1st row강릉시장애인종합복지관
2nd row강릉시수어통역센터
3rd row강릉시장애인생활이동지원센터
4th row강원도장애인생산품판매시설
5th row강릉시교통약자이동지원센터
ValueCountFrequency (%)
강원도장애인종합복지관 4
 
3.5%
장애인생활이동지원센터 4
 
3.5%
수어통역센터 3
 
2.6%
광명장애인종합복지관 2
 
1.8%
김포시 2
 
1.8%
수화통역센터 2
 
1.8%
강원도장애인생산품판매시설 2
 
1.8%
가평군 2
 
1.8%
평창군수어통역센터 1
 
0.9%
과천시장애인생활이동지원센터 1
 
0.9%
Other values (91) 91
79.8%
2023-12-10T19:07:28.095870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
센 65
 
5.9%
터 65
 
5.9%
지 60
 
5.4%
인 57
 
5.2%
장 53
 
4.8%
애 53
 
4.8%
원 52
 
4.7%
시 49
 
4.4%
동 42
 
3.8%
이 38
 
3.4%
Other values (107) 572
51.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1092
98.7%
Space Separator 14
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
센 65
 
6.0%
터 65
 
6.0%
지 60
 
5.5%
인 57
 
5.2%
장 53
 
4.9%
애 53
 
4.9%
원 52
 
4.8%
시 49
 
4.5%
동 42
 
3.8%
이 38
 
3.5%
Other values (106) 558
51.1%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1092
98.7%
Common 14
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
센 65
 
6.0%
터 65
 
6.0%
지 60
 
5.5%
인 57
 
5.2%
장 53
 
4.9%
애 53
 
4.9%
원 52
 
4.8%
시 49
 
4.5%
동 42
 
3.8%
이 38
 
3.5%
Other values (106) 558
51.1%
Common
ValueCountFrequency (%)
14
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1092
98.7%
ASCII 14
 
1.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
센 65
 
6.0%
터 65
 
6.0%
지 60
 
5.5%
인 57
 
5.2%
장 53
 
4.9%
애 53
 
4.9%
원 52
 
4.8%
시 49
 
4.5%
동 42
 
3.8%
이 38
 
3.5%
Other values (106) 558
51.1%
ASCII
ValueCountFrequency (%)
14
100.0%

rn_adres
Text

Distinct89
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:07:28.607254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length27.5
Mean length20.19
Min length10

Characters and Unicode

Total characters2019
Distinct characters171
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)78.0%

Sample

1st row강원도 강릉시 사천면 방동길68
2nd row강원도 강릉시 용지각길20, 201호
3rd row강원도 강릉시 범일로 664
4th row강원도 강릉시 유산로25번길22
5th row강원도 강릉시 두산동 228-3
ValueCountFrequency (%)
강원도 56
 
12.2%
경기도 37
 
8.0%
강릉시 12
 
2.6%
춘천시 10
 
2.2%
고양시 5
 
1.1%
광명시 5
 
1.1%
일산서구 4
 
0.9%
중앙로 4
 
0.9%
김포시 4
 
0.9%
1층 3
 
0.7%
Other values (230) 320
69.6%
2023-12-10T19:07:29.564961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
360
 
17.8%
도 96
 
4.8%
1 86
 
4.3%
로 76
 
3.8%
강 74
 
3.7%
시 73
 
3.6%
원 69
 
3.4%
2 60
 
3.0%
경 42
 
2.1%
기 38
 
1.9%
Other values (161) 1045
51.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1199
59.4%
Decimal Number 371
 
18.4%
Space Separator 360
 
17.8%
Dash Punctuation 34
 
1.7%
Other Punctuation 20
 
1.0%
Open Punctuation 18
 
0.9%
Close Punctuation 17
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 96
 
8.0%
로 76
 
6.3%
강 74
 
6.2%
시 73
 
6.1%
원 69
 
5.8%
경 42
 
3.5%
기 38
 
3.2%
동 37
 
3.1%
군 31
 
2.6%
천 30
 
2.5%
Other values (144) 633
52.8%
Decimal Number
ValueCountFrequency (%)
1 86
23.2%
2 60
16.2%
3 35
9.4%
4 33
 
8.9%
9 31
 
8.4%
0 29
 
7.8%
6 28
 
7.5%
5 27
 
7.3%
7 24
 
6.5%
8 18
 
4.9%
Other Punctuation
ValueCountFrequency (%)
, 18
90.0%
. 1
 
5.0%
: 1
 
5.0%
Space Separator
ValueCountFrequency (%)
360
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1199
59.4%
Common 820
40.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 96
 
8.0%
로 76
 
6.3%
강 74
 
6.2%
시 73
 
6.1%
원 69
 
5.8%
경 42
 
3.5%
기 38
 
3.2%
동 37
 
3.1%
군 31
 
2.6%
천 30
 
2.5%
Other values (144) 633
52.8%
Common
ValueCountFrequency (%)
360
43.9%
1 86
 
10.5%
2 60
 
7.3%
3 35
 
4.3%
- 34
 
4.1%
4 33
 
4.0%
9 31
 
3.8%
0 29
 
3.5%
6 28
 
3.4%
5 27
 
3.3%
Other values (7) 97
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1199
59.4%
ASCII 820
40.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
360
43.9%
1 86
 
10.5%
2 60
 
7.3%
3 35
 
4.3%
- 34
 
4.1%
4 33
 
4.0%
9 31
 
3.8%
0 29
 
3.5%
6 28
 
3.4%
5 27
 
3.3%
Other values (7) 97
 
11.8%
Hangul
ValueCountFrequency (%)
도 96
 
8.0%
로 76
 
6.3%
강 74
 
6.2%
시 73
 
6.1%
원 69
 
5.8%
경 42
 
3.5%
기 38
 
3.2%
동 37
 
3.1%
군 31
 
2.6%
천 30
 
2.5%
Other values (144) 633
52.8%

ctprvn_klang_nm
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강원도
63 
경기도
37 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원도
2nd row강원도
3rd row강원도
4th row강원도
5th row강원도

Common Values

ValueCountFrequency (%)
강원도 63
63.0%
경기도 37
37.0%

Length

2023-12-10T19:07:29.810965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:29.985178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원도 63
63.0%
경기도 37
37.0%

signgn_klang_nm
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강릉시
12 
춘천시
11 
광명시
 
5
김포시
 
4
고양시 일산서구
 
4
Other values (25)
64 

Length

Max length8
Median length3
Mean length3.3
Min length3

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row강릉시
2nd row강릉시
3rd row강릉시
4th row강릉시
5th row강릉시

Common Values

ValueCountFrequency (%)
강릉시 12
 
12.0%
춘천시 11
 
11.0%
광명시 5
 
5.0%
김포시 4
 
4.0%
고양시 일산서구 4
 
4.0%
고성군 4
 
4.0%
가평군 3
 
3.0%
속초시 3
 
3.0%
영월군 3
 
3.0%
원주시 3
 
3.0%
Other values (20) 48
48.0%

Length

2023-12-10T19:07:30.191161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강릉시 12
 
11.4%
춘천시 11
 
10.5%
광명시 5
 
4.8%
고양시 5
 
4.8%
김포시 4
 
3.8%
일산서구 4
 
3.8%
고성군 4
 
3.8%
태백시 3
 
2.9%
평창군 3
 
2.9%
동두천시 3
 
2.9%
Other values (21) 51
48.6%

ctprvn_engl_nm
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Gangwon-do
63 
Gyeonggi-do
37 

Length

Max length11
Median length10
Mean length10.37
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGangwon-do
2nd rowGangwon-do
3rd rowGangwon-do
4th rowGangwon-do
5th rowGangwon-do

Common Values

ValueCountFrequency (%)
Gangwon-do 63
63.0%
Gyeonggi-do 37
37.0%

Length

2023-12-10T19:07:30.409186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:30.601285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
gangwon-do 63
63.0%
gyeonggi-do 37
37.0%

signgn_engl_nm
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Gangneung-si
12 
Chuncheon-si
11 
Gwangmyeong-si
 
5
Gimpo-si
 
4
Goyang-si Ilsanseo-gu
 
4
Other values (25)
64 

Length

Max length21
Median length14.5
Mean length11.8
Min length7

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st rowGangneung-si
2nd rowGangneung-si
3rd rowGangneung-si
4th rowGangneung-si
5th rowGangneung-si

Common Values

ValueCountFrequency (%)
Gangneung-si 12
 
12.0%
Chuncheon-si 11
 
11.0%
Gwangmyeong-si 5
 
5.0%
Gimpo-si 4
 
4.0%
Goyang-si Ilsanseo-gu 4
 
4.0%
Goseong-gun 4
 
4.0%
Gapyeong-gun 3
 
3.0%
Sokcho-si 3
 
3.0%
Yeongwol-gun 3
 
3.0%
Wonju-si 3
 
3.0%
Other values (20) 48
48.0%

Length

2023-12-10T19:07:30.790267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gangneung-si 12
 
11.4%
chuncheon-si 11
 
10.5%
gwangmyeong-si 5
 
4.8%
goyang-si 5
 
4.8%
gimpo-si 4
 
3.8%
ilsanseo-gu 4
 
3.8%
goseong-gun 4
 
3.8%
taebaek-si 3
 
2.9%
pyeongchang-gun 3
 
2.9%
dongducheon-si 3
 
2.9%
Other values (21) 51
48.6%

ctprvn_chnlng_nm
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
江原道
63 
京畿道
37 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row江原道
2nd row江原道
3rd row江原道
4th row江原道
5th row江原道

Common Values

ValueCountFrequency (%)
江原道 63
63.0%
京畿道 37
37.0%

Length

2023-12-10T19:07:31.042569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:31.235035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
江原道 63
63.0%
京畿道 37
37.0%

signgn_chnlng_nm
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
江陵市
12 
春川市
11 
光明市
 
5
金浦市
 
4
高陽市 一山西區
 
4
Other values (25)
64 

Length

Max length8
Median length3
Mean length3.3
Min length3

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row江陵市
2nd row江陵市
3rd row江陵市
4th row江陵市
5th row江陵市

Common Values

ValueCountFrequency (%)
江陵市 12
 
12.0%
春川市 11
 
11.0%
光明市 5
 
5.0%
金浦市 4
 
4.0%
高陽市 一山西區 4
 
4.0%
高城郡 4
 
4.0%
加平郡 3
 
3.0%
束草市 3
 
3.0%
寧越郡 3
 
3.0%
原州市 3
 
3.0%
Other values (20) 48
48.0%

Length

2023-12-10T19:07:31.453487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
江陵市 12
 
11.4%
春川市 11
 
10.5%
光明市 5
 
4.8%
高陽市 5
 
4.8%
金浦市 4
 
3.8%
一山西區 4
 
3.8%
高城郡 4
 
3.8%
太白市 3
 
2.9%
平昌郡 3
 
2.9%
東豆川市 3
 
2.9%
Other values (21) 51
48.6%

ctprvn_jlang_nm
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
江原道
63 
京畿道
37 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row江原道
2nd row江原道
3rd row江原道
4th row江原道
5th row江原道

Common Values

ValueCountFrequency (%)
江原道 63
63.0%
京畿道 37
37.0%

Length

2023-12-10T19:07:31.687058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:31.867497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
江原道 63
63.0%
京畿道 37
37.0%

signgn_jlang_nm
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
江陵市（カンヌンシ）
12 
春川市（チュンチョンシ）
11 
光明市（クァンミョンシ）
 
5
金浦市（キンポシ）
 
4
高陽市（コヤンシ）イルサンソグ
 
4
Other values (25)
64 

Length

Max length19
Median length14.5
Mean length11.24
Min length8

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row江陵市（カンヌンシ）
2nd row江陵市（カンヌンシ）
3rd row江陵市（カンヌンシ）
4th row江陵市（カンヌンシ）
5th row江陵市（カンヌンシ）

Common Values

ValueCountFrequency (%)
江陵市（カンヌンシ） 12
 
12.0%
春川市（チュンチョンシ） 11
 
11.0%
光明市（クァンミョンシ） 5
 
5.0%
金浦市（キンポシ） 4
 
4.0%
高陽市（コヤンシ）イルサンソグ 4
 
4.0%
高城郡（コソングン） 4
 
4.0%
加平郡（カピョングン） 3
 
3.0%
束草市（ソクチョシ） 3
 
3.0%
寧越郡（ヨンウォルグン） 3
 
3.0%
原州市（ウォンジュシ） 3
 
3.0%
Other values (20) 48
48.0%

Length

2023-12-10T19:07:32.106602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
江陵市（カンヌンシ） 12
 
12.0%
春川市（チュンチョンシ） 11
 
11.0%
光明市（クァンミョンシ） 5
 
5.0%
金浦市（キンポシ） 4
 
4.0%
高陽市（コヤンシ）イルサンソグ 4
 
4.0%
高城郡（コソングン） 4
 
4.0%
太白市（テベクシ） 3
 
3.0%
平昌郡（ピョンチャングン） 3
 
3.0%
東豆川市（トンドゥチョンシ） 3
 
3.0%
南揚州市（ナムヤンジュシ） 3
 
3.0%
Other values (20) 48
48.0%

city_do_cd
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
32
63 
31
37 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row32
2nd row32
3rd row32
4th row32
5th row32

Common Values

ValueCountFrequency (%)
32 63
63.0%
31 37
37.0%

Length

2023-12-10T19:07:32.352415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:32.550252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
32 63
63.0%
31 37
37.0%

city_gn_gu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31792.37
Minimum31050
Maximum32410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:32.738699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31050
5-th percentile31060
Q131160
median32020
Q332310
95-th percentile32400
Maximum32410
Range1360
Interquartile range (IQR)1150

Descriptive statistics

Standard deviation515.7758
Coefficient of variation (CV)0.016223257
Kurtosis-1.5835086
Mean31792.37
Median Absolute Deviation (MAD)335
Skewness-0.3750902
Sum3179237
Variance266024.68
MonotonicityNot monotonic
2023-12-10T19:07:32.969074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
32030 12
 
12.0%
32010 11
 
11.0%
31060 5
 
5.0%
31230 4
 
4.0%
31104 4
 
4.0%
32400 4
 
4.0%
32340 3
 
3.0%
31080 3
 
3.0%
31130 3
 
3.0%
31160 3
 
3.0%
Other values (20) 48
48.0%
ValueCountFrequency (%)
31050 2
 
2.0%
31060 5
5.0%
31080 3
3.0%
31101 1
 
1.0%
31104 4
4.0%
31110 3
3.0%
31120 3
3.0%
31130 3
3.0%
31160 3
3.0%
31230 4
4.0%
ValueCountFrequency (%)
32410 2
2.0%
32400 4
4.0%
32390 2
2.0%
32380 1
 
1.0%
32370 1
 
1.0%
32360 3
3.0%
32350 3
3.0%
32340 3
3.0%
32330 3
3.0%
32320 2
2.0%

lo
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.86629
Minimum126.65868
Maximum129.17648
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:33.230785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.65868
5-th percentile126.76182
Q1127.10221
median127.74983
Q3128.59208
95-th percentile129.05431
Maximum129.17648
Range2.5177972
Interquartile range (IQR)1.4898693

Descriptive statistics

Standard deviation0.80653009
Coefficient of variation (CV)0.0063076051
Kurtosis-1.4121581
Mean127.86629
Median Absolute Deviation (MAD)0.7483045
Skewness0.089041698
Sum12786.629
Variance0.65049078
MonotonicityNot monotonic
2023-12-10T19:07:33.476946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.7498325 3
 
3.0%
129.1080669 3
 
3.0%
128.5710657 2
 
2.0%
126.7645759 2
 
2.0%
126.8465456 2
 
2.0%
127.2201277 2
 
2.0%
127.8886131 2
 
2.0%
128.4631635 2
 
2.0%
128.627381 2
 
2.0%
127.0019934 2
 
2.0%
Other values (70) 78
78.0%
ValueCountFrequency (%)
126.6586825 1
1.0%
126.7080552 2
2.0%
126.7223133 1
1.0%
126.743078 1
1.0%
126.7628035 1
1.0%
126.7645759 2
2.0%
126.7731526 1
1.0%
126.813711 1
1.0%
126.8382596 1
1.0%
126.8465456 2
2.0%
ValueCountFrequency (%)
129.1764797 2
2.0%
129.1080669 3
3.0%
129.0514825 1
 
1.0%
128.9941172 1
 
1.0%
128.9938131 1
 
1.0%
128.9907693 1
 
1.0%
128.9298926 1
 
1.0%
128.9210687 1
 
1.0%
128.913343 1
 
1.0%
128.9123093 1
 
1.0%

la
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.689647
Minimum37.092361
Maximum38.381068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:33.729392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.092361
5-th percentile37.185246
Q137.447182
median37.674997
Q337.885569
95-th percentile38.203899
Maximum38.381068
Range1.2887065
Interquartile range (IQR)0.43838673

Descriptive statistics

Standard deviation0.29612813
Coefficient of variation (CV)0.0078570152
Kurtosis-0.34934981
Mean37.689647
Median Absolute Deviation (MAD)0.2188595
Skewness0.3050428
Sum3768.9647
Variance0.087691868
MonotonicityNot monotonic
2023-12-10T19:07:33.986748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.8968749 3
 
3.0%
37.5193584 3
 
3.0%
37.6537642 2
 
2.0%
37.7031591 2
 
2.0%
37.4790251 2
 
2.0%
38.2038993 2
 
2.0%
37.6944789 2
 
2.0%
37.1836025 2
 
2.0%
38.0776943 2
 
2.0%
37.4288073 2
 
2.0%
Other values (70) 78
78.0%
ValueCountFrequency (%)
37.0923611 1
1.0%
37.1493763 1
1.0%
37.1611397 1
1.0%
37.1836025 2
2.0%
37.1853321 1
1.0%
37.3164594 1
1.0%
37.3189735 1
1.0%
37.3525651 1
1.0%
37.3532634 1
1.0%
37.3599881 1
1.0%
ValueCountFrequency (%)
38.3810676 1
1.0%
38.378186 1
1.0%
38.37806 1
1.0%
38.2361083 1
1.0%
38.2038993 2
2.0%
38.2007201 1
1.0%
38.1975962 1
1.0%
38.1975129 1
1.0%
38.108706 1
1.0%
38.1039842 1
1.0%

hmpg_url
Text

MISSING 

Distinct6
Distinct (%)85.7%
Missing93
Missing (%)93.0%
Memory size932.0 B
2023-12-10T19:07:34.286863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length22.285714
Min length16

Characters and Unicode

Total characters156
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)71.4%

Sample

1st rowhttps://stjohns.co.kr
2nd rowhttps://ojuk.gtdc.or.kr
3rd rowwww.skybay.co.kr
4th rowhttps://lakaisandpine.co.kr
5th rowwww.hotel-topsten.co.kr
ValueCountFrequency (%)
https://stjohns.co.kr 2
28.6%
https://ojuk.gtdc.or.kr 1
14.3%
www.skybay.co.kr 1
14.3%
https://lakaisandpine.co.kr 1
14.3%
www.hotel-topsten.co.kr 1
14.3%
http://www.russohotel.com 1
14.3%
2023-12-10T19:07:34.923431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 17
 
10.9%
t 17
 
10.9%
o 14
 
9.0%
s 13
 
8.3%
/ 10
 
6.4%
h 9
 
5.8%
w 9
 
5.8%
k 9
 
5.8%
r 8
 
5.1%
c 7
 
4.5%
Other values (15) 43
27.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 123
78.8%
Other Punctuation 32
 
20.5%
Dash Punctuation 1
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 17
13.8%
o 14
11.4%
s 13
10.6%
h 9
 
7.3%
w 9
 
7.3%
k 9
 
7.3%
r 8
 
6.5%
c 7
 
5.7%
p 7
 
5.7%
n 5
 
4.1%
Other values (11) 25
20.3%
Other Punctuation
ValueCountFrequency (%)
. 17
53.1%
/ 10
31.2%
: 5
 
15.6%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 123
78.8%
Common 33
 
21.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 17
13.8%
o 14
11.4%
s 13
10.6%
h 9
 
7.3%
w 9
 
7.3%
k 9
 
7.3%
r 8
 
6.5%
c 7
 
5.7%
p 7
 
5.7%
n 5
 
4.1%
Other values (11) 25
20.3%
Common
ValueCountFrequency (%)
. 17
51.5%
/ 10
30.3%
: 5
 
15.2%
- 1
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 156
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 17
 
10.9%
t 17
 
10.9%
o 14
 
9.0%
s 13
 
8.3%
/ 10
 
6.4%
h 9
 
5.8%
w 9
 
5.8%
k 9
 
5.8%
r 8
 
5.1%
c 7
 
4.5%
Other values (15) 43
27.6%

tel_no
Text

MISSING 

Distinct87
Distinct (%)94.6%
Missing8
Missing (%)8.0%
Memory size932.0 B
2023-12-10T19:07:35.429146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length12
Min length9

Characters and Unicode

Total characters1104
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)90.2%

Sample

1st row033-643-1801
2nd row033-642-0432
3rd row033-641-8288
4th row033-262-4265
5th row033-660-9000
ValueCountFrequency (%)
033-255-2491 3
 
3.3%
02-2616-3700 2
 
2.2%
033-923-2000 2
 
2.2%
033-643-1801 2
 
2.2%
031-799-0303 1
 
1.1%
02-808-1444 1
 
1.1%
02-892-1388 1
 
1.1%
02-507-8001 1
 
1.1%
02-504-1142 1
 
1.1%
02-2185-8000 1
 
1.1%
Other values (77) 77
83.7%
2023-12-10T19:07:36.073694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 198
17.9%
- 183
16.6%
0 172
15.6%
1 87
7.9%
2 79
 
7.2%
5 77
 
7.0%
4 72
 
6.5%
6 64
 
5.8%
8 62
 
5.6%
7 58
 
5.3%
Other values (3) 52
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 919
83.2%
Dash Punctuation 183
 
16.6%
Math Symbol 1
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 198
21.5%
0 172
18.7%
1 87
9.5%
2 79
 
8.6%
5 77
 
8.4%
4 72
 
7.8%
6 64
 
7.0%
8 62
 
6.7%
7 58
 
6.3%
9 50
 
5.4%
Dash Punctuation
ValueCountFrequency (%)
- 183
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1104
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 198
17.9%
- 183
16.6%
0 172
15.6%
1 87
7.9%
2 79
 
7.2%
5 77
 
7.0%
4 72
 
6.5%
6 64
 
5.8%
8 62
 
5.6%
7 58
 
5.3%
Other values (3) 52
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1104
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 198
17.9%
- 183
16.6%
0 172
15.6%
1 87
7.9%
2 79
 
7.2%
5 77
 
7.0%
4 72
 
6.5%
6 64
 
5.8%
8 62
 
5.6%
7 58
 
5.3%
Other values (3) 52
 
4.7%

BASE_YMD
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2020-12-09 00:00:00
Maximum2020-12-09 00:00:00
2023-12-10T19:07:36.283274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:36.447604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-10T19:07:24.655128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:23.555964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:24.132645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:24.854215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:23.725347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:24.331462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:25.023050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:23.904415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:24.484493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:07:36.599559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
se_nmentrp_nmrn_adresctprvn_klang_nmsigngn_klang_nmctprvn_engl_nmsigngn_engl_nmctprvn_chnlng_nmsigngn_chnlng_nmctprvn_jlang_nmsigngn_jlang_nmcity_do_cdcity_gn_gu_cdlolahmpg_urltel_no
se_nm1.0001.0000.9020.8340.0000.8340.0000.8340.0000.8340.0000.8340.6270.5970.344NaN0.972
entrp_nm1.0001.0000.9381.0000.9771.0000.9771.0000.9771.0000.9771.0000.9700.0000.9281.0000.998
rn_adres0.9020.9381.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.8970.995
ctprvn_klang_nm0.8341.0001.0001.0001.0000.9991.0000.9991.0000.9991.0000.9991.0000.9960.594NaN1.000
signgn_klang_nm0.0000.9771.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9950.993NaN0.991
ctprvn_engl_nm0.8341.0001.0000.9991.0001.0001.0000.9991.0000.9991.0000.9991.0000.9960.594NaN1.000
signgn_engl_nm0.0000.9771.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9950.993NaN0.991
ctprvn_chnlng_nm0.8341.0001.0000.9991.0000.9991.0001.0001.0000.9991.0000.9991.0000.9960.594NaN1.000
signgn_chnlng_nm0.0000.9771.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9950.993NaN0.991
ctprvn_jlang_nm0.8341.0001.0000.9991.0000.9991.0000.9991.0001.0001.0000.9991.0000.9960.594NaN1.000
signgn_jlang_nm0.0000.9771.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9950.993NaN0.991
city_do_cd0.8341.0001.0000.9991.0000.9991.0000.9991.0000.9991.0001.0001.0000.9960.594NaN1.000
city_gn_gu_cd0.6270.9701.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9510.772NaN0.993
lo0.5970.0001.0000.9960.9950.9960.9950.9960.9950.9960.9950.9960.9511.0000.8521.0000.000
la0.3440.9281.0000.5940.9930.5940.9930.5940.9930.5940.9930.5940.7720.8521.0001.0000.968
hmpg_urlNaN1.0000.897NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0001.0001.0000.897
tel_no0.9720.9980.9951.0000.9911.0000.9911.0000.9911.0000.9911.0000.9930.0000.9680.8971.000
2023-12-10T19:07:36.910188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_do_cdctprvn_jlang_nmsigngn_klang_nmctprvn_engl_nmsigngn_jlang_nmsigngn_chnlng_nmsigngn_engl_nmctprvn_chnlng_nmse_nmctprvn_klang_nm
city_do_cd1.0000.9780.8450.9780.8450.8450.8450.9780.6380.978
ctprvn_jlang_nm0.9781.0000.8450.9780.8450.8450.8450.9780.6380.978
signgn_klang_nm0.8450.8451.0000.8451.0001.0001.0000.8450.0000.845
ctprvn_engl_nm0.9780.9780.8451.0000.8450.8450.8450.9780.6380.978
signgn_jlang_nm0.8450.8451.0000.8451.0001.0001.0000.8450.0000.845
signgn_chnlng_nm0.8450.8451.0000.8451.0001.0001.0000.8450.0000.845
signgn_engl_nm0.8450.8451.0000.8451.0001.0001.0000.8450.0000.845
ctprvn_chnlng_nm0.9780.9780.8450.9780.8450.8450.8451.0000.6380.978
se_nm0.6380.6380.0000.6380.0000.0000.0000.6381.0000.638
ctprvn_klang_nm0.9780.9780.8450.9780.8450.8450.8450.9780.6381.000
2023-12-10T19:07:37.159222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_gn_gu_cdlolase_nmctprvn_klang_nmsigngn_klang_nmctprvn_engl_nmsigngn_engl_nmctprvn_chnlng_nmsigngn_chnlng_nmctprvn_jlang_nmsigngn_jlang_nmcity_do_cd
city_gn_gu_cd1.0000.7430.2520.3040.9850.8580.9850.8580.9850.8580.9850.8580.985
lo0.7431.0000.0590.2180.9060.7790.9060.7790.9060.7790.9060.7790.906
la0.2520.0591.0000.1070.4390.7610.4390.7610.4390.7610.4390.7610.439
se_nm0.3040.2180.1071.0000.6380.0000.6380.0000.6380.0000.6380.0000.638
ctprvn_klang_nm0.9850.9060.4390.6381.0000.8450.9780.8450.9780.8450.9780.8450.978
signgn_klang_nm0.8580.7790.7610.0000.8451.0000.8451.0000.8451.0000.8451.0000.845
ctprvn_engl_nm0.9850.9060.4390.6380.9780.8451.0000.8450.9780.8450.9780.8450.978
signgn_engl_nm0.8580.7790.7610.0000.8451.0000.8451.0000.8451.0000.8451.0000.845
ctprvn_chnlng_nm0.9850.9060.4390.6380.9780.8450.9780.8451.0000.8450.9780.8450.978
signgn_chnlng_nm0.8580.7790.7610.0000.8451.0000.8451.0000.8451.0000.8451.0000.845
ctprvn_jlang_nm0.9850.9060.4390.6380.9780.8450.9780.8450.9780.8451.0000.8450.978
signgn_jlang_nm0.8580.7790.7610.0000.8451.0000.8451.0000.8451.0000.8451.0000.845
city_do_cd0.9850.9060.4390.6380.9780.8450.9780.8450.9780.8450.9780.8451.000

Missing values

2023-12-10T19:07:25.630270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:07:26.071609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-10T19:07:26.320031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

se_nmentrp_nmrn_adresctprvn_klang_nmsigngn_klang_nmctprvn_engl_nmsigngn_engl_nmctprvn_chnlng_nmsigngn_chnlng_nmctprvn_jlang_nmsigngn_jlang_nmcity_do_cdcity_gn_gu_cdlolahmpg_urltel_noBASE_YMD
0장애인복지관강릉시장애인종합복지관강원도 강릉시 사천면 방동길68강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.85629737.819776<NA>033-643-18012020-12-09
1수어통연센터강릉시수어통역센터강원도 강릉시 용지각길20, 201호강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.90577737.761362<NA>033-642-04322020-12-09
2생활이동지원센터강릉시장애인생활이동지원센터강원도 강릉시 범일로 664강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.884937.745083<NA>033-641-82882020-12-09
3장애인생산품판매시설강원도장애인생산품판매시설강원도 강릉시 유산로25번길22강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.91230937.741564<NA>033-262-42652020-12-09
4전국교통약자이동지원센터강릉시교통약자이동지원센터강원도 강릉시 두산동 228-3강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.92989337.769075<NA><NA>2020-12-09
5관광약자 숙박시설세인트존스호텔강릉시 창해로 307강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.92106937.791111https://stjohns.co.kr033-660-90002020-12-09
6관광약자 숙박시설오죽한옥마을강릉시 죽헌길 114강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.87642837.775567https://ojuk.gtdc.or.kr033-655-1117~1112020-12-09
7관광약자 숙박시설스카이베이호텔강릉시 해안로 476강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.90979737.800701www.skybay.co.kr033-923-20002020-12-09
8관광약자 숙박시설리카이샌드파인호텔강릉시 해안로 53강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030128.90443437.806132https://lakaisandpine.co.kr1644-30012020-12-09
9관광약자 숙박시설탑스텐호텔강릉시 옥계면 헌화로 455-34강원도강릉시Gangwon-doGangneung-si江原道江陵市江原道江陵市（カンヌンシ）3232030129.05148237.653282www.hotel-topsten.co.kr033-530-48002020-12-09
se_nmentrp_nmrn_adresctprvn_klang_nmsigngn_klang_nmctprvn_engl_nmsigngn_engl_nmctprvn_chnlng_nmsigngn_chnlng_nmctprvn_jlang_nmsigngn_jlang_nmcity_do_cdcity_gn_gu_cdlolahmpg_urltel_noBASE_YMD
90생활이동지원센터김포시 장애인생활이동지원센터경기도 김포시 감암로 125(걸포동)경기도김포시Gyeonggi-doGimpo-si京畿道金浦市京畿道金浦市（キンポシ）3131230126.70805537.643327<NA>031-983-18522020-12-09
91재활치료시설장애아동재활치료센터경기도 김포시 사우중로 100(사우동)경기도김포시Gyeonggi-doGimpo-si京畿道金浦市京畿道金浦市（キンポシ）3131230126.72231337.62299<NA>031-989-86622020-12-09
92장애인복지관남양주시장애인복지관경기도 남양주시 홍유릉로 273-1 (금곡동)경기도남양주시Gyeonggi-doNamyangju-si京畿道南楊州市京畿道南揚州市（ナムヤンジュシ）3131130127.20013337.627003<NA>031-592-71502020-12-09
93수어통역센터남양주시수어통역센터경기도 남양주시 금곡로 58, 3층 (금곡동, 은하빌딩)경기도남양주시Gyeonggi-doNamyangju-si京畿道南楊州市京畿道南揚州市（ナムヤンジュシ）3131130127.20959237.634052<NA>031-575-56472020-12-09
94생활이동지원센터남양주시장애인생활이동지원센터경기도 남양주시 경춘로 1298, 313호 (평내동)경기도남양주시Gyeonggi-doNamyangju-si京畿道南楊州市京畿道南揚州市（ナムヤンジュシ）3131130127.23499437.644963<NA>031-591-26872020-12-09
95장애인복지관동두천시장애인종합복지관경기도 동두천시 상패로 64경기도동두천시Gyeonggi-doDongducheon-si京畿道東豆川市京畿道東豆川市（トンドゥチョンシ）3131080127.04788437.90733<NA>031-867-00802020-12-09
96수어통역센터동두천시 수어통역센터경기도 동두천시 동광로174(2층)경기도동두천시Gyeonggi-doDongducheon-si京畿道東豆川市京畿道東豆川市（トンドゥチョンシ）3131080127.06055137.915532<NA>031-865-66782020-12-09
97생활이동지원센터동두천시장애인생활이동지원센터경기도 동두천시 동광로 174경기도동두천시Gyeonggi-doDongducheon-si京畿道東豆川市京畿道東豆川市（トンドゥチョンシ）3131080127.06055137.915532<NA>031-865-00302020-12-09
98장애인복지관부천시장애인종합복지관경기도 부천시 역곡로 367경기도부천시Gyeonggi-doBucheon-si京畿道富川市京畿道富川市（プチョンシ）3131050126.81371137.517511<NA>032-670-11002020-12-09
99수어통역센터부천시수어통역센터경기도 부천시 상이로39번길 7-20경기도부천시Gyeonggi-doBucheon-si京畿道富川市京畿道富川市（プチョンシ）3131050126.74307837.491964<NA>032-328-50182020-12-09