Overview

Dataset statistics

Number of variables13
Number of observations1000
Missing cells36
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory107.6 KiB
Average record size in memory110.1 B

Variable types

Numeric6
Categorical1
Text5
Boolean1

Alerts

COUNTRY_NM has constant value ""Constant
RSTRNT_TEL_NO is highly overall correlated with RSTRNT_LAHigh correlation
RSTRNT_LA is highly overall correlated with RSTRNT_TEL_NOHigh correlation
RSTRNT_SNITAT_GRAD_CD is highly imbalanced (92.6%)Imbalance
RSTRNT_OPBIZ_DT has 20 (2.0%) missing valuesMissing
RSTRNT_ID has unique valuesUnique
RSTRNT_LA has unique valuesUnique
RSTRNT_LO has unique valuesUnique
RSTRNT_AVRG_SCORE_CO has 923 (92.3%) zerosZeros

Reproduction

Analysis started2023-12-10 10:00:50.929401
Analysis finished2023-12-10 10:01:03.193927
Duration12.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

RSTRNT_ID
Real number (ℝ)

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99514.161
Minimum100
Maximum114001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-10T19:01:03.398716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile11168.5
Q1102019.25
median103898.5
Q3105972.75
95-th percentile112628.3
Maximum114001
Range113901
Interquartile range (IQR)3953.5

Descriptive statistics

Standard deviation22640.296
Coefficient of variation (CV)0.22750828
Kurtosis12.468923
Mean99514.161
Median Absolute Deviation (MAD)1962
Skewness-3.7375777
Sum99514161
Variance5.12583 × 108
MonotonicityNot monotonic
2023-12-10T19:01:03.717884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1
 
0.1%
105601 1
 
0.1%
105535 1
 
0.1%
105540 1
 
0.1%
105549 1
 
0.1%
105559 1
 
0.1%
105563 1
 
0.1%
105564 1
 
0.1%
105568 1
 
0.1%
105575 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
100 1
0.1%
101 1
0.1%
111 1
0.1%
112 1
0.1%
1002 1
0.1%
1022 1
0.1%
1038 1
0.1%
1045 1
0.1%
1062 1
0.1%
1086 1
0.1%
ValueCountFrequency (%)
114001 1
0.1%
113937 1
0.1%
113927 1
0.1%
113867 1
0.1%
113848 1
0.1%
113846 1
0.1%
113819 1
0.1%
113807 1
0.1%
113806 1
0.1%
113802 1
0.1%

COUNTRY_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
KOR
1000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
KOR 1000
100.0%

Length

2023-12-10T19:01:03.978201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:01:04.173215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kor 1000
100.0%

CTY_NM
Text

Distinct133
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-12-10T19:01:04.906570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length6.226
Min length4

Characters and Unicode

Total characters6226
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)2.9%

Sample

1st rowseoul
2nd rowwanju
3rd rowyongin
4th rowcheongju
5th roweumseong
ValueCountFrequency (%)
seoul 171
 
17.1%
busan 63
 
6.3%
incheon 56
 
5.6%
daegu 48
 
4.8%
gwangju 34
 
3.4%
daejeon 30
 
3.0%
suwon 24
 
2.4%
ulsan 21
 
2.1%
cheongju 20
 
2.0%
goyang 19
 
1.9%
Other values (123) 514
51.4%
2023-12-10T19:01:05.847535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 944
15.2%
o 707
11.4%
e 673
10.8%
u 589
9.5%
g 579
9.3%
a 567
9.1%
s 439
7.1%
h 253
 
4.1%
j 228
 
3.7%
l 201
 
3.2%
Other values (11) 1046
16.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6226
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 944
15.2%
o 707
11.4%
e 673
10.8%
u 589
9.5%
g 579
9.3%
a 567
9.1%
s 439
7.1%
h 253
 
4.1%
j 228
 
3.7%
l 201
 
3.2%
Other values (11) 1046
16.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 6226
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 944
15.2%
o 707
11.4%
e 673
10.8%
u 589
9.5%
g 579
9.3%
a 567
9.1%
s 439
7.1%
h 253
 
4.1%
j 228
 
3.7%
l 201
 
3.2%
Other values (11) 1046
16.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6226
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 944
15.2%
o 707
11.4%
e 673
10.8%
u 589
9.5%
g 579
9.3%
a 567
9.1%
s 439
7.1%
h 253
 
4.1%
j 228
 
3.7%
l 201
 
3.2%
Other values (11) 1046
16.8%

RSTRNT_NM
Text

Distinct995
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-12-10T19:01:06.527880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length15
Mean length6.054
Min length2

Characters and Unicode

Total characters6054
Distinct characters561
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique990 ?
Unique (%)99.0%

Sample

1st row강가 강남점
2nd rowOK부대찌개
3rd row페이머스치킨 에버랜드점
4th row더존김밥
5th row송골매
ValueCountFrequency (%)
감탄떡볶이 6
 
0.5%
본점 6
 
0.5%
코코호도 5
 
0.4%
강남점 4
 
0.3%
부평점 4
 
0.3%
놀부보쌈 3
 
0.2%
남원추어탕 3
 
0.2%
멕시카나치킨 2
 
0.2%
맛깔참죽 2
 
0.2%
처갓집양념치킨 2
 
0.2%
Other values (1185) 1210
97.0%
2023-12-10T19:01:07.456942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
247
 
4.1%
점 236
 
3.9%
당 102
 
1.7%
식 95
 
1.6%
수 88
 
1.5%
이 88
 
1.5%
가 86
 
1.4%
대 84
 
1.4%
국 82
 
1.4%
장 81
 
1.3%
Other values (551) 4865
80.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5732
94.7%
Space Separator 247
 
4.1%
Decimal Number 50
 
0.8%
Lowercase Letter 14
 
0.2%
Other Punctuation 5
 
0.1%
Uppercase Letter 5
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 236
 
4.1%
당 102
 
1.8%
식 95
 
1.7%
수 88
 
1.5%
이 88
 
1.5%
가 86
 
1.5%
대 84
 
1.5%
국 82
 
1.4%
장 81
 
1.4%
리 81
 
1.4%
Other values (529) 4709
82.2%
Decimal Number
ValueCountFrequency (%)
1 13
26.0%
0 11
22.0%
8 5
 
10.0%
2 5
 
10.0%
9 5
 
10.0%
6 3
 
6.0%
7 3
 
6.0%
4 2
 
4.0%
5 2
 
4.0%
3 1
 
2.0%
Lowercase Letter
ValueCountFrequency (%)
f 3
21.4%
e 3
21.4%
c 3
21.4%
a 3
21.4%
g 2
14.3%
Uppercase Letter
ValueCountFrequency (%)
D 2
40.0%
K 1
20.0%
O 1
20.0%
C 1
20.0%
Space Separator
ValueCountFrequency (%)
247
100.0%
Other Punctuation
ValueCountFrequency (%)
& 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5732
94.7%
Common 303
 
5.0%
Latin 19
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 236
 
4.1%
당 102
 
1.8%
식 95
 
1.7%
수 88
 
1.5%
이 88
 
1.5%
가 86
 
1.5%
대 84
 
1.5%
국 82
 
1.4%
장 81
 
1.4%
리 81
 
1.4%
Other values (529) 4709
82.2%
Common
ValueCountFrequency (%)
247
81.5%
1 13
 
4.3%
0 11
 
3.6%
8 5
 
1.7%
2 5
 
1.7%
& 5
 
1.7%
9 5
 
1.7%
6 3
 
1.0%
7 3
 
1.0%
4 2
 
0.7%
Other values (3) 4
 
1.3%
Latin
ValueCountFrequency (%)
f 3
15.8%
e 3
15.8%
c 3
15.8%
a 3
15.8%
g 2
10.5%
D 2
10.5%
K 1
 
5.3%
O 1
 
5.3%
C 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5732
94.7%
ASCII 322
 
5.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
247
76.7%
1 13
 
4.0%
0 11
 
3.4%
8 5
 
1.6%
2 5
 
1.6%
& 5
 
1.6%
9 5
 
1.6%
6 3
 
0.9%
f 3
 
0.9%
7 3
 
0.9%
Other values (12) 22
 
6.8%
Hangul
ValueCountFrequency (%)
점 236
 
4.1%
당 102
 
1.8%
식 95
 
1.7%
수 88
 
1.5%
이 88
 
1.5%
가 86
 
1.5%
대 84
 
1.5%
국 82
 
1.4%
장 81
 
1.4%
리 81
 
1.4%
Other values (529) 4709
82.2%

RSTRNT_CTGRY_FLAG_CD
Text

Distinct97
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-12-10T19:01:07.953806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length4.299
Min length1

Characters and Unicode

Total characters4299
Distinct characters166
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)1.8%

Sample

1st row인도음식
2nd row찌개,전골
3rd row치킨,닭강정
4th row종합분식
5th row한식
ValueCountFrequency (%)
한식 133
 
13.3%
육류,고기요리 84
 
8.4%
생선회 53
 
5.3%
치킨,닭강정 47
 
4.7%
해물,생선요리 47
 
4.7%
중식당 41
 
4.1%
돼지고기구이 33
 
3.3%
오리요리 28
 
2.8%
족발,보쌈 25
 
2.5%
한정식 22
 
2.2%
Other values (87) 487
48.7%
2023-12-10T19:01:08.753719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 373
 
8.7%
리 269
 
6.3%
식 259
 
6.0%
요 223
 
5.2%
한 156
 
3.6%
기 135
 
3.1%
고 134
 
3.1%
생 100
 
2.3%
선 100
 
2.3%
탕 88
 
2.0%
Other values (156) 2462
57.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3908
90.9%
Other Punctuation 373
 
8.7%
Uppercase Letter 12
 
0.3%
Close Punctuation 3
 
0.1%
Open Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 269
 
6.9%
식 259
 
6.6%
요 223
 
5.7%
한 156
 
4.0%
기 135
 
3.5%
고 134
 
3.4%
생 100
 
2.6%
선 100
 
2.6%
탕 88
 
2.3%
육 87
 
2.2%
Other values (148) 2357
60.3%
Uppercase Letter
ValueCountFrequency (%)
B 3
25.0%
A 3
25.0%
R 3
25.0%
D 2
16.7%
C 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
, 373
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3908
90.9%
Common 379
 
8.8%
Latin 12
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 269
 
6.9%
식 259
 
6.6%
요 223
 
5.7%
한 156
 
4.0%
기 135
 
3.5%
고 134
 
3.4%
생 100
 
2.6%
선 100
 
2.6%
탕 88
 
2.3%
육 87
 
2.2%
Other values (148) 2357
60.3%
Latin
ValueCountFrequency (%)
B 3
25.0%
A 3
25.0%
R 3
25.0%
D 2
16.7%
C 1
 
8.3%
Common
ValueCountFrequency (%)
, 373
98.4%
) 3
 
0.8%
( 3
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3908
90.9%
ASCII 391
 
9.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 373
95.4%
) 3
 
0.8%
( 3
 
0.8%
B 3
 
0.8%
A 3
 
0.8%
R 3
 
0.8%
D 2
 
0.5%
C 1
 
0.3%
Hangul
ValueCountFrequency (%)
리 269
 
6.9%
식 259
 
6.6%
요 223
 
5.7%
한 156
 
4.0%
기 135
 
3.5%
고 134
 
3.4%
생 100
 
2.6%
선 100
 
2.6%
탕 88
 
2.3%
육 87
 
2.2%
Other values (148) 2357
60.3%

RSTRNT_AVRG_SCORE_CO
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3127
Minimum0
Maximum5
Zeros923
Zeros (%)92.3%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-10T19:01:09.062153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0911826
Coefficient of variation (CV)3.4895509
Kurtosis8.8473567
Mean0.3127
Median Absolute Deviation (MAD)0
Skewness3.2598512
Sum312.7
Variance1.1906794
MonotonicityNot monotonic
2023-12-10T19:01:09.337014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.0 923
92.3%
4.0 36
 
3.6%
5.0 7
 
0.7%
4.5 7
 
0.7%
3.5 6
 
0.6%
3.0 5
 
0.5%
4.3 4
 
0.4%
3.7 3
 
0.3%
3.8 2
 
0.2%
3.9 2
 
0.2%
Other values (4) 5
 
0.5%
ValueCountFrequency (%)
0.0 923
92.3%
3.0 5
 
0.5%
3.5 6
 
0.6%
3.7 3
 
0.3%
3.8 2
 
0.2%
3.9 2
 
0.2%
4.0 36
 
3.6%
4.2 2
 
0.2%
4.3 4
 
0.4%
4.4 1
 
0.1%
ValueCountFrequency (%)
5.0 7
 
0.7%
4.9 1
 
0.1%
4.8 1
 
0.1%
4.5 7
 
0.7%
4.4 1
 
0.1%
4.3 4
 
0.4%
4.2 2
 
0.2%
4.0 36
3.6%
3.9 2
 
0.2%
3.8 2
 
0.2%

RSTRNT_OPBIZ_DT
Real number (ℝ)

MISSING 

Distinct901
Distinct (%)91.9%
Missing20
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean2.0024736 × 1013
Minimum1.9620117 × 1013
Maximum2.0200221 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-10T19:01:09.676615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9620117 × 1013
5-th percentile1.986101 × 1013
Q11.9980303 × 1013
median2.0040517 × 1013
Q32.0071128 × 1013
95-th percentile2.0140812 × 1013
Maximum2.0200221 × 1013
Range5.80104 × 1011
Interquartile range (IQR)9.082475 × 1010

Descriptive statistics

Standard deviation8.5185883 × 1010
Coefficient of variation (CV)0.0042540328
Kurtosis1.5379042
Mean2.0024736 × 1013
Median Absolute Deviation (MAD)5.0151 × 1010
Skewness-0.96530076
Sum1.9624241 × 1016
Variance7.2566347 × 1021
MonotonicityNot monotonic
2023-12-10T19:01:10.413288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20071030000000 3
 
0.3%
20070709000000 3
 
0.3%
20060526000000 3
 
0.3%
20010910000000 3
 
0.3%
20061114000000 2
 
0.2%
20110831000000 2
 
0.2%
19990702000000 2
 
0.2%
20130418000000 2
 
0.2%
19941128000000 2
 
0.2%
20070419000000 2
 
0.2%
Other values (891) 956
95.6%
(Missing) 20
 
2.0%
ValueCountFrequency (%)
19620117000000 1
0.1%
19651002000000 1
0.1%
19690715000000 1
0.1%
19710119000000 1
0.1%
19710724000000 1
0.1%
19721120000000 1
0.1%
19721127000000 1
0.1%
19740214000000 1
0.1%
19760611000000 1
0.1%
19760702000000 1
0.1%
ValueCountFrequency (%)
20200221000000 1
0.1%
20191024000000 1
0.1%
20181023000000 1
0.1%
20180712000000 1
0.1%
20160921000000 1
0.1%
20160516000000 1
0.1%
20160225000000 1
0.1%
20150819000000 1
0.1%
20150731000000 1
0.1%
20150720000000 1
0.1%

RSTRNT_SNITAT_GRAD_CD
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
991 
True
 
9
ValueCountFrequency (%)
False 991
99.1%
True 9
 
0.9%
2023-12-10T19:01:10.694930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

RSTRNT_LNM_ADDR
Text

Distinct999
Distinct (%)100.0%
Missing1
Missing (%)0.1%
Memory size7.9 KiB
2023-12-10T19:01:11.282326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length37
Mean length18.413413
Min length11

Characters and Unicode

Total characters18395
Distinct characters375
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique999 ?
Unique (%)100.0%

Sample

1st row서울 서초구 서초동 1320-10 삼성타운 지하1층
2nd row전북 완주군 삼례읍 삼례리 1419-12
3rd row경기 용인시 처인구 포곡읍 전대리 360-10 1층
4th row충북 청주시 서원구 개신동 613
5th row충북 음성군 음성읍 읍내리 512-1
ValueCountFrequency (%)
경기 204
 
4.5%
서울 171
 
3.7%
강원 72
 
1.6%
경남 67
 
1.5%
부산 63
 
1.4%
인천 56
 
1.2%
경북 54
 
1.2%
대구 48
 
1.0%
충남 48
 
1.0%
전남 45
 
1.0%
Other values (2207) 3750
81.9%
2023-12-10T19:01:12.347838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3581
 
19.5%
1 944
 
5.1%
동 916
 
5.0%
- 807
 
4.4%
구 660
 
3.6%
2 571
 
3.1%
3 489
 
2.7%
시 476
 
2.6%
4 388
 
2.1%
5 380
 
2.1%
Other values (365) 9183
49.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9666
52.5%
Decimal Number 4313
23.4%
Space Separator 3581
 
19.5%
Dash Punctuation 807
 
4.4%
Uppercase Letter 19
 
0.1%
Close Punctuation 3
 
< 0.1%
Other Punctuation 3
 
< 0.1%
Open Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 916
 
9.5%
구 660
 
6.8%
시 476
 
4.9%
경 343
 
3.5%
남 297
 
3.1%
서 296
 
3.1%
산 289
 
3.0%
리 269
 
2.8%
기 226
 
2.3%
울 197
 
2.0%
Other values (339) 5697
58.9%
Uppercase Letter
ValueCountFrequency (%)
F 4
21.1%
B 3
15.8%
A 3
15.8%
T 2
10.5%
I 1
 
5.3%
E 1
 
5.3%
Z 1
 
5.3%
O 1
 
5.3%
W 1
 
5.3%
G 1
 
5.3%
Decimal Number
ValueCountFrequency (%)
1 944
21.9%
2 571
13.2%
3 489
11.3%
4 388
9.0%
5 380
8.8%
6 354
 
8.2%
7 316
 
7.3%
0 307
 
7.1%
8 299
 
6.9%
9 265
 
6.1%
Space Separator
ValueCountFrequency (%)
3581
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 807
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9664
52.5%
Common 8710
47.3%
Latin 19
 
0.1%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 916
 
9.5%
구 660
 
6.8%
시 476
 
4.9%
경 343
 
3.5%
남 297
 
3.1%
서 296
 
3.1%
산 289
 
3.0%
리 269
 
2.8%
기 226
 
2.3%
울 197
 
2.0%
Other values (337) 5695
58.9%
Common
ValueCountFrequency (%)
3581
41.1%
1 944
 
10.8%
- 807
 
9.3%
2 571
 
6.6%
3 489
 
5.6%
4 388
 
4.5%
5 380
 
4.4%
6 354
 
4.1%
7 316
 
3.6%
0 307
 
3.5%
Other values (5) 573
 
6.6%
Latin
ValueCountFrequency (%)
F 4
21.1%
B 3
15.8%
A 3
15.8%
T 2
10.5%
I 1
 
5.3%
E 1
 
5.3%
Z 1
 
5.3%
O 1
 
5.3%
W 1
 
5.3%
G 1
 
5.3%
Han
ValueCountFrequency (%)
山 1
50.0%
花 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9664
52.5%
ASCII 8729
47.5%
CJK 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3581
41.0%
1 944
 
10.8%
- 807
 
9.2%
2 571
 
6.5%
3 489
 
5.6%
4 388
 
4.4%
5 380
 
4.4%
6 354
 
4.1%
7 316
 
3.6%
0 307
 
3.5%
Other values (16) 592
 
6.8%
Hangul
ValueCountFrequency (%)
동 916
 
9.5%
구 660
 
6.8%
시 476
 
4.9%
경 343
 
3.5%
남 297
 
3.1%
서 296
 
3.1%
산 289
 
3.0%
리 269
 
2.8%
기 226
 
2.3%
울 197
 
2.0%
Other values (337) 5695
58.9%
CJK
ValueCountFrequency (%)
山 1
50.0%
花 1
50.0%

RSTRNT_ROAD_NM_ADDR
Text

Distinct992
Distinct (%)100.0%
Missing8
Missing (%)0.8%
Memory size7.9 KiB
2023-12-10T19:01:13.611816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length35
Mean length19.642137
Min length11

Characters and Unicode

Total characters19485
Distinct characters468
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique992 ?
Unique (%)100.0%

Sample

1st row서울특별시 서초구 서초대로74길 11 삼성전자 서초사옥 지하1층
2nd row전북 완주군 삼례읍 삼례역로 20-1
3rd row경기 용인시 처인구 포곡읍 전대로78번길 1 1층
4th row충북 청주시 서원구 경신로 65 개신주공1단지 정문
5th row충북 음성군 음성읍 예술로8번길 3
ValueCountFrequency (%)
경기 204
 
4.3%
서울특별시 171
 
3.6%
강원 71
 
1.5%
경남 65
 
1.4%
부산 62
 
1.3%
인천 56
 
1.2%
경북 54
 
1.1%
1층 53
 
1.1%
대구 48
 
1.0%
충남 47
 
1.0%
Other values (2123) 3903
82.4%
2023-12-10T19:01:15.182271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3743
 
19.2%
1 859
 
4.4%
로 837
 
4.3%
구 656
 
3.4%
시 656
 
3.4%
길 508
 
2.6%
2 470
 
2.4%
3 368
 
1.9%
경 364
 
1.9%
5 322
 
1.7%
Other values (458) 10702
54.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11918
61.2%
Space Separator 3743
 
19.2%
Decimal Number 3569
 
18.3%
Dash Punctuation 218
 
1.1%
Uppercase Letter 19
 
0.1%
Lowercase Letter 9
 
< 0.1%
Other Punctuation 5
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 837
 
7.0%
구 656
 
5.5%
시 656
 
5.5%
길 508
 
4.3%
경 364
 
3.1%
서 309
 
2.6%
남 307
 
2.6%
산 272
 
2.3%
대 263
 
2.2%
기 231
 
1.9%
Other values (423) 7515
63.1%
Uppercase Letter
ValueCountFrequency (%)
A 3
15.8%
T 3
15.8%
O 2
10.5%
W 2
10.5%
E 2
10.5%
G 2
10.5%
I 1
 
5.3%
B 1
 
5.3%
L 1
 
5.3%
Z 1
 
5.3%
Decimal Number
ValueCountFrequency (%)
1 859
24.1%
2 470
13.2%
3 368
10.3%
5 322
 
9.0%
4 314
 
8.8%
7 267
 
7.5%
6 261
 
7.3%
8 252
 
7.1%
0 237
 
6.6%
9 219
 
6.1%
Lowercase Letter
ValueCountFrequency (%)
e 4
44.4%
s 1
 
11.1%
g 1
 
11.1%
f 1
 
11.1%
a 1
 
11.1%
c 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
, 2
40.0%
' 1
20.0%
. 1
20.0%
· 1
20.0%
Space Separator
ValueCountFrequency (%)
3743
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 218
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11918
61.2%
Common 7539
38.7%
Latin 28
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 837
 
7.0%
구 656
 
5.5%
시 656
 
5.5%
길 508
 
4.3%
경 364
 
3.1%
서 309
 
2.6%
남 307
 
2.6%
산 272
 
2.3%
대 263
 
2.2%
기 231
 
1.9%
Other values (423) 7515
63.1%
Common
ValueCountFrequency (%)
3743
49.6%
1 859
 
11.4%
2 470
 
6.2%
3 368
 
4.9%
5 322
 
4.3%
4 314
 
4.2%
7 267
 
3.5%
6 261
 
3.5%
8 252
 
3.3%
0 237
 
3.1%
Other values (8) 446
 
5.9%
Latin
ValueCountFrequency (%)
e 4
14.3%
A 3
10.7%
T 3
10.7%
O 2
 
7.1%
W 2
 
7.1%
E 2
 
7.1%
G 2
 
7.1%
I 1
 
3.6%
B 1
 
3.6%
s 1
 
3.6%
Other values (7) 7
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11918
61.2%
ASCII 7566
38.8%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3743
49.5%
1 859
 
11.4%
2 470
 
6.2%
3 368
 
4.9%
5 322
 
4.3%
4 314
 
4.2%
7 267
 
3.5%
6 261
 
3.4%
8 252
 
3.3%
0 237
 
3.1%
Other values (24) 473
 
6.3%
Hangul
ValueCountFrequency (%)
로 837
 
7.0%
구 656
 
5.5%
시 656
 
5.5%
길 508
 
4.3%
경 364
 
3.1%
서 309
 
2.6%
남 307
 
2.6%
산 272
 
2.3%
대 263
 
2.2%
기 231
 
1.9%
Other values (423) 7515
63.1%
None
ValueCountFrequency (%)
· 1
100.0%

RSTRNT_TEL_NO
Real number (ℝ)

HIGH CORRELATION 

Distinct993
Distinct (%)100.0%
Missing7
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean4.1969504 × 108
Minimum23075005
Maximum7.088091 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-10T19:01:15.582415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23075005
5-th percentile25838144
Q13.1483548 × 108
median3.3763656 × 108
Q35.38543 × 108
95-th percentile6.3245302 × 108
Maximum7.088091 × 109
Range7.065016 × 109
Interquartile range (IQR)2.2370752 × 108

Descriptive statistics

Standard deviation5.0431926 × 108
Coefficient of variation (CV)1.2016326
Kurtosis149.27968
Mean4.1969504 × 108
Median Absolute Deviation (MAD)1.1562259 × 108
Skewness11.469374
Sum4.1675717 × 1011
Variance2.5433791 × 1017
MonotonicityNot monotonic
2023-12-10T19:01:15.986475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
614529906 1
 
0.1%
618347016 1
 
0.1%
539844200 1
 
0.1%
617429496 1
 
0.1%
24991413 1
 
0.1%
312040003 1
 
0.1%
538118534 1
 
0.1%
329326836 1
 
0.1%
312442446 1
 
0.1%
616435300 1
 
0.1%
Other values (983) 983
98.3%
(Missing) 7
 
0.7%
ValueCountFrequency (%)
23075005 1
0.1%
23126439 1
0.1%
23184249 1
0.1%
23229778 1
0.1%
23259253 1
0.1%
23338747 1
0.1%
23364479 1
0.1%
23857452 1
0.1%
23886659 1
0.1%
23913441 1
0.1%
ValueCountFrequency (%)
7088091004 1
0.1%
7078084862 1
0.1%
7042162012 1
0.1%
7041399290 1
0.1%
7040429299 1
0.1%
647968049 1
0.1%
647928092 1
0.1%
647837615 1
0.1%
647821089 1
0.1%
647598488 1
0.1%

RSTRNT_LA
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.612047
Minimum33.234557
Maximum38.213835
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-10T19:01:16.360652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.234557
5-th percentile34.916819
Q135.728948
median37.049876
Q337.532962
95-th percentile37.76534
Maximum38.213835
Range4.9792773
Interquartile range (IQR)1.8040146

Descriptive statistics

Standard deviation1.0755544
Coefficient of variation (CV)0.029377063
Kurtosis-0.6048658
Mean36.612047
Median Absolute Deviation (MAD)0.61322915
Skewness-0.65604866
Sum36612.047
Variance1.1568173
MonotonicityNot monotonic
2023-12-10T19:01:16.780413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.4963654 1
 
0.1%
37.573716 1
 
0.1%
34.8517654 1
 
0.1%
37.568342 1
 
0.1%
37.250195 1
 
0.1%
35.8242493 1
 
0.1%
37.7410726 1
 
0.1%
37.2680521 1
 
0.1%
34.7385061 1
 
0.1%
37.7961914 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
33.2345573 1
0.1%
33.2386417 1
0.1%
33.2463252 1
0.1%
33.2594759 1
0.1%
33.2610624 1
0.1%
33.4406029 1
0.1%
33.461193 1
0.1%
33.488375 1
0.1%
33.4902845 1
0.1%
33.496141 1
0.1%
ValueCountFrequency (%)
38.2138346 1
0.1%
38.2092899 1
0.1%
38.2074935 1
0.1%
38.2040027 1
0.1%
38.2031719 1
0.1%
38.1968552 1
0.1%
38.1959814 1
0.1%
38.1921462 1
0.1%
38.1905205 1
0.1%
38.185576 1
0.1%

RSTRNT_LO
Real number (ℝ)

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.56922
Minimum124.71441
Maximum130.87311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-10T19:01:17.617191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum124.71441
5-th percentile126.60012
Q1126.93778
median127.13547
Q3128.41951
95-th percentile129.16702
Maximum130.87311
Range6.1587013
Interquartile range (IQR)1.4817288

Descriptive statistics

Standard deviation0.87988643
Coefficient of variation (CV)0.0068973253
Kurtosis-0.65600178
Mean127.56922
Median Absolute Deviation (MAD)0.36711575
Skewness0.73388494
Sum127569.22
Variance0.77420013
MonotonicityNot monotonic
2023-12-10T19:01:17.974475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0265789 1
 
0.1%
127.05767 1
 
0.1%
127.4914875 1
 
0.1%
127.082752 1
 
0.1%
127.08039 1
 
0.1%
128.7376086 1
 
0.1%
126.493179 1
 
0.1%
127.0018839 1
 
0.1%
127.7179768 1
 
0.1%
127.1060092 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
124.7144077 1
0.1%
126.2100452 1
0.1%
126.2840458 1
0.1%
126.308092 1
0.1%
126.3252303 1
0.1%
126.327353 1
0.1%
126.3344456 1
0.1%
126.370444 1
0.1%
126.373493 1
0.1%
126.3767232 1
0.1%
ValueCountFrequency (%)
130.873109 1
0.1%
129.5707189 1
0.1%
129.4458347 1
0.1%
129.4321457 1
0.1%
129.4234993 1
0.1%
129.4016585 1
0.1%
129.385619 1
0.1%
129.3841356 1
0.1%
129.3810741 1
0.1%
129.3802246 1
0.1%

Interactions

2023-12-10T19:01:00.552704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:53.263335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:54.858336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:56.093648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:57.475449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:59.171207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:00.762702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:53.472252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:55.113908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:56.273832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:57.782519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:59.377526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:00.962196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:53.672558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:55.285659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:56.466590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:57.995021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:59.666184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:01.194501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:54.288099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:55.496807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:56.658613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:58.265919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:59.843277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:01.401955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:54.474380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:55.711058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:56.910691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:58.697790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:00.085419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:01.607766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:54.649916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:55.908170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:57.158283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:58.941779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:00.305558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:01:18.219467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RSTRNT_IDRSTRNT_CTGRY_FLAG_CDRSTRNT_AVRG_SCORE_CORSTRNT_OPBIZ_DTRSTRNT_SNITAT_GRAD_CDRSTRNT_TEL_NORSTRNT_LARSTRNT_LO
RSTRNT_ID1.0000.4350.0720.1500.0000.0220.1810.090
RSTRNT_CTGRY_FLAG_CD0.4351.0000.4260.0000.0000.1290.0000.000
RSTRNT_AVRG_SCORE_CO0.0720.4261.0000.1950.0000.0000.1980.085
RSTRNT_OPBIZ_DT0.1500.0000.1951.0000.0000.1830.0620.036
RSTRNT_SNITAT_GRAD_CD0.0000.0000.0000.0001.0000.0000.1330.046
RSTRNT_TEL_NO0.0220.1290.0000.1830.0001.0000.0000.000
RSTRNT_LA0.1810.0000.1980.0620.1330.0001.0000.619
RSTRNT_LO0.0900.0000.0850.0360.0460.0000.6191.000
2023-12-10T19:01:18.593059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RSTRNT_IDRSTRNT_AVRG_SCORE_CORSTRNT_OPBIZ_DTRSTRNT_TEL_NORSTRNT_LARSTRNT_LORSTRNT_SNITAT_GRAD_CD
RSTRNT_ID1.000-0.1280.1440.062-0.1020.0330.000
RSTRNT_AVRG_SCORE_CO-0.1281.000-0.038-0.2700.188-0.1240.000
RSTRNT_OPBIZ_DT0.144-0.0381.000-0.0420.023-0.0590.000
RSTRNT_TEL_NO0.062-0.270-0.0421.000-0.8110.3530.000
RSTRNT_LA-0.1020.1880.023-0.8111.000-0.3540.133
RSTRNT_LO0.033-0.124-0.0590.353-0.3541.0000.035
RSTRNT_SNITAT_GRAD_CD0.0000.0000.0000.0000.1330.0351.000

Missing values

2023-12-10T19:01:02.011765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:01:02.640404image/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.
2023-12-10T19:01:03.015865image/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

RSTRNT_IDCOUNTRY_NMCTY_NMRSTRNT_NMRSTRNT_CTGRY_FLAG_CDRSTRNT_AVRG_SCORE_CORSTRNT_OPBIZ_DTRSTRNT_SNITAT_GRAD_CDRSTRNT_LNM_ADDRRSTRNT_ROAD_NM_ADDRRSTRNT_TEL_NORSTRNT_LARSTRNT_LO
0100KORseoul강가 강남점인도음식4.020081031000000N서울 서초구 서초동 1320-10 삼성타운 지하1층서울특별시 서초구 서초대로74길 11 삼성전자 서초사옥 지하1층22055361037.496365127.026579
1100013KORwanjuOK부대찌개찌개,전골0.020131224000000N전북 완주군 삼례읍 삼례리 1419-12전북 완주군 삼례읍 삼례역로 20-163291398835.909538127.070812
2100014KORyongin페이머스치킨 에버랜드점치킨,닭강정0.019981016000000N경기 용인시 처인구 포곡읍 전대리 360-10 1층경기 용인시 처인구 포곡읍 전대로78번길 1 1층31339717037.285177127.217306
310004KORcheongju더존김밥종합분식0.020021102000000N충북 청주시 서원구 개신동 613충북 청주시 서원구 경신로 65 개신주공1단지 정문43236228736.621845127.44898
4100056KOReumseong송골매한식0.020150608000000N충북 음성군 음성읍 읍내리 512-1충북 음성군 음성읍 예술로8번길 343873484436.935967127.688279
5100119KORhongcheon북카페 더숲카페,디저트0.020081127000000N강원 홍천군 남면 양덕원리 239-3강원 홍천군 남면 양덕원3길 42 북카페 더숲<NA>37.618899127.768495
6100143KORgimpo김밥천국 양곡점분식0.020100719000000N경기 김포시 양촌읍 양곡리 419-4경기 김포시 양촌읍 양곡1로 5431981768637.65531126.623524
7100154KORsuwon스시사랑초밥,롤0.020150413000000N경기 수원시 권선구 세류동 845경기 수원시 권선구 세지로 93 웅희빌딩 1층31222740937.260371127.018402
81002KORseoul춘향고을육류,고기요리0.019951218000000N서울 송파구 방이동 36-1서울특별시 송파구 올림픽로32길 523431143337.51556127.10902
910022KORgimhae나루꼬치오뎅,꼬치0.020031117000000N경남 김해시 삼정동 637-5경남 김해시 인제로51번길 2455327715835.233188128.902352
RSTRNT_IDCOUNTRY_NMCTY_NMRSTRNT_NMRSTRNT_CTGRY_FLAG_CDRSTRNT_AVRG_SCORE_CORSTRNT_OPBIZ_DTRSTRNT_SNITAT_GRAD_CDRSTRNT_LNM_ADDRRSTRNT_ROAD_NM_ADDRRSTRNT_TEL_NORSTRNT_LARSTRNT_LO
990113807KORjeongseon동강숯불촌육류,고기요리0.019950719000000N강원 정선군 신동읍 조동리 206-25강원 정선군 신동읍 조동8길 5-833378765037.215669128.669052
991113819KORdaegu대복한우리식육식당소고기구이0.020001103000000N대구 수성구 황금동 690-15대구 수성구 청솔로4길 6053761375135.847576128.623702
992113846KORhoengseong소잡는날본점소고기구이0.019971205000000N강원 횡성군 서원면 유현리 1183-2강원 횡성군 서원면 서원서로 85433344270137.523686127.807612
993113848KORseoul청진동해장국 화곡1호점해장국0.019930729000000N서울 강서구 화곡본동 70-46서울특별시 강서구 초록마을로 522692222437.54495126.845235
994113867KORincheon호식이두마리치킨 삼산1호점치킨,닭강정0.020110721000000N인천 부평구 삼산동 431-8인천 부평구 충선로311번길 11-732526227937.516509126.734755
9951139KORseoul가마목육류,고기요리0.019970704000000N서울 종로구 관철동 44-3서울특별시 종로구 종로8길 162737498737.569385126.983899
996113927KORbusan동원숯불갈비돼지고기구이0.019841110000000N부산 연제구 연산동 366-47 1층부산 연제구 과정로276번길 9 1층51864237935.188577129.097152
997113937KORpohang치킨과바람피자 포항남구점치킨,닭강정0.020000302000000N경북 포항시 남구 대잠동 946-5경북 포항시 남구 새천년대로434번길 20-5 1층 치킨과바람피자54283078936.013174129.346383
99811396KORansan베이지데이지카페,디저트0.020140418000000N경기 안산시 단원구 고잔동 768경기 안산시 단원구 광덕대로 174 A동 133호31403665237.311699126.830962
999114001KORchangwon대패폭탄무한리필육류,고기요리0.020140512000000N경남 창원시 마산회원구 회원동 50-5경남 창원시 마산회원구 북성로 138 영사우나 2층55223062235.219206128.57108