Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells60999
Missing cells (%)33.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory162.0 B

Variable types

Numeric9
Categorical2
Text6
Unsupported1

Dataset

Description시군구코드,업종명,업태명,허가신고일,폐업일자,교부번호,업소명,소재지도로명,소재지지번,행정동명,법정동명,영업장면적(㎡),지상_부터,지상_까지,지하_부터,지하_까지,총층수,업소위치
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-13656/S/1/datasetView.do

Alerts

시군구코드 is highly overall correlated with 교부번호High correlation
허가신고일 is highly overall correlated with 교부번호High correlation
교부번호 is highly overall correlated with 시군구코드 and 1 other fieldsHigh correlation
지상_부터 is highly overall correlated with 지상_까지High correlation
지상_까지 is highly overall correlated with 지상_부터High correlation
지하_부터 is highly overall correlated with 지하_까지 and 1 other fieldsHigh correlation
지하_까지 is highly overall correlated with 지하_부터 and 1 other fieldsHigh correlation
총층수 is highly overall correlated with 지하_부터 and 1 other fieldsHigh correlation
폐업일자 has 10000 (100.0%) missing valuesMissing
영업장면적(㎡) has 9463 (94.6%) missing valuesMissing
지상_부터 has 6961 (69.6%) missing valuesMissing
지상_까지 has 7005 (70.0%) missing valuesMissing
지하_부터 has 9253 (92.5%) missing valuesMissing
지하_까지 has 9264 (92.6%) missing valuesMissing
총층수 has 9033 (90.3%) missing valuesMissing
지하_부터 is highly skewed (γ1 = 26.50362792)Skewed
지하_까지 is highly skewed (γ1 = 26.33505887)Skewed
폐업일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-11 04:48:57.322593
Analysis finished2024-05-11 04:49:34.459632
Duration37.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3137168
Minimum3000000
Maximum3240000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T04:49:34.646573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000000
5-th percentile3010000
Q13070000
median3150000
Q33210000
95-th percentile3230000
Maximum3240000
Range240000
Interquartile range (IQR)140000

Descriptive statistics

Standard deviation73738.616
Coefficient of variation (CV)0.023504835
Kurtosis-1.1717522
Mean3137168
Median Absolute Deviation (MAD)60000
Skewness-0.3178433
Sum3.137168 × 1010
Variance5.4373835 × 109
MonotonicityNot monotonic
2024-05-11T04:49:35.054777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
3220000 1073
 
10.7%
3130000 708
 
7.1%
3230000 697
 
7.0%
3150000 561
 
5.6%
3180000 551
 
5.5%
3210000 512
 
5.1%
3200000 411
 
4.1%
3240000 385
 
3.9%
3010000 354
 
3.5%
3030000 350
 
3.5%
Other values (15) 4398
44.0%
ValueCountFrequency (%)
3000000 278
2.8%
3010000 354
3.5%
3020000 343
3.4%
3030000 350
3.5%
3040000 323
3.2%
3050000 332
3.3%
3060000 271
2.7%
3070000 294
2.9%
3080000 279
2.8%
3090000 182
1.8%
ValueCountFrequency (%)
3240000 385
 
3.9%
3230000 697
7.0%
3220000 1073
10.7%
3210000 512
5.1%
3200000 411
 
4.1%
3190000 248
 
2.5%
3180000 551
5.5%
3170000 263
 
2.6%
3160000 342
 
3.4%
3150000 561
5.6%

업종명
Categorical

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반음식점
4435 
건강기능식품일반판매업
1929 
휴게음식점
1604 
즉석판매제조가공업
824 
유통전문판매업
 
357
Other values (15)
851 

Length

Max length13
Median length5
Mean length6.7953
Min length4

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row즉석판매제조가공업
2nd row건강기능식품일반판매업
3rd row건강기능식품일반판매업
4th row휴게음식점
5th row위탁급식영업

Common Values

ValueCountFrequency (%)
일반음식점 4435
44.4%
건강기능식품일반판매업 1929
19.3%
휴게음식점 1604
 
16.0%
즉석판매제조가공업 824
 
8.2%
유통전문판매업 357
 
3.6%
식품자동판매기영업 238
 
2.4%
제과점영업 177
 
1.8%
건강기능식품유통전문판매업 143
 
1.4%
식품소분업 79
 
0.8%
위탁급식영업 75
 
0.8%
Other values (10) 139
 
1.4%

Length

2024-05-11T04:49:35.498482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반음식점 4435
44.4%
건강기능식품일반판매업 1929
19.3%
휴게음식점 1604
 
16.0%
즉석판매제조가공업 824
 
8.2%
유통전문판매업 357
 
3.6%
식품자동판매기영업 238
 
2.4%
제과점영업 177
 
1.8%
건강기능식품유통전문판매업 143
 
1.4%
식품소분업 79
 
0.8%
위탁급식영업 75
 
0.8%
Other values (10) 139
 
1.4%
Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T04:49:35.979399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length5.6659
Min length2

Characters and Unicode

Total characters56659
Distinct characters145
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st row즉석판매제조가공업
2nd row전자상거래(통신판매업)
3rd row전자상거래(통신판매업)
4th row기타 휴게음식점
5th row위탁급식영업
ValueCountFrequency (%)
기타 1739
16.5%
한식 1639
15.6%
전자상거래(통신판매업 1478
14.1%
즉석판매제조가공업 824
 
7.8%
커피숍 722
 
6.9%
휴게음식점 420
 
4.0%
영업장판매 361
 
3.4%
유통전문판매업 357
 
3.4%
일식 315
 
3.0%
경양식 299
 
2.8%
Other values (56) 2356
22.4%
2024-05-11T04:49:36.961706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3908
 
6.9%
3844
 
6.8%
3621
 
6.4%
3600
 
6.4%
2216
 
3.9%
2162
 
3.8%
2125
 
3.8%
1767
 
3.1%
) 1724
 
3.0%
( 1724
 
3.0%
Other values (135) 29968
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 52312
92.3%
Close Punctuation 1724
 
3.0%
Open Punctuation 1724
 
3.0%
Space Separator 510
 
0.9%
Other Punctuation 389
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3908
 
7.5%
3844
 
7.3%
3621
 
6.9%
3600
 
6.9%
2216
 
4.2%
2162
 
4.1%
2125
 
4.1%
1767
 
3.4%
1716
 
3.3%
1639
 
3.1%
Other values (129) 25714
49.2%
Other Punctuation
ValueCountFrequency (%)
/ 253
65.0%
, 135
34.7%
. 1
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 1724
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1724
100.0%
Space Separator
ValueCountFrequency (%)
510
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 52312
92.3%
Common 4347
 
7.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3908
 
7.5%
3844
 
7.3%
3621
 
6.9%
3600
 
6.9%
2216
 
4.2%
2162
 
4.1%
2125
 
4.1%
1767
 
3.4%
1716
 
3.3%
1639
 
3.1%
Other values (129) 25714
49.2%
Common
ValueCountFrequency (%)
) 1724
39.7%
( 1724
39.7%
510
 
11.7%
/ 253
 
5.8%
, 135
 
3.1%
. 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 52312
92.3%
ASCII 4347
 
7.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3908
 
7.5%
3844
 
7.3%
3621
 
6.9%
3600
 
6.9%
2216
 
4.2%
2162
 
4.1%
2125
 
4.1%
1767
 
3.4%
1716
 
3.3%
1639
 
3.1%
Other values (129) 25714
49.2%
ASCII
ValueCountFrequency (%)
) 1724
39.7%
( 1724
39.7%
510
 
11.7%
/ 253
 
5.8%
, 135
 
3.1%
. 1
 
< 0.1%

허가신고일
Real number (ℝ)

HIGH CORRELATION 

Distinct357
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20233048
Minimum20221206
Maximum20240510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T04:49:37.395609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20221206
5-th percentile20230106
Q120230421
median20230831
Q320240116
95-th percentile20240423
Maximum20240510
Range19304
Interquartile range (IQR)9695

Descriptive statistics

Standard deviation4902.356
Coefficient of variation (CV)0.00024229448
Kurtosis-0.35101988
Mean20233048
Median Absolute Deviation (MAD)426
Skewness0.31956173
Sum2.0233048 × 1011
Variance24033094
MonotonicityNot monotonic
2024-05-11T04:49:37.897375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20240507 56
 
0.6%
20240429 51
 
0.5%
20240412 50
 
0.5%
20240229 50
 
0.5%
20240419 48
 
0.5%
20230831 46
 
0.5%
20240510 45
 
0.4%
20240304 45
 
0.4%
20240102 44
 
0.4%
20240503 44
 
0.4%
Other values (347) 9521
95.2%
ValueCountFrequency (%)
20221206 5
 
0.1%
20221207 21
0.2%
20221208 26
0.3%
20221209 30
0.3%
20221212 21
0.2%
20221213 23
0.2%
20221214 15
0.1%
20221215 31
0.3%
20221216 22
0.2%
20221219 20
0.2%
ValueCountFrequency (%)
20240510 45
0.4%
20240509 39
0.4%
20240508 42
0.4%
20240507 56
0.6%
20240503 44
0.4%
20240502 39
0.4%
20240501 22
 
0.2%
20240430 43
0.4%
20240429 51
0.5%
20240426 35
0.4%

폐업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

교부번호
Real number (ℝ)

HIGH CORRELATION 

Distinct9984
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0232576 × 1010
Minimum2.0220028 × 1010
Maximum2.0240561 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T04:49:38.442615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0220028 × 1010
5-th percentile2.0230036 × 1010
Q12.023008 × 1010
median2.0230121 × 1010
Q32.0240054 × 1010
95-th percentile2.0240145 × 1010
Maximum2.0240561 × 1010
Range20533275
Interquartile range (IQR)9974709.8

Descriptive statistics

Standard deviation5098036.4
Coefficient of variation (CV)0.00025197169
Kurtosis-0.29161363
Mean2.0232576 × 1010
Median Absolute Deviation (MAD)49308.5
Skewness0.29903716
Sum2.0232576 × 1014
Variance2.5989975 × 1013
MonotonicityNot monotonic
2024-05-11T04:49:38.919699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20240076083 2
 
< 0.1%
20230071742 2
 
< 0.1%
20230071350 2
 
< 0.1%
20240076289 2
 
< 0.1%
20240076259 2
 
< 0.1%
20230071567 2
 
< 0.1%
20230071824 2
 
< 0.1%
20230071706 2
 
< 0.1%
20230071498 2
 
< 0.1%
20230071509 2
 
< 0.1%
Other values (9974) 9980
99.8%
ValueCountFrequency (%)
20220027850 1
< 0.1%
20220027851 1
< 0.1%
20220027854 1
< 0.1%
20220027862 1
< 0.1%
20220027867 1
< 0.1%
20220027869 1
< 0.1%
20220027877 1
< 0.1%
20220027882 1
< 0.1%
20220027884 1
< 0.1%
20220027889 1
< 0.1%
ValueCountFrequency (%)
20240561125 1
< 0.1%
20240535245 1
< 0.1%
20240520001 1
< 0.1%
20240161676 1
< 0.1%
20240161672 1
< 0.1%
20240161671 1
< 0.1%
20240161666 1
< 0.1%
20240161665 1
< 0.1%
20240161663 1
< 0.1%
20240161656 1
< 0.1%
Distinct9781
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T04:49:39.875602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length34
Mean length8.1755
Min length1

Characters and Unicode

Total characters81755
Distinct characters1193
Distinct categories13 ?
Distinct scripts5 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9591 ?
Unique (%)95.9%

Sample

1st row아미까(Amigga)
2nd row황금쇼핑gs
3rd row더한커머스
4th row사계 김밥
5th rowJF 한국우편사업진흥원점
ValueCountFrequency (%)
주식회사 429
 
2.8%
카페 93
 
0.6%
씨유 83
 
0.5%
세븐일레븐 70
 
0.5%
coffee 54
 
0.3%
메가엠지씨커피 51
 
0.3%
지에스25 35
 
0.2%
커피 35
 
0.2%
gs25 33
 
0.2%
강남점 31
 
0.2%
Other values (11622) 14603
94.1%
2024-05-11T04:49:41.033842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5524
 
6.8%
2536
 
3.1%
2105
 
2.6%
1788
 
2.2%
) 1627
 
2.0%
( 1620
 
2.0%
1129
 
1.4%
1041
 
1.3%
809
 
1.0%
727
 
0.9%
Other values (1183) 62849
76.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 62532
76.5%
Space Separator 5524
 
6.8%
Lowercase Letter 4568
 
5.6%
Uppercase Letter 4254
 
5.2%
Close Punctuation 1629
 
2.0%
Open Punctuation 1622
 
2.0%
Decimal Number 1258
 
1.5%
Other Punctuation 305
 
0.4%
Dash Punctuation 33
 
< 0.1%
Connector Punctuation 15
 
< 0.1%
Other values (3) 15
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2536
 
4.1%
2105
 
3.4%
1788
 
2.9%
1129
 
1.8%
1041
 
1.7%
809
 
1.3%
727
 
1.2%
697
 
1.1%
673
 
1.1%
627
 
1.0%
Other values (1092) 50400
80.6%
Lowercase Letter
ValueCountFrequency (%)
e 697
15.3%
o 446
 
9.8%
a 433
 
9.5%
r 289
 
6.3%
n 289
 
6.3%
i 275
 
6.0%
t 248
 
5.4%
l 215
 
4.7%
s 200
 
4.4%
f 192
 
4.2%
Other values (16) 1284
28.1%
Uppercase Letter
ValueCountFrequency (%)
E 381
 
9.0%
A 339
 
8.0%
C 315
 
7.4%
O 310
 
7.3%
S 298
 
7.0%
T 205
 
4.8%
G 203
 
4.8%
B 203
 
4.8%
R 194
 
4.6%
N 193
 
4.5%
Other values (16) 1613
37.9%
Other Punctuation
ValueCountFrequency (%)
& 115
37.7%
. 63
20.7%
, 44
 
14.4%
' 23
 
7.5%
23
 
7.5%
! 9
 
3.0%
: 9
 
3.0%
# 6
 
2.0%
/ 4
 
1.3%
3
 
1.0%
Other values (3) 6
 
2.0%
Decimal Number
ValueCountFrequency (%)
2 320
25.4%
5 207
16.5%
1 180
14.3%
4 123
 
9.8%
3 94
 
7.5%
9 91
 
7.2%
0 81
 
6.4%
8 62
 
4.9%
7 61
 
4.8%
6 39
 
3.1%
Math Symbol
ValueCountFrequency (%)
> 2
28.6%
+ 2
28.6%
< 2
28.6%
× 1
14.3%
Other Symbol
ValueCountFrequency (%)
2
40.0%
1
20.0%
° 1
20.0%
1
20.0%
Close Punctuation
ValueCountFrequency (%)
) 1627
99.9%
] 2
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 1620
99.9%
[ 2
 
0.1%
Space Separator
ValueCountFrequency (%)
5524
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 33
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 15
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 62488
76.4%
Common 10399
 
12.7%
Latin 8822
 
10.8%
Han 37
 
< 0.1%
Hiragana 9
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2536
 
4.1%
2105
 
3.4%
1788
 
2.9%
1129
 
1.8%
1041
 
1.7%
809
 
1.3%
727
 
1.2%
697
 
1.1%
673
 
1.1%
627
 
1.0%
Other values (1052) 50356
80.6%
Latin
ValueCountFrequency (%)
e 697
 
7.9%
o 446
 
5.1%
a 433
 
4.9%
E 381
 
4.3%
A 339
 
3.8%
C 315
 
3.6%
O 310
 
3.5%
S 298
 
3.4%
r 289
 
3.3%
n 289
 
3.3%
Other values (42) 5025
57.0%
Common
ValueCountFrequency (%)
5524
53.1%
) 1627
 
15.6%
( 1620
 
15.6%
2 320
 
3.1%
5 207
 
2.0%
1 180
 
1.7%
4 123
 
1.2%
& 115
 
1.1%
3 94
 
0.9%
9 91
 
0.9%
Other values (28) 498
 
4.8%
Han
ValueCountFrequency (%)
2
 
5.4%
2
 
5.4%
2
 
5.4%
2
 
5.4%
2
 
5.4%
1
 
2.7%
1
 
2.7%
1
 
2.7%
1
 
2.7%
1
 
2.7%
Other values (22) 22
59.5%
Hiragana
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 62479
76.4%
ASCII 19191
 
23.5%
CJK 37
 
< 0.1%
None 30
 
< 0.1%
Hiragana 9
 
< 0.1%
Compat Jamo 7
 
< 0.1%
Misc Symbols 1
 
< 0.1%
Letterlike Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5524
28.8%
) 1627
 
8.5%
( 1620
 
8.4%
e 697
 
3.6%
o 446
 
2.3%
a 433
 
2.3%
E 381
 
2.0%
A 339
 
1.8%
2 320
 
1.7%
C 315
 
1.6%
Other values (74) 7489
39.0%
Hangul
ValueCountFrequency (%)
2536
 
4.1%
2105
 
3.4%
1788
 
2.9%
1129
 
1.8%
1041
 
1.7%
809
 
1.3%
727
 
1.2%
697
 
1.1%
673
 
1.1%
627
 
1.0%
Other values (1045) 50347
80.6%
None
ValueCountFrequency (%)
23
76.7%
3
 
10.0%
2
 
6.7%
× 1
 
3.3%
° 1
 
3.3%
CJK
ValueCountFrequency (%)
2
 
5.4%
2
 
5.4%
2
 
5.4%
2
 
5.4%
2
 
5.4%
1
 
2.7%
1
 
2.7%
1
 
2.7%
1
 
2.7%
1
 
2.7%
Other values (22) 22
59.5%
Compat Jamo
ValueCountFrequency (%)
2
28.6%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Hiragana
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Misc Symbols
ValueCountFrequency (%)
1
100.0%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%
Distinct9647
Distinct (%)96.6%
Missing10
Missing (%)0.1%
Memory size156.2 KiB
2024-05-11T04:49:41.724031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length77
Median length65
Mean length36.547447
Min length22

Characters and Unicode

Total characters365109
Distinct characters693
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9438 ?
Unique (%)94.5%

Sample

1st row서울특별시 마포구 숭문길 98, 1층 109호 (염리동, 마포자이 더 센트리지)
2nd row서울특별시 서대문구 증가로 150, 101동 902호 (남가좌동, DMC센트럴아이파크)
3rd row서울특별시 금천구 가산디지털1로 119, SK트윈테크타워 B동 412(일부)호 (가산동)
4th row서울특별시 마포구 양화로7안길 38, 1층 (서교동)
5th row서울특별시 영등포구 영중로 83, 직원식당 8층 (영등포동7가)
ValueCountFrequency (%)
서울특별시 9990
 
14.0%
1층 4158
 
5.8%
강남구 1073
 
1.5%
지하1층 858
 
1.2%
2층 805
 
1.1%
마포구 708
 
1.0%
송파구 697
 
1.0%
강서구 560
 
0.8%
영등포구 549
 
0.8%
서초구 510
 
0.7%
Other values (11364) 51351
72.1%
2024-05-11T04:49:43.203633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
61272
 
16.8%
1 19800
 
5.4%
13607
 
3.7%
12405
 
3.4%
, 12310
 
3.4%
10878
 
3.0%
10769
 
2.9%
10435
 
2.9%
10197
 
2.8%
) 10127
 
2.8%
Other values (683) 193309
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 205058
56.2%
Decimal Number 62109
 
17.0%
Space Separator 61272
 
16.8%
Other Punctuation 12348
 
3.4%
Close Punctuation 10128
 
2.8%
Open Punctuation 10128
 
2.8%
Dash Punctuation 1870
 
0.5%
Uppercase Letter 1860
 
0.5%
Lowercase Letter 231
 
0.1%
Math Symbol 61
 
< 0.1%
Other values (3) 44
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
13607
 
6.6%
12405
 
6.0%
10878
 
5.3%
10769
 
5.3%
10435
 
5.1%
10197
 
5.0%
10002
 
4.9%
9993
 
4.9%
8296
 
4.0%
5769
 
2.8%
Other values (602) 102707
50.1%
Uppercase Letter
ValueCountFrequency (%)
B 489
26.3%
A 217
11.7%
C 129
 
6.9%
S 104
 
5.6%
T 84
 
4.5%
D 82
 
4.4%
E 82
 
4.4%
M 72
 
3.9%
R 66
 
3.5%
K 61
 
3.3%
Other values (16) 474
25.5%
Lowercase Letter
ValueCountFrequency (%)
e 52
22.5%
n 25
10.8%
b 20
 
8.7%
r 19
 
8.2%
t 13
 
5.6%
a 13
 
5.6%
w 12
 
5.2%
i 12
 
5.2%
o 12
 
5.2%
l 11
 
4.8%
Other values (11) 42
18.2%
Decimal Number
ValueCountFrequency (%)
1 19800
31.9%
2 9095
14.6%
0 6764
 
10.9%
3 6192
 
10.0%
4 4625
 
7.4%
5 3898
 
6.3%
6 3484
 
5.6%
7 3116
 
5.0%
8 2596
 
4.2%
9 2539
 
4.1%
Other Punctuation
ValueCountFrequency (%)
, 12310
99.7%
. 19
 
0.2%
/ 5
 
< 0.1%
& 4
 
< 0.1%
? 4
 
< 0.1%
2
 
< 0.1%
' 2
 
< 0.1%
1
 
< 0.1%
# 1
 
< 0.1%
Letter Number
ValueCountFrequency (%)
21
52.5%
12
30.0%
6
 
15.0%
1
 
2.5%
Math Symbol
ValueCountFrequency (%)
~ 59
96.7%
= 1
 
1.6%
+ 1
 
1.6%
Close Punctuation
ValueCountFrequency (%)
) 10127
> 99.9%
] 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 10127
> 99.9%
[ 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
61272
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1870
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Other Symbol
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 205060
56.2%
Common 157918
43.3%
Latin 2131
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
13607
 
6.6%
12405
 
6.0%
10878
 
5.3%
10769
 
5.3%
10435
 
5.1%
10197
 
5.0%
10002
 
4.9%
9993
 
4.9%
8296
 
4.0%
5769
 
2.8%
Other values (603) 102709
50.1%
Latin
ValueCountFrequency (%)
B 489
22.9%
A 217
 
10.2%
C 129
 
6.1%
S 104
 
4.9%
T 84
 
3.9%
D 82
 
3.8%
E 82
 
3.8%
M 72
 
3.4%
R 66
 
3.1%
K 61
 
2.9%
Other values (41) 745
35.0%
Common
ValueCountFrequency (%)
61272
38.8%
1 19800
 
12.5%
, 12310
 
7.8%
) 10127
 
6.4%
( 10127
 
6.4%
2 9095
 
5.8%
0 6764
 
4.3%
3 6192
 
3.9%
4 4625
 
2.9%
5 3898
 
2.5%
Other values (19) 13708
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 205058
56.2%
ASCII 160006
43.8%
Number Forms 40
 
< 0.1%
None 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
61272
38.3%
1 19800
 
12.4%
, 12310
 
7.7%
) 10127
 
6.3%
( 10127
 
6.3%
2 9095
 
5.7%
0 6764
 
4.2%
3 6192
 
3.9%
4 4625
 
2.9%
5 3898
 
2.4%
Other values (64) 15796
 
9.9%
Hangul
ValueCountFrequency (%)
13607
 
6.6%
12405
 
6.0%
10878
 
5.3%
10769
 
5.3%
10435
 
5.1%
10197
 
5.0%
10002
 
4.9%
9993
 
4.9%
8296
 
4.0%
5769
 
2.8%
Other values (602) 102707
50.1%
Number Forms
ValueCountFrequency (%)
21
52.5%
12
30.0%
6
 
15.0%
1
 
2.5%
None
ValueCountFrequency (%)
2
40.0%
2
40.0%
1
20.0%
Distinct8458
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T04:49:44.026600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length71
Median length52
Mean length29.0114
Min length19

Characters and Unicode

Total characters290114
Distinct characters653
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7644 ?
Unique (%)76.4%

Sample

1st row서울특별시 마포구 염리동 533번지 마포자이 더 센트리지
2nd row서울특별시 서대문구 남가좌동 388번지 DMC센트럴아이파크
3rd row서울특별시 금천구 가산동 345번지 9호 SK트윈테크타워 B동 412(일부)호
4th row서울특별시 마포구 서교동 380번지 17호
5th row서울특별시 영등포구 영등포동7가 47번지 2호
ValueCountFrequency (%)
서울특별시 10000
 
18.0%
강남구 1073
 
1.9%
1호 847
 
1.5%
마포구 708
 
1.3%
송파구 697
 
1.3%
강서구 561
 
1.0%
영등포구 551
 
1.0%
서초구 512
 
0.9%
2호 512
 
0.9%
3호 488
 
0.9%
Other values (6255) 39468
71.2%
2024-05-11T04:49:45.588819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
69709
24.0%
11963
 
4.1%
11595
 
4.0%
10711
 
3.7%
10646
 
3.7%
10335
 
3.6%
10146
 
3.5%
10070
 
3.5%
10005
 
3.4%
10003
 
3.4%
Other values (643) 124931
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 171494
59.1%
Space Separator 69709
24.0%
Decimal Number 46462
 
16.0%
Uppercase Letter 1077
 
0.4%
Dash Punctuation 745
 
0.3%
Lowercase Letter 228
 
0.1%
Other Punctuation 173
 
0.1%
Close Punctuation 82
 
< 0.1%
Open Punctuation 82
 
< 0.1%
Letter Number 42
 
< 0.1%
Other values (3) 20
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
11963
 
7.0%
11595
 
6.8%
10711
 
6.2%
10646
 
6.2%
10335
 
6.0%
10146
 
5.9%
10070
 
5.9%
10005
 
5.8%
10003
 
5.8%
8333
 
4.9%
Other values (567) 67687
39.5%
Uppercase Letter
ValueCountFrequency (%)
B 88
 
8.2%
C 88
 
8.2%
S 85
 
7.9%
A 79
 
7.3%
T 71
 
6.6%
E 61
 
5.7%
M 60
 
5.6%
K 59
 
5.5%
R 54
 
5.0%
D 50
 
4.6%
Other values (16) 382
35.5%
Lowercase Letter
ValueCountFrequency (%)
e 56
24.6%
n 27
11.8%
r 21
 
9.2%
o 19
 
8.3%
a 13
 
5.7%
w 13
 
5.7%
t 12
 
5.3%
i 12
 
5.3%
l 10
 
4.4%
c 9
 
3.9%
Other values (9) 36
15.8%
Decimal Number
ValueCountFrequency (%)
1 9888
21.3%
2 6164
13.3%
3 5055
10.9%
4 4379
9.4%
5 3913
 
8.4%
6 3858
 
8.3%
0 3688
 
7.9%
7 3459
 
7.4%
9 3112
 
6.7%
8 2946
 
6.3%
Other Punctuation
ValueCountFrequency (%)
, 148
85.5%
. 7
 
4.0%
& 5
 
2.9%
? 4
 
2.3%
/ 4
 
2.3%
' 2
 
1.2%
2
 
1.2%
1
 
0.6%
Letter Number
ValueCountFrequency (%)
21
50.0%
12
28.6%
8
 
19.0%
1
 
2.4%
Math Symbol
ValueCountFrequency (%)
~ 15
88.2%
= 1
 
5.9%
+ 1
 
5.9%
Space Separator
ValueCountFrequency (%)
69709
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 745
100.0%
Close Punctuation
ValueCountFrequency (%)
) 82
100.0%
Open Punctuation
ValueCountFrequency (%)
( 82
100.0%
Other Symbol
ValueCountFrequency (%)
2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 171496
59.1%
Common 117271
40.4%
Latin 1347
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
11963
 
7.0%
11595
 
6.8%
10711
 
6.2%
10646
 
6.2%
10335
 
6.0%
10146
 
5.9%
10070
 
5.9%
10005
 
5.8%
10003
 
5.8%
8333
 
4.9%
Other values (568) 67689
39.5%
Latin
ValueCountFrequency (%)
B 88
 
6.5%
C 88
 
6.5%
S 85
 
6.3%
A 79
 
5.9%
T 71
 
5.3%
E 61
 
4.5%
M 60
 
4.5%
K 59
 
4.4%
e 56
 
4.2%
R 54
 
4.0%
Other values (39) 646
48.0%
Common
ValueCountFrequency (%)
69709
59.4%
1 9888
 
8.4%
2 6164
 
5.3%
3 5055
 
4.3%
4 4379
 
3.7%
5 3913
 
3.3%
6 3858
 
3.3%
0 3688
 
3.1%
7 3459
 
2.9%
9 3112
 
2.7%
Other values (16) 4046
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 171494
59.1%
ASCII 118573
40.9%
Number Forms 42
 
< 0.1%
None 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
69709
58.8%
1 9888
 
8.3%
2 6164
 
5.2%
3 5055
 
4.3%
4 4379
 
3.7%
5 3913
 
3.3%
6 3858
 
3.3%
0 3688
 
3.1%
7 3459
 
2.9%
9 3112
 
2.6%
Other values (59) 5348
 
4.5%
Hangul
ValueCountFrequency (%)
11963
 
7.0%
11595
 
6.8%
10711
 
6.2%
10646
 
6.2%
10335
 
6.0%
10146
 
5.9%
10070
 
5.9%
10005
 
5.8%
10003
 
5.8%
8333
 
4.9%
Other values (567) 67687
39.5%
Number Forms
ValueCountFrequency (%)
21
50.0%
12
28.6%
8
 
19.0%
1
 
2.4%
None
ValueCountFrequency (%)
2
40.0%
2
40.0%
1
20.0%
Distinct424
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T04:49:46.328397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length7
Mean length4.1797
Min length2

Characters and Unicode

Total characters41797
Distinct characters188
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st row염리동
2nd row남가좌제2동
3rd row가산동
4th row서교동
5th row영등포동
ValueCountFrequency (%)
역삼1동 264
 
2.6%
신사동 190
 
1.9%
논현1동 175
 
1.8%
서교동 171
 
1.7%
가양제1동 131
 
1.3%
여의동 129
 
1.3%
가산동 118
 
1.2%
성수2가제1동 116
 
1.2%
삼성1동 106
 
1.1%
대치1동 99
 
1.0%
Other values (414) 8501
85.0%
2024-05-11T04:49:47.428232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10047
24.0%
3993
 
9.6%
1 3450
 
8.3%
2 1549
 
3.7%
812
 
1.9%
753
 
1.8%
3 721
 
1.7%
545
 
1.3%
490
 
1.2%
487
 
1.2%
Other values (178) 18950
45.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 34867
83.4%
Decimal Number 6560
 
15.7%
Other Punctuation 370
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10047
28.8%
3993
 
11.5%
812
 
2.3%
753
 
2.2%
545
 
1.6%
490
 
1.4%
487
 
1.4%
466
 
1.3%
452
 
1.3%
434
 
1.2%
Other values (167) 16388
47.0%
Decimal Number
ValueCountFrequency (%)
1 3450
52.6%
2 1549
23.6%
3 721
 
11.0%
4 481
 
7.3%
5 166
 
2.5%
6 95
 
1.4%
7 49
 
0.7%
8 40
 
0.6%
0 5
 
0.1%
9 4
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 370
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 34867
83.4%
Common 6930
 
16.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10047
28.8%
3993
 
11.5%
812
 
2.3%
753
 
2.2%
545
 
1.6%
490
 
1.4%
487
 
1.4%
466
 
1.3%
452
 
1.3%
434
 
1.2%
Other values (167) 16388
47.0%
Common
ValueCountFrequency (%)
1 3450
49.8%
2 1549
22.4%
3 721
 
10.4%
4 481
 
6.9%
. 370
 
5.3%
5 166
 
2.4%
6 95
 
1.4%
7 49
 
0.7%
8 40
 
0.6%
0 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 34867
83.4%
ASCII 6930
 
16.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
10047
28.8%
3993
 
11.5%
812
 
2.3%
753
 
2.2%
545
 
1.6%
490
 
1.4%
487
 
1.4%
466
 
1.3%
452
 
1.3%
434
 
1.2%
Other values (167) 16388
47.0%
ASCII
ValueCountFrequency (%)
1 3450
49.8%
2 1549
22.4%
3 721
 
10.4%
4 481
 
6.9%
. 370
 
5.3%
5 166
 
2.4%
6 95
 
1.4%
7 49
 
0.7%
8 40
 
0.6%
0 5
 
0.1%
Distinct413
Distinct (%)4.1%
Missing10
Missing (%)0.1%
Memory size156.2 KiB
2024-05-11T04:49:48.086241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.232032
Min length2

Characters and Unicode

Total characters32288
Distinct characters208
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

Unique49 ?
Unique (%)0.5%

Sample

1st row염리동
2nd row남가좌동
3rd row가산동
4th row서교동
5th row영등포동7가
ValueCountFrequency (%)
역삼동 270
 
2.7%
봉천동 200
 
2.0%
신림동 194
 
1.9%
논현동 189
 
1.9%
서초동 184
 
1.8%
마곡동 184
 
1.8%
신사동 167
 
1.7%
화곡동 160
 
1.6%
구로동 160
 
1.6%
상계동 140
 
1.4%
Other values (403) 8142
81.5%
2024-05-11T04:49:49.242947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9827
30.4%
1352
 
4.2%
862
 
2.7%
507
 
1.6%
459
 
1.4%
458
 
1.4%
445
 
1.4%
439
 
1.4%
421
 
1.3%
406
 
1.3%
Other values (198) 17112
53.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31246
96.8%
Decimal Number 1042
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9827
31.5%
1352
 
4.3%
862
 
2.8%
507
 
1.6%
459
 
1.5%
458
 
1.5%
445
 
1.4%
439
 
1.4%
421
 
1.3%
406
 
1.3%
Other values (190) 16070
51.4%
Decimal Number
ValueCountFrequency (%)
2 334
32.1%
1 248
23.8%
3 188
18.0%
4 100
 
9.6%
5 80
 
7.7%
6 50
 
4.8%
7 31
 
3.0%
8 11
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31246
96.8%
Common 1042
 
3.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
9827
31.5%
1352
 
4.3%
862
 
2.8%
507
 
1.6%
459
 
1.5%
458
 
1.5%
445
 
1.4%
439
 
1.4%
421
 
1.3%
406
 
1.3%
Other values (190) 16070
51.4%
Common
ValueCountFrequency (%)
2 334
32.1%
1 248
23.8%
3 188
18.0%
4 100
 
9.6%
5 80
 
7.7%
6 50
 
4.8%
7 31
 
3.0%
8 11
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31246
96.8%
ASCII 1042
 
3.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
9827
31.5%
1352
 
4.3%
862
 
2.8%
507
 
1.6%
459
 
1.5%
458
 
1.5%
445
 
1.4%
439
 
1.4%
421
 
1.3%
406
 
1.3%
Other values (190) 16070
51.4%
ASCII
ValueCountFrequency (%)
2 334
32.1%
1 248
23.8%
3 188
18.0%
4 100
 
9.6%
5 80
 
7.7%
6 50
 
4.8%
7 31
 
3.0%
8 11
 
1.1%

영업장면적(㎡)
Real number (ℝ)

MISSING 

Distinct390
Distinct (%)72.6%
Missing9463
Missing (%)94.6%
Infinite0
Infinite (%)0.0%
Mean95.010559
Minimum0
Maximum1835.1
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T04:49:49.677503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.3
Q117.08
median36.42
Q386.22
95-th percentile307.994
Maximum1835.1
Range1835.1
Interquartile range (IQR)69.14

Descriptive statistics

Standard deviation193.56317
Coefficient of variation (CV)2.0372807
Kurtosis31.833338
Mean95.010559
Median Absolute Deviation (MAD)25.97
Skewness5.1094873
Sum51020.67
Variance37466.703
MonotonicityNot monotonic
2024-05-11T04:49:50.151395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.3 14
 
0.1%
20.0 14
 
0.1%
10.0 9
 
0.1%
25.0 8
 
0.1%
30.0 7
 
0.1%
23.0 7
 
0.1%
110.75 6
 
0.1%
15.0 6
 
0.1%
0.0 6
 
0.1%
13.0 5
 
0.1%
Other values (380) 455
 
4.5%
(Missing) 9463
94.6%
ValueCountFrequency (%)
0.0 6
0.1%
1.0 3
< 0.1%
1.1 1
 
< 0.1%
1.65 3
< 0.1%
1.85 1
 
< 0.1%
2.0 2
 
< 0.1%
2.2 1
 
< 0.1%
2.4 1
 
< 0.1%
2.7 1
 
< 0.1%
2.8 2
 
< 0.1%
ValueCountFrequency (%)
1835.1 1
 
< 0.1%
1683.79 1
 
< 0.1%
1425.26 1
 
< 0.1%
1193.71 1
 
< 0.1%
1074.79 1
 
< 0.1%
1070.0 3
< 0.1%
1002.0 1
 
< 0.1%
870.88 1
 
< 0.1%
834.78 1
 
< 0.1%
708.59 1
 
< 0.1%

지상_부터
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)0.6%
Missing6961
Missing (%)69.6%
Infinite0
Infinite (%)0.0%
Mean1.3405726
Minimum0
Maximum41
Zeros33
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T04:49:50.584063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum41
Range41
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4498993
Coefficient of variation (CV)1.0815522
Kurtosis258.99268
Mean1.3405726
Median Absolute Deviation (MAD)0
Skewness12.740176
Sum4074
Variance2.1022078
MonotonicityNot monotonic
2024-05-11T04:49:50.956138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 2503
 
25.0%
2 319
 
3.2%
3 85
 
0.9%
0 33
 
0.3%
4 30
 
0.3%
5 22
 
0.2%
6 17
 
0.2%
7 8
 
0.1%
8 7
 
0.1%
11 4
 
< 0.1%
Other values (8) 11
 
0.1%
(Missing) 6961
69.6%
ValueCountFrequency (%)
0 33
 
0.3%
1 2503
25.0%
2 319
 
3.2%
3 85
 
0.9%
4 30
 
0.3%
5 22
 
0.2%
6 17
 
0.2%
7 8
 
0.1%
8 7
 
0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
41 1
 
< 0.1%
27 1
 
< 0.1%
24 1
 
< 0.1%
22 1
 
< 0.1%
16 1
 
< 0.1%
13 1
 
< 0.1%
11 4
< 0.1%
10 2
 
< 0.1%
9 3
< 0.1%
8 7
0.1%

지상_까지
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)0.6%
Missing7005
Missing (%)70.0%
Infinite0
Infinite (%)0.0%
Mean1.366611
Minimum0
Maximum41
Zeros33
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T04:49:51.335692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum41
Range41
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4502967
Coefficient of variation (CV)1.0612359
Kurtosis260.15411
Mean1.366611
Median Absolute Deviation (MAD)0
Skewness12.684908
Sum4093
Variance2.1033604
MonotonicityNot monotonic
2024-05-11T04:49:51.706490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 2404
 
24.0%
2 359
 
3.6%
3 97
 
1.0%
0 33
 
0.3%
4 32
 
0.3%
5 25
 
0.2%
6 17
 
0.2%
7 8
 
0.1%
8 6
 
0.1%
11 4
 
< 0.1%
Other values (7) 10
 
0.1%
(Missing) 7005
70.0%
ValueCountFrequency (%)
0 33
 
0.3%
1 2404
24.0%
2 359
 
3.6%
3 97
 
1.0%
4 32
 
0.3%
5 25
 
0.2%
6 17
 
0.2%
7 8
 
0.1%
8 6
 
0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
41 1
 
< 0.1%
27 1
 
< 0.1%
24 1
 
< 0.1%
22 1
 
< 0.1%
16 1
 
< 0.1%
11 4
< 0.1%
10 2
 
< 0.1%
9 3
 
< 0.1%
8 6
0.1%
7 8
0.1%

지하_부터
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct6
Distinct (%)0.8%
Missing9253
Missing (%)92.5%
Infinite0
Infinite (%)0.0%
Mean1.2650602
Minimum0
Maximum107
Zeros48
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T04:49:52.056786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile2
Maximum107
Range107
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.9138713
Coefficient of variation (CV)3.0938221
Kurtosis716.77568
Mean1.2650602
Median Absolute Deviation (MAD)0
Skewness26.503628
Sum945
Variance15.318389
MonotonicityNot monotonic
2024-05-11T04:49:52.436106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 580
 
5.8%
2 100
 
1.0%
0 48
 
0.5%
3 16
 
0.2%
5 2
 
< 0.1%
107 1
 
< 0.1%
(Missing) 9253
92.5%
ValueCountFrequency (%)
0 48
 
0.5%
1 580
5.8%
2 100
 
1.0%
3 16
 
0.2%
5 2
 
< 0.1%
107 1
 
< 0.1%
ValueCountFrequency (%)
107 1
 
< 0.1%
5 2
 
< 0.1%
3 16
 
0.2%
2 100
 
1.0%
1 580
5.8%
0 48
 
0.5%

지하_까지
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct6
Distinct (%)0.8%
Missing9264
Missing (%)92.6%
Infinite0
Infinite (%)0.0%
Mean1.2730978
Minimum0
Maximum109
Zeros48
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T04:49:52.805824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile2
Maximum109
Range109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.0159728
Coefficient of variation (CV)3.1544887
Kurtosis707.20918
Mean1.2730978
Median Absolute Deviation (MAD)0
Skewness26.335059
Sum937
Variance16.128037
MonotonicityNot monotonic
2024-05-11T04:49:53.152900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 568
 
5.7%
2 101
 
1.0%
0 48
 
0.5%
3 16
 
0.2%
5 2
 
< 0.1%
109 1
 
< 0.1%
(Missing) 9264
92.6%
ValueCountFrequency (%)
0 48
 
0.5%
1 568
5.7%
2 101
 
1.0%
3 16
 
0.2%
5 2
 
< 0.1%
109 1
 
< 0.1%
ValueCountFrequency (%)
109 1
 
< 0.1%
5 2
 
< 0.1%
3 16
 
0.2%
2 101
 
1.0%
1 568
5.7%
0 48
 
0.5%

총층수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)3.4%
Missing9033
Missing (%)90.3%
Infinite0
Infinite (%)0.0%
Mean6.918304
Minimum0
Maximum49
Zeros48
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T04:49:53.704761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q38
95-th percentile19
Maximum49
Range49
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.4857341
Coefficient of variation (CV)0.93747458
Kurtosis6.9356695
Mean6.918304
Median Absolute Deviation (MAD)2
Skewness2.2404602
Sum6690
Variance42.064747
MonotonicityNot monotonic
2024-05-11T04:49:54.130648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
4 179
 
1.8%
3 140
 
1.4%
5 134
 
1.3%
6 86
 
0.9%
2 59
 
0.6%
0 48
 
0.5%
7 44
 
0.4%
1 33
 
0.3%
17 26
 
0.3%
9 24
 
0.2%
Other values (23) 194
 
1.9%
(Missing) 9033
90.3%
ValueCountFrequency (%)
0 48
 
0.5%
1 33
 
0.3%
2 59
 
0.6%
3 140
1.4%
4 179
1.8%
5 134
1.3%
6 86
0.9%
7 44
 
0.4%
8 22
 
0.2%
9 24
 
0.2%
ValueCountFrequency (%)
49 1
 
< 0.1%
43 3
< 0.1%
39 3
< 0.1%
38 1
 
< 0.1%
29 5
0.1%
28 3
< 0.1%
26 1
 
< 0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 4
< 0.1%

업소위치
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
지상
6368 
<NA>
2254 
지하
1169 
지상2층이상
 
172
지상+지하
 
35

Length

Max length6
Median length2
Mean length2.5301
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지상
2nd row<NA>
3rd row지상
4th row지상
5th row지상

Common Values

ValueCountFrequency (%)
지상 6368
63.7%
<NA> 2254
 
22.5%
지하 1169
 
11.7%
지상2층이상 172
 
1.7%
지상+지하 35
 
0.4%
도선 2
 
< 0.1%

Length

2024-05-11T04:49:54.574281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:49:54.951338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지상 6368
63.7%
na 2254
 
22.5%
지하 1169
 
11.7%
지상2층이상 172
 
1.7%
지상+지하 35
 
0.4%
도선 2
 
< 0.1%

Interactions

2024-05-11T04:49:29.046701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:05.156182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:08.243052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:12.182913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:15.120404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:18.259142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:21.031593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:23.713921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:26.189229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:29.386685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:05.461803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:08.670139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:12.693605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:15.422080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:18.556487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:21.327171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:23.993745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:26.470897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:29.776894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:05.796914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:09.065948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:13.030540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:15.927383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:18.928883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:21.955052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:24.268447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:26.772190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:30.127718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:06.099001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:09.474305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:13.292510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:16.308588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:19.204677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:22.238348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:24.557026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:27.095530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:30.431903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:06.442990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:10.032467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:13.573249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:16.668306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:19.471478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:22.545465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:24.811123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:27.350949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:30.753602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:06.727226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:10.549721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:13.851975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:16.953138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:19.775409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:22.798186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:25.090758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:27.605428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:31.205937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:07.002367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:11.009819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:14.127484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:17.213005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:20.163428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:23.061626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:25.372460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:27.875950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:31.569823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:07.339672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:11.431618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:14.433800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:17.514024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:20.458281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:23.220208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:25.654620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:28.315942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:31.973893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:07.932340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:11.840668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:14.758137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:17.971787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:20.719125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:23.421854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:25.931685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:49:28.787642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T04:49:55.225854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드업종명업태명허가신고일교부번호영업장면적(㎡)지상_부터지상_까지지하_부터지하_까지총층수업소위치
시군구코드1.0000.1330.2570.0000.0000.0000.0240.0000.1930.1920.5090.220
업종명0.1331.0001.0000.1090.1100.6490.5070.6440.0000.0000.1110.231
업태명0.2571.0001.0000.1330.1180.7980.6750.7810.1850.1820.0000.271
허가신고일0.0000.1090.1331.0000.9820.1110.0210.0190.0000.0000.0000.046
교부번호0.0000.1100.1180.9821.0000.0850.0340.0310.0170.0180.0000.054
영업장면적(㎡)0.0000.6490.7980.1110.0851.0000.3500.338NaNNaN0.0000.227
지상_부터0.0240.5070.6750.0210.0340.3501.0001.000NaNNaN0.1190.000
지상_까지0.0000.6440.7810.0190.0310.3381.0001.000NaNNaN0.1190.000
지하_부터0.1930.0000.1850.0000.017NaNNaNNaN1.0000.705NaN0.000
지하_까지0.1920.0000.1820.0000.018NaNNaNNaN0.7051.000NaN0.000
총층수0.5090.1110.0000.0000.0000.0000.1190.119NaNNaN1.0000.077
업소위치0.2200.2310.2710.0460.0540.2270.0000.0000.0000.0000.0771.000
2024-05-11T04:49:55.622492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업소위치
업종명1.0000.101
업소위치0.1011.000
2024-05-11T04:49:55.889222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드허가신고일교부번호영업장면적(㎡)지상_부터지상_까지지하_부터지하_까지총층수업종명업소위치
시군구코드1.0000.0170.5370.045-0.023-0.0440.0820.0870.1380.0440.089
허가신고일0.0171.0000.6890.029-0.001-0.0140.0010.0070.0620.0510.038
교부번호0.5370.6891.0000.052-0.010-0.0310.0090.0140.0860.0520.044
영업장면적(㎡)0.0450.0290.0521.0000.1010.140-0.092-0.092-0.1790.3410.146
지상_부터-0.023-0.001-0.0100.1011.0000.9400.0830.0830.2100.2970.000
지상_까지-0.044-0.014-0.0310.1400.9401.0000.1690.1690.1840.4150.000
지하_부터0.0820.0010.009-0.0920.0830.1691.0000.9940.6400.0000.000
지하_까지0.0870.0070.014-0.0920.0830.1690.9941.0000.6440.0000.000
총층수0.1380.0620.086-0.1790.2100.1840.6400.6441.0000.0540.050
업종명0.0440.0510.0520.3410.2970.4150.0000.0000.0541.0000.101
업소위치0.0890.0380.0440.1460.0000.0000.0000.0000.0500.1011.000

Missing values

2024-05-11T04:49:32.577269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T04:49:33.613599image/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.
2024-05-11T04:49:34.152405image/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

시군구코드업종명업태명허가신고일폐업일자교부번호업소명소재지도로명소재지지번행정동명법정동명영업장면적(㎡)지상_부터지상_까지지하_부터지하_까지총층수업소위치
17733130000즉석판매제조가공업즉석판매제조가공업20240425<NA>20240097789아미까(Amigga)서울특별시 마포구 숭문길 98, 1층 109호 (염리동, 마포자이 더 센트리지)서울특별시 마포구 염리동 533번지 마포자이 더 센트리지염리동염리동<NA><NA><NA><NA><NA><NA>지상
30413120000건강기능식품일반판매업전자상거래(통신판매업)20240415<NA>20240093324황금쇼핑gs서울특별시 서대문구 증가로 150, 101동 902호 (남가좌동, DMC센트럴아이파크)서울특별시 서대문구 남가좌동 388번지 DMC센트럴아이파크남가좌제2동남가좌동<NA><NA><NA><NA><NA><NA><NA>
81803170000건강기능식품일반판매업전자상거래(통신판매업)20240214<NA>20240118124더한커머스서울특별시 금천구 가산디지털1로 119, SK트윈테크타워 B동 412(일부)호 (가산동)서울특별시 금천구 가산동 345번지 9호 SK트윈테크타워 B동 412(일부)호가산동가산동<NA><NA><NA><NA><NA><NA>지상
31153130000휴게음식점기타 휴게음식점20240412<NA>20240097700사계 김밥서울특별시 마포구 양화로7안길 38, 1층 (서교동)서울특별시 마포구 서교동 380번지 17호서교동서교동<NA><NA><NA><NA><NA><NA>지상
145603180000위탁급식영업위탁급식영업20231120<NA>20230116323JF 한국우편사업진흥원점서울특별시 영등포구 영중로 83, 직원식당 8층 (영등포동7가)서울특별시 영등포구 영등포동7가 47번지 2호영등포동영등포동7가<NA><NA><NA><NA><NA><NA>지상
175573220000건강기능식품일반판매업전자상거래(통신판매업)20231013<NA>20230140789이지기획서울특별시 강남구 강남대로114길 19, 2층 (논현동)서울특별시 강남구 논현동 184번지 19호논현1동논현동<NA><NA><NA><NA><NA><NA>지상
233663160000휴게음식점커피숍20230726<NA>20230106953시스터즈 쿠키(sisters cookie)서울특별시 구로구 서해안로34가길 19, 101호 (개봉동)서울특별시 구로구 개봉동 309번지 32호개봉제2동개봉동<NA>11<NA><NA><NA><NA>
302223130000일반음식점기타20230503<NA>20230092985환대(hwandae)서울특별시 마포구 연남로1길 70, 신원빌딩 1층 (연남동)서울특별시 마포구 연남동 564번지 20호 신원빌딩연남동연남동<NA><NA><NA><NA><NA><NA>지상
143533020000휴게음식점기타 휴게음식점20231122<NA>20230046193소복소복서울특별시 용산구 한강대로23길 55, 이마트 지하2층 (한강로3가)서울특별시 용산구 한강로3가 40번지 999호 용산역한강로동한강로3가<NA><NA><NA>2229<NA>
58783220000유통전문판매업유통전문판매업20240312<NA>20240144921팩브라더서울특별시 강남구 학동로55길 12-11, 지하2층 (청담동)서울특별시 강남구 청담동 39번지 9호청담동청담동<NA><NA><NA><NA><NA><NA>지하
시군구코드업종명업태명허가신고일폐업일자교부번호업소명소재지도로명소재지지번행정동명법정동명영업장면적(㎡)지상_부터지상_까지지하_부터지하_까지총층수업소위치
263083150000건강기능식품일반판매업전자상거래(통신판매업)20230620<NA>20230102126비타닥터서울특별시 강서구 공항대로43길 39, 1103동 202호 (등촌동, 등촌주공아파트11단지)서울특별시 강서구 등촌동 705번지 6호 1103 등촌주공아파트11단지-202등촌제3동등촌동<NA><NA><NA><NA><NA><NA>지상
149983020000일반음식점일식20231114<NA>20230046168갓텐스시 서울역점서울특별시 용산구 청파로 378, 3층 211호 (동자동)서울특별시 용산구 동자동 43번지 205호 한국철도공사남영동동자동<NA>33<NA><NA><NA><NA>
244223070000휴게음식점일반조리판매20230713<NA>20230067739우리함께라면서울특별시 성북구 돌곶이로22길 12, 1층 (석관동)서울특별시 성북구 석관동 202번지 20호석관동석관동<NA><NA><NA><NA><NA><NA>지상
128873100000일반음식점호프/통닭20231211<NA>20230080249부어치킨 노원점서울특별시 노원구 동일로 1493, 상계주공아파트(10단지) 106호 (상계동)서울특별시 노원구 상계동 666번지 3호 상계주공아파트(10단지)상계8동상계동<NA><NA><NA><NA><NA><NA><NA>
48183180000일반음식점한식20240325<NA>20240121524더본 테이스티서울특별시 영등포구 여의대로 128, 지하1층 (여의도동)서울특별시 영등포구 여의도동 20번지여의동여의도동<NA><NA><NA><NA><NA><NA>지하
336693180000일반음식점중국식20230324<NA>20230114539신국밥서울특별시 영등포구 디지털로 382-1, 1층 (대림동)서울특별시 영등포구 대림동 1032번지 5호신길제5동대림동<NA>11<NA><NA><NA>지상
186793230000일반음식점기타20230922<NA>20230149174Bunny Butt(버니버트)서울특별시 송파구 오금로15길 7-20, 1층 (방이동)서울특별시 송파구 방이동 68번지 9호방이2동방이동<NA><NA><NA><NA><NA><NA>지상
313493060000즉석판매제조가공업즉석판매제조가공업20230419<NA>20230062346지니양과점서울특별시 중랑구 면목로35길 22, 1층 (면목동)서울특별시 중랑구 면목동 661번지 14호면목제7동면목동<NA><NA><NA><NA><NA><NA>지상
264393200000일반음식점한식20230619<NA>20230124749빙수바보, 빙식이서울특별시 관악구 은천로 143, 관악동부센트레빌아파트 상가동 지1층 비101호 (봉천동)서울특별시 관악구 봉천동 1719번지 1호 관악동부센트레빌아파트성현동봉천동<NA><NA><NA>11<NA><NA>
317273070000일반음식점기타20230414<NA>20230067379루나서울특별시 성북구 보문로34길 68-5, 지하1층 (동선동2가)서울특별시 성북구 동선동2가 135번지 19호동선동동선동2가<NA><NA><NA>11<NA>지하