Overview

Dataset statistics

Number of variables17
Number of observations1979
Missing cells2394
Missing cells (%)7.1%
Duplicate rows200
Duplicate rows (%)10.1%
Total size in memory274.6 KiB
Average record size in memory142.1 B

Variable types

Categorical4
Numeric5
Text8

Dataset

Description시군구코드,처분일자,교부번호,업종명,업태명,업소명,소재지도로명,소재지지번,지도점검일자,행정처분상태,처분명,법적근거,위반일자,위반내용,처분내용,처분기간,영업장면적(㎡)
Author영등포구
URLhttps://data.seoul.go.kr/dataList/OA-10451/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
행정처분상태 has constant value ""Constant
Dataset has 200 (10.1%) duplicate rowsDuplicates
처분일자 is highly overall correlated with 지도점검일자 and 2 other fieldsHigh correlation
지도점검일자 is highly overall correlated with 처분일자 and 2 other fieldsHigh correlation
위반일자 is highly overall correlated with 처분일자 and 2 other fieldsHigh correlation
처분기간 is highly overall correlated with 처분일자 and 2 other fieldsHigh correlation
업종명 is highly overall correlated with 업태명High correlation
업태명 is highly overall correlated with 업종명High correlation
교부번호 has 139 (7.0%) missing valuesMissing
소재지도로명 has 752 (38.0%) missing valuesMissing
처분기간 has 1270 (64.2%) missing valuesMissing
영업장면적(㎡) has 223 (11.3%) missing valuesMissing
처분기간 has 572 (28.9%) zerosZeros
영업장면적(㎡) has 35 (1.8%) zerosZeros

Reproduction

Analysis started2024-05-10 23:10:11.248041
Analysis finished2024-05-10 23:10:24.913508
Duration13.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
3180000
1979 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3180000
2nd row3180000
3rd row3180000
4th row3180000
5th row3180000

Common Values

ValueCountFrequency (%)
3180000 1979
100.0%

Length

2024-05-10T23:10:25.100404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:10:25.429964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3180000 1979
100.0%

처분일자
Real number (ℝ)

HIGH CORRELATION 

Distinct435
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20078967
Minimum19921017
Maximum20240213
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.5 KiB
2024-05-10T23:10:25.942233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19921017
5-th percentile19961007
Q119990616
median20091223
Q320140317
95-th percentile20200807
Maximum20240213
Range319196
Interquartile range (IQR)149701

Descriptive statistics

Standard deviation76827.677
Coefficient of variation (CV)0.0038262764
Kurtosis-1.2168437
Mean20078967
Median Absolute Deviation (MAD)59785
Skewness-0.028418344
Sum3.9736276 × 1010
Variance5.9024919 × 109
MonotonicityDecreasing
2024-05-10T23:10:26.379439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19990616 131
 
6.6%
19990611 130
 
6.6%
19990519 44
 
2.2%
20091224 40
 
2.0%
19980821 40
 
2.0%
20130220 35
 
1.8%
20050325 32
 
1.6%
20050616 30
 
1.5%
19960913 29
 
1.5%
20090817 24
 
1.2%
Other values (425) 1444
73.0%
ValueCountFrequency (%)
19921017 2
 
0.1%
19940119 4
0.2%
19940716 8
0.4%
19940719 1
 
0.1%
19941024 2
 
0.1%
19941109 2
 
0.1%
19941221 2
 
0.1%
19941222 3
 
0.2%
19950117 1
 
0.1%
19950208 1
 
0.1%
ValueCountFrequency (%)
20240213 2
0.1%
20240205 2
0.1%
20231227 2
0.1%
20230629 1
 
0.1%
20230619 1
 
0.1%
20230612 1
 
0.1%
20230607 1
 
0.1%
20230405 3
0.2%
20230328 1
 
0.1%
20230221 1
 
0.1%

교부번호
Text

MISSING 

Distinct1000
Distinct (%)54.3%
Missing139
Missing (%)7.0%
Memory size15.6 KiB
2024-05-10T23:10:27.188316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length7.4701087
Min length1

Characters and Unicode

Total characters13745
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique689 ?
Unique (%)37.4%

Sample

1st row276
2nd row276
3rd row276
4th row276
5th row362
ValueCountFrequency (%)
88 23
 
1.2%
326 16
 
0.9%
276 16
 
0.9%
9 14
 
0.8%
89 14
 
0.8%
104 13
 
0.7%
67 11
 
0.6%
81 10
 
0.5%
228 9
 
0.5%
115 9
 
0.5%
Other values (990) 1705
92.7%
2024-05-10T23:10:28.426424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5063
36.8%
1 1932
 
14.1%
4 1322
 
9.6%
5 1204
 
8.8%
6 1077
 
7.8%
2 956
 
7.0%
3 692
 
5.0%
8 410
 
3.0%
7 404
 
2.9%
9 399
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13459
97.9%
Dash Punctuation 286
 
2.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5063
37.6%
1 1932
 
14.4%
4 1322
 
9.8%
5 1204
 
8.9%
6 1077
 
8.0%
2 956
 
7.1%
3 692
 
5.1%
8 410
 
3.0%
7 404
 
3.0%
9 399
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13745
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5063
36.8%
1 1932
 
14.1%
4 1322
 
9.6%
5 1204
 
8.8%
6 1077
 
7.8%
2 956
 
7.0%
3 692
 
5.0%
8 410
 
3.0%
7 404
 
2.9%
9 399
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13745
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5063
36.8%
1 1932
 
14.1%
4 1322
 
9.6%
5 1204
 
8.8%
6 1077
 
7.8%
2 956
 
7.0%
3 692
 
5.0%
8 410
 
3.0%
7 404
 
2.9%
9 399
 
2.9%

업종명
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
위생관리용역업
365 
숙박업(일반)
350 
이용업
335 
목욕장업
217 
세탁업
207 
Other values (14)
505 

Length

Max length23
Median length16
Mean length4.9924204
Min length3

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st row종합미용업
2nd row종합미용업
3rd row종합미용업
4th row종합미용업
5th row일반미용업

Common Values

ValueCountFrequency (%)
위생관리용역업 365
18.4%
숙박업(일반) 350
17.7%
이용업 335
16.9%
목욕장업 217
11.0%
세탁업 207
10.5%
미용업 204
10.3%
일반미용업 137
 
6.9%
피부미용업 83
 
4.2%
종합미용업 41
 
2.1%
네일미용업 11
 
0.6%
Other values (9) 29
 
1.5%

Length

2024-05-10T23:10:28.935084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
위생관리용역업 365
18.0%
숙박업(일반 350
17.3%
이용업 335
16.6%
미용업 220
10.9%
목욕장업 217
10.7%
세탁업 207
10.2%
일반미용업 141
 
7.0%
피부미용업 98
 
4.8%
종합미용업 41
 
2.0%
네일미용업 31
 
1.5%
Other values (2) 18
 
0.9%

업태명
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
일반미용업
369 
일반이용업
335 
위생관리용역업
323 
여관업
257 
일반세탁업
202 
Other values (15)
493 

Length

Max length14
Median length5
Mean length5.3178373
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 369
18.6%
일반이용업 335
16.9%
위생관리용역업 323
16.3%
여관업 257
13.0%
일반세탁업 202
10.2%
공동탕업 134
 
6.8%
피부미용업 89
 
4.5%
공동탕업+찜질시설서비스영업 58
 
2.9%
여인숙업 58
 
2.9%
위생관리용역업 기타 42
 
2.1%
Other values (10) 112
 
5.7%

Length

2024-05-10T23:10:29.386489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 369
18.2%
위생관리용역업 365
18.0%
일반이용업 335
16.5%
여관업 257
12.7%
일반세탁업 202
10.0%
공동탕업 134
 
6.6%
피부미용업 89
 
4.4%
공동탕업+찜질시설서비스영업 58
 
2.9%
여인숙업 58
 
2.9%
기타 52
 
2.6%
Other values (9) 111
 
5.5%

업소명
Text

Distinct1073
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
2024-05-10T23:10:30.167627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length5.3046993
Min length1

Characters and Unicode

Total characters10498
Distinct characters525
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique719 ?
Unique (%)36.3%

Sample

1st row조복순 헤어 아뜨리에
2nd row조복순 헤어 아뜨리에
3rd row조복순 헤어 아뜨리에
4th row조복순 헤어 아뜨리에
5th row와이헤어모드
ValueCountFrequency (%)
대신 31
 
1.4%
현대이용원 30
 
1.3%
주식회사 20
 
0.9%
호텔 16
 
0.7%
로타리사우나 16
 
0.7%
우리불한증사우나 12
 
0.5%
헤어 12
 
0.5%
아뜨리에 10
 
0.4%
여의도불한증사우나 10
 
0.4%
주)제이엘티엠 10
 
0.4%
Other values (1162) 2080
92.6%
2024-05-10T23:10:31.367973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 318
 
3.0%
) 294
 
2.8%
( 294
 
2.8%
사 287
 
2.7%
이 284
 
2.7%
271
 
2.6%
스 199
 
1.9%
우 186
 
1.8%
대 175
 
1.7%
리 168
 
1.6%
Other values (515) 8022
76.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9266
88.3%
Close Punctuation 294
 
2.8%
Open Punctuation 294
 
2.8%
Space Separator 271
 
2.6%
Uppercase Letter 131
 
1.2%
Lowercase Letter 115
 
1.1%
Decimal Number 104
 
1.0%
Other Punctuation 21
 
0.2%
Dash Punctuation 1
 
< 0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 318
 
3.4%
사 287
 
3.1%
이 284
 
3.1%
스 199
 
2.1%
우 186
 
2.0%
대 175
 
1.9%
리 168
 
1.8%
용 165
 
1.8%
여 159
 
1.7%
나 151
 
1.6%
Other values (452) 7174
77.4%
Uppercase Letter
ValueCountFrequency (%)
L 15
 
11.5%
A 10
 
7.6%
H 9
 
6.9%
E 9
 
6.9%
M 8
 
6.1%
R 8
 
6.1%
D 7
 
5.3%
N 7
 
5.3%
J 6
 
4.6%
T 6
 
4.6%
Other values (14) 46
35.1%
Lowercase Letter
ValueCountFrequency (%)
a 24
20.9%
e 16
13.9%
l 10
8.7%
z 8
 
7.0%
r 7
 
6.1%
s 6
 
5.2%
t 6
 
5.2%
o 6
 
5.2%
n 6
 
5.2%
y 5
 
4.3%
Other values (8) 21
18.3%
Decimal Number
ValueCountFrequency (%)
1 26
25.0%
2 23
22.1%
4 20
19.2%
9 8
 
7.7%
0 7
 
6.7%
6 7
 
6.7%
3 7
 
6.7%
7 4
 
3.8%
5 1
 
1.0%
8 1
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 12
57.1%
& 3
 
14.3%
' 2
 
9.5%
？ 2
 
9.5%
# 1
 
4.8%
＃ 1
 
4.8%
Close Punctuation
ValueCountFrequency (%)
) 294
100.0%
Open Punctuation
ValueCountFrequency (%)
( 294
100.0%
Space Separator
ValueCountFrequency (%)
271
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9264
88.2%
Common 985
 
9.4%
Latin 247
 
2.4%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 318
 
3.4%
사 287
 
3.1%
이 284
 
3.1%
스 199
 
2.1%
우 186
 
2.0%
대 175
 
1.9%
리 168
 
1.8%
용 165
 
1.8%
여 159
 
1.7%
나 151
 
1.6%
Other values (450) 7172
77.4%
Latin
ValueCountFrequency (%)
a 24
 
9.7%
e 16
 
6.5%
L 15
 
6.1%
l 10
 
4.0%
A 10
 
4.0%
H 9
 
3.6%
E 9
 
3.6%
M 8
 
3.2%
z 8
 
3.2%
R 8
 
3.2%
Other values (33) 130
52.6%
Common
ValueCountFrequency (%)
) 294
29.8%
( 294
29.8%
271
27.5%
1 26
 
2.6%
2 23
 
2.3%
4 20
 
2.0%
. 12
 
1.2%
9 8
 
0.8%
0 7
 
0.7%
6 7
 
0.7%
Other values (10) 23
 
2.3%
Han
ValueCountFrequency (%)
鄭 1
50.0%
美 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9263
88.2%
ASCII 1228
 
11.7%
None 3
 
< 0.1%
CJK 2
 
< 0.1%
Compat Jamo 1
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 318
 
3.4%
사 287
 
3.1%
이 284
 
3.1%
스 199
 
2.1%
우 186
 
2.0%
대 175
 
1.9%
리 168
 
1.8%
용 165
 
1.8%
여 159
 
1.7%
나 151
 
1.6%
Other values (449) 7171
77.4%
ASCII
ValueCountFrequency (%)
) 294
23.9%
( 294
23.9%
271
22.1%
1 26
 
2.1%
a 24
 
2.0%
2 23
 
1.9%
4 20
 
1.6%
e 16
 
1.3%
L 15
 
1.2%
. 12
 
1.0%
Other values (50) 233
19.0%
None
ValueCountFrequency (%)
？ 2
66.7%
＃ 1
33.3%
Compat Jamo
ValueCountFrequency (%)
ㅍ 1
100.0%
CJK
ValueCountFrequency (%)
鄭 1
50.0%
美 1
50.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

소재지도로명
Text

MISSING 

Distinct582
Distinct (%)47.4%
Missing752
Missing (%)38.0%
Memory size15.6 KiB
2024-05-10T23:10:32.388581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length48
Mean length33.193154
Min length23

Characters and Unicode

Total characters40728
Distinct characters258
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique349 ?
Unique (%)28.4%

Sample

1st row서울특별시 영등포구 디지털로57길 24, (대림동,(지하1층 ~ 지상1층))
2nd row서울특별시 영등포구 디지털로57길 24, (대림동,(지하1층 ~ 지상1층))
3rd row서울특별시 영등포구 디지털로57길 24, (대림동,(지하1층 ~ 지상1층))
4th row서울특별시 영등포구 디지털로57길 24, (대림동,(지하1층 ~ 지상1층))
5th row서울특별시 영등포구 대방천로 188, (신길동,1층)
ValueCountFrequency (%)
서울특별시 1227
 
17.8%
영등포구 1227
 
17.8%
신길동 196
 
2.8%
영등포로 109
 
1.6%
대림동 99
 
1.4%
여의도동 87
 
1.3%
영등포동3가 67
 
1.0%
영등포동5가 66
 
1.0%
2 60
 
0.9%
2층 49
 
0.7%
Other values (878) 3700
53.7%
2024-05-10T23:10:34.379750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5662
 
13.9%
영 1909
 
4.7%
, 1794
 
4.4%
포 1745
 
4.3%
등 1734
 
4.3%
동 1326
 
3.3%
) 1267
 
3.1%
( 1267
 
3.1%
1 1258
 
3.1%
시 1254
 
3.1%
Other values (248) 21512
52.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24270
59.6%
Decimal Number 6141
 
15.1%
Space Separator 5662
 
13.9%
Other Punctuation 1799
 
4.4%
Close Punctuation 1267
 
3.1%
Open Punctuation 1267
 
3.1%
Dash Punctuation 239
 
0.6%
Uppercase Letter 70
 
0.2%
Math Symbol 12
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 1909
 
7.9%
포 1745
 
7.2%
등 1734
 
7.1%
동 1326
 
5.5%
시 1254
 
5.2%
서 1247
 
5.1%
로 1236
 
5.1%
별 1230
 
5.1%
구 1229
 
5.1%
울 1228
 
5.1%
Other values (217) 10132
41.7%
Uppercase Letter
ValueCountFrequency (%)
B 28
40.0%
A 11
 
15.7%
L 6
 
8.6%
G 6
 
8.6%
T 5
 
7.1%
P 5
 
7.1%
K 4
 
5.7%
R 1
 
1.4%
O 1
 
1.4%
H 1
 
1.4%
Other values (2) 2
 
2.9%
Decimal Number
ValueCountFrequency (%)
1 1258
20.5%
2 998
16.3%
3 721
11.7%
0 541
8.8%
5 540
8.8%
4 535
8.7%
6 434
 
7.1%
7 432
 
7.0%
8 391
 
6.4%
9 291
 
4.7%
Other Punctuation
ValueCountFrequency (%)
, 1794
99.7%
. 3
 
0.2%
/ 2
 
0.1%
Space Separator
ValueCountFrequency (%)
5662
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1267
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1267
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 239
100.0%
Math Symbol
ValueCountFrequency (%)
~ 12
100.0%
Lowercase Letter
ValueCountFrequency (%)
n 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24270
59.6%
Common 16387
40.2%
Latin 71
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 1909
 
7.9%
포 1745
 
7.2%
등 1734
 
7.1%
동 1326
 
5.5%
시 1254
 
5.2%
서 1247
 
5.1%
로 1236
 
5.1%
별 1230
 
5.1%
구 1229
 
5.1%
울 1228
 
5.1%
Other values (217) 10132
41.7%
Common
ValueCountFrequency (%)
5662
34.6%
, 1794
 
10.9%
) 1267
 
7.7%
( 1267
 
7.7%
1 1258
 
7.7%
2 998
 
6.1%
3 721
 
4.4%
0 541
 
3.3%
5 540
 
3.3%
4 535
 
3.3%
Other values (8) 1804
 
11.0%
Latin
ValueCountFrequency (%)
B 28
39.4%
A 11
 
15.5%
L 6
 
8.5%
G 6
 
8.5%
T 5
 
7.0%
P 5
 
7.0%
K 4
 
5.6%
n 1
 
1.4%
R 1
 
1.4%
O 1
 
1.4%
Other values (3) 3
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24270
59.6%
ASCII 16458
40.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5662
34.4%
, 1794
 
10.9%
) 1267
 
7.7%
( 1267
 
7.7%
1 1258
 
7.6%
2 998
 
6.1%
3 721
 
4.4%
0 541
 
3.3%
5 540
 
3.3%
4 535
 
3.3%
Other values (21) 1875
 
11.4%
Hangul
ValueCountFrequency (%)
영 1909
 
7.9%
포 1745
 
7.2%
등 1734
 
7.1%
동 1326
 
5.5%
시 1254
 
5.2%
서 1247
 
5.1%
로 1236
 
5.1%
별 1230
 
5.1%
구 1229
 
5.1%
울 1228
 
5.1%
Other values (217) 10132
41.7%

소재지지번
Text

Distinct1142
Distinct (%)58.0%
Missing10
Missing (%)0.5%
Memory size15.6 KiB
2024-05-10T23:10:35.457433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length46
Mean length30.486541
Min length23

Characters and Unicode

Total characters60028
Distinct characters268
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique812 ?
Unique (%)41.2%

Sample

1st row서울특별시 영등포구 대림동 832번지 32호 (지하1층 ~ 지상1층)
2nd row서울특별시 영등포구 대림동 832번지 32호 (지하1층 ~ 지상1층)
3rd row서울특별시 영등포구 대림동 832번지 32호 (지하1층 ~ 지상1층)
4th row서울특별시 영등포구 대림동 832번지 32호 (지하1층 ~ 지상1층)
5th row서울특별시 영등포구 신길동 4528번지 1층
ValueCountFrequency (%)
서울특별시 1969
 
17.4%
영등포구 1969
 
17.4%
산 616
 
5.4%
신길동 479
 
4.2%
여의도동 322
 
2.8%
대림동 233
 
2.1%
1호 215
 
1.9%
2호 163
 
1.4%
0호 132
 
1.2%
3호 128
 
1.1%
Other values (1015) 5088
45.0%
2024-05-10T23:10:36.745339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14511
24.2%
영 2454
 
4.1%
포 2447
 
4.1%
등 2437
 
4.1%
지 2147
 
3.6%
동 2015
 
3.4%
1 2012
 
3.4%
시 1985
 
3.3%
서 1974
 
3.3%
구 1972
 
3.3%
Other values (258) 26074
43.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 35036
58.4%
Space Separator 14511
24.2%
Decimal Number 10077
 
16.8%
Dash Punctuation 183
 
0.3%
Uppercase Letter 74
 
0.1%
Other Punctuation 54
 
0.1%
Close Punctuation 39
 
0.1%
Open Punctuation 39
 
0.1%
Math Symbol 12
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 2454
 
7.0%
포 2447
 
7.0%
등 2437
 
7.0%
지 2147
 
6.1%
동 2015
 
5.8%
시 1985
 
5.7%
서 1974
 
5.6%
구 1972
 
5.6%
별 1972
 
5.6%
울 1971
 
5.6%
Other values (224) 13662
39.0%
Uppercase Letter
ValueCountFrequency (%)
B 29
39.2%
A 13
17.6%
G 6
 
8.1%
L 6
 
8.1%
T 5
 
6.8%
P 5
 
6.8%
K 4
 
5.4%
H 1
 
1.4%
E 1
 
1.4%
R 1
 
1.4%
Other values (3) 3
 
4.1%
Decimal Number
ValueCountFrequency (%)
1 2012
20.0%
2 1411
14.0%
3 1405
13.9%
4 1095
10.9%
0 963
9.6%
5 906
9.0%
6 751
 
7.5%
7 559
 
5.5%
9 550
 
5.5%
8 425
 
4.2%
Other Punctuation
ValueCountFrequency (%)
, 47
87.0%
. 5
 
9.3%
/ 2
 
3.7%
Lowercase Letter
ValueCountFrequency (%)
g 1
50.0%
n 1
50.0%
Space Separator
ValueCountFrequency (%)
14511
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 183
100.0%
Close Punctuation
ValueCountFrequency (%)
) 39
100.0%
Open Punctuation
ValueCountFrequency (%)
( 39
100.0%
Math Symbol
ValueCountFrequency (%)
~ 12
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 35036
58.4%
Common 24916
41.5%
Latin 76
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 2454
 
7.0%
포 2447
 
7.0%
등 2437
 
7.0%
지 2147
 
6.1%
동 2015
 
5.8%
시 1985
 
5.7%
서 1974
 
5.6%
구 1972
 
5.6%
별 1972
 
5.6%
울 1971
 
5.6%
Other values (224) 13662
39.0%
Common
ValueCountFrequency (%)
14511
58.2%
1 2012
 
8.1%
2 1411
 
5.7%
3 1405
 
5.6%
4 1095
 
4.4%
0 963
 
3.9%
5 906
 
3.6%
6 751
 
3.0%
7 559
 
2.2%
9 550
 
2.2%
Other values (9) 753
 
3.0%
Latin
ValueCountFrequency (%)
B 29
38.2%
A 13
17.1%
G 6
 
7.9%
L 6
 
7.9%
T 5
 
6.6%
P 5
 
6.6%
K 4
 
5.3%
H 1
 
1.3%
E 1
 
1.3%
R 1
 
1.3%
Other values (5) 5
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 35036
58.4%
ASCII 24992
41.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14511
58.1%
1 2012
 
8.1%
2 1411
 
5.6%
3 1405
 
5.6%
4 1095
 
4.4%
0 963
 
3.9%
5 906
 
3.6%
6 751
 
3.0%
7 559
 
2.2%
9 550
 
2.2%
Other values (24) 829
 
3.3%
Hangul
ValueCountFrequency (%)
영 2454
 
7.0%
포 2447
 
7.0%
등 2437
 
7.0%
지 2147
 
6.1%
동 2015
 
5.8%
시 1985
 
5.7%
서 1974
 
5.6%
구 1972
 
5.6%
별 1972
 
5.6%
울 1971
 
5.6%
Other values (224) 13662
39.0%

지도점검일자
Real number (ℝ)

HIGH CORRELATION 

Distinct501
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20078042
Minimum19921017
Maximum20240110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.5 KiB
2024-05-10T23:10:37.151364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19921017
5-th percentile19961007
Q119990616
median20090525
Q320131104
95-th percentile20200624
Maximum20240110
Range319093
Interquartile range (IQR)140488

Descriptive statistics

Standard deviation76345.918
Coefficient of variation (CV)0.0038024583
Kurtosis-1.2089388
Mean20078042
Median Absolute Deviation (MAD)60083
Skewness-0.014027557
Sum3.9734446 × 1010
Variance5.8286993 × 109
MonotonicityNot monotonic
2024-05-10T23:10:37.447843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19990616 131
 
6.6%
19990611 130
 
6.6%
20130109 66
 
3.3%
19990519 44
 
2.2%
19980821 40
 
2.0%
20170102 37
 
1.9%
20180220 34
 
1.7%
19960913 29
 
1.5%
20050616 28
 
1.4%
20050325 28
 
1.4%
Other values (491) 1412
71.3%
ValueCountFrequency (%)
19921017 2
 
0.1%
19940119 4
0.2%
19940716 8
0.4%
19940719 1
 
0.1%
19941024 2
 
0.1%
19941109 2
 
0.1%
19941221 2
 
0.1%
19941222 3
 
0.2%
19950117 1
 
0.1%
19950208 1
 
0.1%
ValueCountFrequency (%)
20240110 4
0.2%
20231227 1
 
0.1%
20231205 1
 
0.1%
20230524 1
 
0.1%
20230519 1
 
0.1%
20230509 1
 
0.1%
20230310 3
0.2%
20230227 1
 
0.1%
20230109 1
 
0.1%
20221222 1
 
0.1%

행정처분상태
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
처분확정
1979 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
처분확정 1979
100.0%

Length

2024-05-10T23:10:37.733991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:10:37.942164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
처분확정 1979
100.0%

처분명
Text

Distinct320
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
2024-05-10T23:10:38.261308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length39
Mean length8.0207175
Min length2

Characters and Unicode

Total characters15873
Distinct characters129
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique176 ?
Unique (%)8.9%

Sample

1st row영업정지 60일 갈음, 과징금 564,000원 부과
2nd row영업정지 60일 갈음, 과징금 564,000원 부과
3rd row과태료부과 64만원(20%감경)
4th row과태료부과 64만원(20%감경)
5th row직권말소
ValueCountFrequency (%)
경고 607
22.8%
개선명령 212
 
8.0%
영업소폐쇄 174
 
6.5%
과태료부과 127
 
4.8%
과태료 93
 
3.5%
부과 93
 
3.5%
영업정지 82
 
3.1%
영업허가취소 54
 
2.0%
및 49
 
1.8%
16만원 48
 
1.8%
Other values (315) 1122
42.2%
2024-05-10T23:10:39.080552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 1234
 
7.8%
( 957
 
6.0%
) 956
 
6.0%
경 735
 
4.6%
고 716
 
4.5%
0 705
 
4.4%
683
 
4.3%
부 601
 
3.8%
료 584
 
3.7%
업 584
 
3.7%
Other values (119) 8118
51.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10745
67.7%
Decimal Number 2220
 
14.0%
Open Punctuation 957
 
6.0%
Close Punctuation 956
 
6.0%
Space Separator 683
 
4.3%
Other Punctuation 274
 
1.7%
Dash Punctuation 21
 
0.1%
Math Symbol 17
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 1234
 
11.5%
경 735
 
6.8%
고 716
 
6.7%
부 601
 
5.6%
료 584
 
5.4%
업 584
 
5.4%
영 563
 
5.2%
태 553
 
5.1%
원 549
 
5.1%
만 483
 
4.5%
Other values (99) 4143
38.6%
Decimal Number
ValueCountFrequency (%)
0 705
31.8%
2 431
19.4%
1 362
16.3%
6 197
 
8.9%
5 128
 
5.8%
4 118
 
5.3%
3 114
 
5.1%
8 93
 
4.2%
7 41
 
1.8%
9 31
 
1.4%
Other Punctuation
ValueCountFrequency (%)
, 114
41.6%
. 80
29.2%
% 48
17.5%
/ 29
 
10.6%
: 3
 
1.1%
Open Punctuation
ValueCountFrequency (%)
( 957
100.0%
Close Punctuation
ValueCountFrequency (%)
) 956
100.0%
Space Separator
ValueCountFrequency (%)
683
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Math Symbol
ValueCountFrequency (%)
~ 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10745
67.7%
Common 5128
32.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 1234
 
11.5%
경 735
 
6.8%
고 716
 
6.7%
부 601
 
5.6%
료 584
 
5.4%
업 584
 
5.4%
영 563
 
5.2%
태 553
 
5.1%
원 549
 
5.1%
만 483
 
4.5%
Other values (99) 4143
38.6%
Common
ValueCountFrequency (%)
( 957
18.7%
) 956
18.6%
0 705
13.7%
683
13.3%
2 431
8.4%
1 362
 
7.1%
6 197
 
3.8%
5 128
 
2.5%
4 118
 
2.3%
, 114
 
2.2%
Other values (10) 477
9.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10744
67.7%
ASCII 5128
32.3%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 1234
 
11.5%
경 735
 
6.8%
고 716
 
6.7%
부 601
 
5.6%
료 584
 
5.4%
업 584
 
5.4%
영 563
 
5.2%
태 553
 
5.1%
원 549
 
5.1%
만 483
 
4.5%
Other values (98) 4142
38.6%
ASCII
ValueCountFrequency (%)
( 957
18.7%
) 956
18.6%
0 705
13.7%
683
13.3%
2 431
8.4%
1 362
 
7.1%
6 197
 
3.8%
5 128
 
2.5%
4 118
 
2.3%
, 114
 
2.2%
Other values (10) 477
9.3%
Compat Jamo
ValueCountFrequency (%)
ㄹ 1
100.0%

법적근거
Text

Distinct178
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
2024-05-10T23:10:39.627394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length53
Mean length11.449722
Min length5

Characters and Unicode

Total characters22659
Distinct characters67
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

Unique77 ?
Unique (%)3.9%

Sample

1st row법 제11조제1항제4호
2nd row법 제11조제1항제4호
3rd row법 제11조제1항제4호
4th row법 제11조제1항제4호
5th row법 제11조제3항제2호
ValueCountFrequency (%)
공중위생관리법 713
18.0%
공중위생법 641
16.2%
법 414
 
10.5%
제17조 297
 
7.5%
및 198
 
5.0%
제19조 156
 
3.9%
제11조 150
 
3.8%
동법시행규칙 112
 
2.8%
제17조제1항 87
 
2.2%
제3조제2항 75
 
1.9%
Other values (146) 1113
28.1%
2024-05-10T23:10:40.644010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 2538
11.2%
법 2158
 
9.5%
1982
 
8.7%
1 1872
 
8.3%
조 1646
 
7.3%
위 1552
 
6.8%
공 1521
 
6.7%
생 1521
 
6.7%
중 1521
 
6.7%
관 880
 
3.9%
Other values (57) 5468
24.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16671
73.6%
Decimal Number 3852
 
17.0%
Space Separator 1982
 
8.7%
Other Punctuation 149
 
0.7%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 2538
15.2%
법 2158
12.9%
조 1646
9.9%
위 1552
9.3%
공 1521
9.1%
생 1521
9.1%
중 1521
9.1%
관 880
 
5.3%
리 874
 
5.2%
항 763
 
4.6%
Other values (41) 1697
10.2%
Decimal Number
ValueCountFrequency (%)
1 1872
48.6%
7 568
 
14.7%
2 475
 
12.3%
3 304
 
7.9%
4 294
 
7.6%
9 209
 
5.4%
6 57
 
1.5%
0 38
 
1.0%
8 33
 
0.9%
5 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
, 147
98.7%
. 2
 
1.3%
Space Separator
ValueCountFrequency (%)
1982
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16671
73.6%
Common 5988
 
26.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 2538
15.2%
법 2158
12.9%
조 1646
9.9%
위 1552
9.3%
공 1521
9.1%
생 1521
9.1%
중 1521
9.1%
관 880
 
5.3%
리 874
 
5.2%
항 763
 
4.6%
Other values (41) 1697
10.2%
Common
ValueCountFrequency (%)
1982
33.1%
1 1872
31.3%
7 568
 
9.5%
2 475
 
7.9%
3 304
 
5.1%
4 294
 
4.9%
9 209
 
3.5%
, 147
 
2.5%
6 57
 
1.0%
0 38
 
0.6%
Other values (6) 42
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16671
73.6%
ASCII 5988
 
26.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 2538
15.2%
법 2158
12.9%
조 1646
9.9%
위 1552
9.3%
공 1521
9.1%
생 1521
9.1%
중 1521
9.1%
관 880
 
5.3%
리 874
 
5.2%
항 763
 
4.6%
Other values (41) 1697
10.2%
ASCII
ValueCountFrequency (%)
1982
33.1%
1 1872
31.3%
7 568
 
9.5%
2 475
 
7.9%
3 304
 
5.1%
4 294
 
4.9%
9 209
 
3.5%
, 147
 
2.5%
6 57
 
1.0%
0 38
 
0.6%
Other values (6) 42
 
0.7%

위반일자
Real number (ℝ)

HIGH CORRELATION 

Distinct503
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20077715
Minimum19921017
Maximum20240110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.5 KiB
2024-05-10T23:10:41.073570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19921017
5-th percentile19961007
Q119990616
median20090525
Q320131104
95-th percentile20200632
Maximum20240110
Range319093
Interquartile range (IQR)140488

Descriptive statistics

Standard deviation76161.687
Coefficient of variation (CV)0.0037933443
Kurtosis-1.2111286
Mean20077715
Median Absolute Deviation (MAD)60080
Skewness-0.010803712
Sum3.9733798 × 1010
Variance5.8006026 × 109
MonotonicityNot monotonic
2024-05-10T23:10:41.591828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19990616 131
 
6.6%
19990611 130
 
6.6%
20130109 76
 
3.8%
19990519 44
 
2.2%
19980821 40
 
2.0%
20100101 38
 
1.9%
20170102 37
 
1.9%
20180102 33
 
1.7%
19960913 29
 
1.5%
20050301 28
 
1.4%
Other values (493) 1393
70.4%
ValueCountFrequency (%)
19921017 2
 
0.1%
19940119 4
0.2%
19940716 8
0.4%
19940719 1
 
0.1%
19941024 2
 
0.1%
19941109 2
 
0.1%
19941221 2
 
0.1%
19941222 3
 
0.2%
19950117 1
 
0.1%
19950208 1
 
0.1%
ValueCountFrequency (%)
20240110 4
0.2%
20231205 2
0.1%
20230524 1
 
0.1%
20230519 1
 
0.1%
20230509 1
 
0.1%
20230310 3
0.2%
20230227 1
 
0.1%
20230109 1
 
0.1%
20221222 1
 
0.1%
20221124 1
 
0.1%

위반내용
Text

Distinct477
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
2024-05-10T23:10:42.394981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length41
Mean length15.616978
Min length2

Characters and Unicode

Total characters30906
Distinct characters313
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique273 ?
Unique (%)13.8%

Sample

1st row반영구 문신 시술
2nd row반영구 문신 시술
3rd row반영구 문신 시술
4th row반영구 문신 시술
5th row폐업신고 미이행
ValueCountFrequency (%)
위생교육 355
 
6.7%
미수료 294
 
5.6%
위생교육미필1차 259
 
4.9%
아니함 128
 
2.4%
위생교육을 107
 
2.0%
받지 107
 
2.0%
미필 98
 
1.9%
및 92
 
1.7%
2008년도 85
 
1.6%
위반 85
 
1.6%
Other values (674) 3655
69.4%
2024-05-10T23:10:43.466178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3329
 
10.8%
위 1361
 
4.4%
미 1076
 
3.5%
생 1030
 
3.3%
( 894
 
2.9%
) 893
 
2.9%
교 889
 
2.9%
육 885
 
2.9%
1 770
 
2.5%
0 695
 
2.2%
Other values (303) 19084
61.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22833
73.9%
Space Separator 3329
 
10.8%
Decimal Number 2650
 
8.6%
Open Punctuation 902
 
2.9%
Close Punctuation 901
 
2.9%
Other Punctuation 264
 
0.9%
Dash Punctuation 24
 
0.1%
Math Symbol 2
 
< 0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
위 1361
 
6.0%
미 1076
 
4.7%
생 1030
 
4.5%
교 889
 
3.9%
육 885
 
3.9%
업 614
 
2.7%
수 593
 
2.6%
년 546
 
2.4%
이 513
 
2.2%
차 498
 
2.2%
Other values (280) 14828
64.9%
Decimal Number
ValueCountFrequency (%)
1 770
29.1%
0 695
26.2%
2 604
22.8%
6 155
 
5.8%
8 120
 
4.5%
3 91
 
3.4%
9 82
 
3.1%
4 51
 
1.9%
7 44
 
1.7%
5 38
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 171
64.8%
, 62
 
23.5%
: 27
 
10.2%
/ 3
 
1.1%
; 1
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 894
99.1%
「 8
 
0.9%
Close Punctuation
ValueCountFrequency (%)
) 893
99.1%
」 8
 
0.9%
Space Separator
ValueCountFrequency (%)
3329
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22833
73.9%
Common 8073
 
26.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
위 1361
 
6.0%
미 1076
 
4.7%
생 1030
 
4.5%
교 889
 
3.9%
육 885
 
3.9%
업 614
 
2.7%
수 593
 
2.6%
년 546
 
2.4%
이 513
 
2.2%
차 498
 
2.2%
Other values (280) 14828
64.9%
Common
ValueCountFrequency (%)
3329
41.2%
( 894
 
11.1%
) 893
 
11.1%
1 770
 
9.5%
0 695
 
8.6%
2 604
 
7.5%
. 171
 
2.1%
6 155
 
1.9%
8 120
 
1.5%
3 91
 
1.1%
Other values (13) 351
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22833
73.9%
ASCII 8056
 
26.1%
None 16
 
0.1%
CJK Compat 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3329
41.3%
( 894
 
11.1%
) 893
 
11.1%
1 770
 
9.6%
0 695
 
8.6%
2 604
 
7.5%
. 171
 
2.1%
6 155
 
1.9%
8 120
 
1.5%
3 91
 
1.1%
Other values (10) 334
 
4.1%
Hangul
ValueCountFrequency (%)
위 1361
 
6.0%
미 1076
 
4.7%
생 1030
 
4.5%
교 889
 
3.9%
육 885
 
3.9%
업 614
 
2.7%
수 593
 
2.6%
년 546
 
2.4%
이 513
 
2.2%
차 498
 
2.2%
Other values (280) 14828
64.9%
None
ValueCountFrequency (%)
」 8
50.0%
「 8
50.0%
CJK Compat
ValueCountFrequency (%)
㎡ 1
100.0%

처분내용
Text

Distinct320
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
2024-05-10T23:10:43.922700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length39
Mean length8.0207175
Min length2

Characters and Unicode

Total characters15873
Distinct characters129
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique176 ?
Unique (%)8.9%

Sample

1st row영업정지 60일 갈음, 과징금 564,000원 부과
2nd row영업정지 60일 갈음, 과징금 564,000원 부과
3rd row과태료부과 64만원(20%감경)
4th row과태료부과 64만원(20%감경)
5th row직권말소
ValueCountFrequency (%)
경고 607
22.8%
개선명령 212
 
8.0%
영업소폐쇄 174
 
6.5%
과태료부과 127
 
4.8%
과태료 93
 
3.5%
부과 93
 
3.5%
영업정지 82
 
3.1%
영업허가취소 54
 
2.0%
및 49
 
1.8%
16만원 48
 
1.8%
Other values (315) 1122
42.2%
2024-05-10T23:10:44.911792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 1234
 
7.8%
( 957
 
6.0%
) 956
 
6.0%
경 735
 
4.6%
고 716
 
4.5%
0 705
 
4.4%
683
 
4.3%
부 601
 
3.8%
료 584
 
3.7%
업 584
 
3.7%
Other values (119) 8118
51.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10745
67.7%
Decimal Number 2220
 
14.0%
Open Punctuation 957
 
6.0%
Close Punctuation 956
 
6.0%
Space Separator 683
 
4.3%
Other Punctuation 274
 
1.7%
Dash Punctuation 21
 
0.1%
Math Symbol 17
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 1234
 
11.5%
경 735
 
6.8%
고 716
 
6.7%
부 601
 
5.6%
료 584
 
5.4%
업 584
 
5.4%
영 563
 
5.2%
태 553
 
5.1%
원 549
 
5.1%
만 483
 
4.5%
Other values (99) 4143
38.6%
Decimal Number
ValueCountFrequency (%)
0 705
31.8%
2 431
19.4%
1 362
16.3%
6 197
 
8.9%
5 128
 
5.8%
4 118
 
5.3%
3 114
 
5.1%
8 93
 
4.2%
7 41
 
1.8%
9 31
 
1.4%
Other Punctuation
ValueCountFrequency (%)
, 114
41.6%
. 80
29.2%
% 48
17.5%
/ 29
 
10.6%
: 3
 
1.1%
Open Punctuation
ValueCountFrequency (%)
( 957
100.0%
Close Punctuation
ValueCountFrequency (%)
) 956
100.0%
Space Separator
ValueCountFrequency (%)
683
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Math Symbol
ValueCountFrequency (%)
~ 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10745
67.7%
Common 5128
32.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 1234
 
11.5%
경 735
 
6.8%
고 716
 
6.7%
부 601
 
5.6%
료 584
 
5.4%
업 584
 
5.4%
영 563
 
5.2%
태 553
 
5.1%
원 549
 
5.1%
만 483
 
4.5%
Other values (99) 4143
38.6%
Common
ValueCountFrequency (%)
( 957
18.7%
) 956
18.6%
0 705
13.7%
683
13.3%
2 431
8.4%
1 362
 
7.1%
6 197
 
3.8%
5 128
 
2.5%
4 118
 
2.3%
, 114
 
2.2%
Other values (10) 477
9.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10744
67.7%
ASCII 5128
32.3%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 1234
 
11.5%
경 735
 
6.8%
고 716
 
6.7%
부 601
 
5.6%
료 584
 
5.4%
업 584
 
5.4%
영 563
 
5.2%
태 553
 
5.1%
원 549
 
5.1%
만 483
 
4.5%
Other values (98) 4142
38.6%
ASCII
ValueCountFrequency (%)
( 957
18.7%
) 956
18.6%
0 705
13.7%
683
13.3%
2 431
8.4%
1 362
 
7.1%
6 197
 
3.8%
5 128
 
2.5%
4 118
 
2.3%
, 114
 
2.2%
Other values (10) 477
9.3%
Compat Jamo
ValueCountFrequency (%)
ㄹ 1
100.0%

처분기간
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct23
Distinct (%)3.2%
Missing1270
Missing (%)64.2%
Infinite0
Infinite (%)0.0%
Mean7.2595205
Minimum0
Maximum122
Zeros572
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size17.5 KiB
2024-05-10T23:10:45.449127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile61
Maximum122
Range122
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21.92689
Coefficient of variation (CV)3.0204322
Kurtosis12.398373
Mean7.2595205
Median Absolute Deviation (MAD)0
Skewness3.5368863
Sum5147
Variance480.78849
MonotonicityNot monotonic
2024-05-10T23:10:45.844541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 572
28.9%
5 20
 
1.0%
15 17
 
0.9%
1 15
 
0.8%
61 14
 
0.7%
92 12
 
0.6%
30 9
 
0.5%
122 9
 
0.5%
10 8
 
0.4%
62 5
 
0.3%
Other values (13) 28
 
1.4%
(Missing) 1270
64.2%
ValueCountFrequency (%)
0 572
28.9%
1 15
 
0.8%
3 1
 
0.1%
5 20
 
1.0%
8 3
 
0.2%
10 8
 
0.4%
12 1
 
0.1%
13 1
 
0.1%
14 1
 
0.1%
15 17
 
0.9%
ValueCountFrequency (%)
122 9
0.5%
92 12
0.6%
76 4
 
0.2%
75 1
 
0.1%
62 5
 
0.3%
61 14
0.7%
60 2
 
0.1%
59 4
 
0.2%
31 4
 
0.2%
30 9
0.5%

영업장면적(㎡)
Real number (ℝ)

MISSING  ZEROS 

Distinct737
Distinct (%)42.0%
Missing223
Missing (%)11.3%
Infinite0
Infinite (%)0.0%
Mean1263.0097
Minimum0
Maximum286431
Zeros35
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size17.5 KiB
2024-05-10T23:10:46.246172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.04
Q129.09
median59
Q3147.25
95-th percentile1200
Maximum286431
Range286431
Interquartile range (IQR)118.16

Descriptive statistics

Standard deviation12383.515
Coefficient of variation (CV)9.8047665
Kurtosis234.89001
Mean1263.0097
Median Absolute Deviation (MAD)40
Skewness14.187301
Sum2217845
Variance1.5335144 × 108
MonotonicityNot monotonic
2024-05-10T23:10:46.679785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.0 52
 
2.6%
53.0 35
 
1.8%
0.0 35
 
1.8%
40.0 26
 
1.3%
99.0 24
 
1.2%
116.0 21
 
1.1%
66.0 20
 
1.0%
1200.0 19
 
1.0%
36.0 19
 
1.0%
150.0 18
 
0.9%
Other values (727) 1487
75.1%
(Missing) 223
 
11.3%
ValueCountFrequency (%)
0.0 35
1.8%
5.0 1
 
0.1%
6.6 2
 
0.1%
6.94 3
 
0.2%
7.0 2
 
0.1%
8.0 2
 
0.1%
8.25 1
 
0.1%
8.4 2
 
0.1%
8.75 1
 
0.1%
9.0 6
 
0.3%
ValueCountFrequency (%)
286431.0 1
 
0.1%
156062.0 1
 
0.1%
130690.0 9
0.5%
93115.1 1
 
0.1%
22316.48 1
 
0.1%
22133.0 3
 
0.2%
20270.0 1
 
0.1%
9017.07 1
 
0.1%
7223.9 1
 
0.1%
6640.98 1
 
0.1%

Interactions

2024-05-10T23:10:21.230334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:10:14.134476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:10:15.929946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:10:17.864839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-05-10T23:10:20.280849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:10:22.267479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-05-10T23:10:17.105966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:10:18.901122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:10:20.572611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:10:22.619383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:10:15.517075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:10:17.455649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:10:19.291068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:10:20.898181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T23:10:46.954727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분일자업종명업태명지도점검일자위반일자처분기간영업장면적(㎡)
처분일자1.0000.6820.7271.0001.0000.5110.041
업종명0.6821.0000.9520.6800.6790.3320.132
업태명0.7270.9521.0000.7230.7220.3230.516
지도점검일자1.0000.6800.7231.0001.0000.5110.052
위반일자1.0000.6790.7221.0001.0000.5100.050
처분기간0.5110.3320.3230.5110.5101.000NaN
영업장면적(㎡)0.0410.1320.5160.0520.050NaN1.000
2024-05-10T23:10:47.252285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업태명
업종명1.0000.673
업태명0.6731.000
2024-05-10T23:10:47.506044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분일자지도점검일자위반일자처분기간영업장면적(㎡)업종명업태명
처분일자1.0000.9990.9990.5440.2540.3340.318
지도점검일자0.9991.0001.0000.5430.2550.3320.315
위반일자0.9991.0001.0000.5450.2570.3320.314
처분기간0.5440.5430.5451.0000.3820.1840.147
영업장면적(㎡)0.2540.2550.2570.3821.0000.0650.247
업종명0.3340.3320.3320.1840.0651.0000.673
업태명0.3180.3150.3140.1470.2470.6731.000

Missing values

2024-05-10T23:10:23.121184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T23:10:24.020596image/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.
2024-05-10T23:10:24.582479image/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

시군구코드처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자행정처분상태처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)
0318000020240213276종합미용업일반미용업조복순 헤어 아뜨리에서울특별시 영등포구 디지털로57길 24, (대림동,(지하1층 ~ 지상1층))서울특별시 영등포구 대림동 832번지 32호 (지하1층 ~ 지상1층)20240110처분확정영업정지 60일 갈음, 과징금 564,000원 부과법 제11조제1항제4호20240110반영구 문신 시술영업정지 60일 갈음, 과징금 564,000원 부과<NA>150.0
1318000020240213276종합미용업일반미용업조복순 헤어 아뜨리에서울특별시 영등포구 디지털로57길 24, (대림동,(지하1층 ~ 지상1층))서울특별시 영등포구 대림동 832번지 32호 (지하1층 ~ 지상1층)20240110처분확정영업정지 60일 갈음, 과징금 564,000원 부과법 제11조제1항제4호20240110반영구 문신 시술영업정지 60일 갈음, 과징금 564,000원 부과<NA>150.0
2318000020240205276종합미용업일반미용업조복순 헤어 아뜨리에서울특별시 영등포구 디지털로57길 24, (대림동,(지하1층 ~ 지상1층))서울특별시 영등포구 대림동 832번지 32호 (지하1층 ~ 지상1층)20240110처분확정과태료부과 64만원(20%감경)법 제11조제1항제4호20240110반영구 문신 시술과태료부과 64만원(20%감경)<NA>150.0
3318000020240205276종합미용업일반미용업조복순 헤어 아뜨리에서울특별시 영등포구 디지털로57길 24, (대림동,(지하1층 ~ 지상1층))서울특별시 영등포구 대림동 832번지 32호 (지하1층 ~ 지상1층)20240110처분확정과태료부과 64만원(20%감경)법 제11조제1항제4호20240110반영구 문신 시술과태료부과 64만원(20%감경)<NA>150.0
4318000020231227362일반미용업일반미용업와이헤어모드서울특별시 영등포구 대방천로 188, (신길동,1층)서울특별시 영등포구 신길동 4528번지 1층20231227처분확정직권말소법 제11조제3항제2호20231205폐업신고 미이행직권말소<NA>42.26
53180000202312272022-0011일반미용업일반미용업명사미용실서울특별시 영등포구 디지털로 392, 정성빌딩 1층 102호 (대림동)서울특별시 영등포구 대림동 862번지 3호 정성빌딩-10220231205처분확정직권말소법 제11조제3항제2호20231205폐업신고 미이행직권말소<NA>29.8
631800002023062988숙박업(일반)여관업영등포여관 오성장서울특별시 영등포구 양산로 206, (영등포동5가)서울특별시 영등포구 영등포동5가 58번지20230524처분확정과태료부과(20%경감된 56,000원)법 제82조제2항20230524재난배상책임보험 미가입과태료부과(20%경감된 56,000원)<NA>189.0
7318000020230619163숙박업(일반)여관업세종서울특별시 영등포구 영등포로18길 2-1, (양평동1가)서울특별시 영등포구 양평동1가 9번지 14호20221222처분확정과징금부과(영업정지 2월 갈음, 564,000원)법 제11조제1항제8호20221222청소년 이성혼숙과징금부과(영업정지 2월 갈음, 564,000원)<NA>122.0
831800002023061240숙박업(일반)일반호텔더문 호텔서울특별시 영등포구 영중로8길 13, (영등포동3가)서울특별시 영등포구 영등포동3가 17번지 9호20230509처분확정과태료부과(20%경감 4만원부과)법 제82조제2항20230509재난배상책임보험 미가입과태료부과(20%경감 4만원부과)<NA>888.0
9318000020230607297숙박업(일반)여관업호텔 레드(Hotel Red)서울특별시 영등포구 영중로10길 32-2, (영등포동3가)서울특별시 영등포구 영등포동3가 15번지 5호20230519처분확정직권말소법 제11조제3항제2호20230519폐업신고의무 위반직권말소<NA>818.87
시군구코드처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자행정처분상태처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)
196931800001994071605600440100268미용업일반미용업조일<NA>서울특별시 영등포구 여의도동 산 43번지 3호19940716처분확정(영업허가취소)공중위생법19940716(정당한사유없이계속하여6월이상휴업한때)(영업허가취소)<NA>28.56
197031800001994071605600440100679미용업일반미용업박순영미용실<NA>서울특별시 영등포구 대림동 산 845번지 15호19940716처분확정(영업허가취소)공중위생법19940716(정당한사유없이계속하여6월이상휴업한때)(영업허가취소)<NA>10.31
197131800001994071605600440100333미용업일반미용업개성시대<NA>서울특별시 영등포구 신길동 산 117번지 16호19940716처분확정(영업허가취소)공중위생법19940716(정당한사유없이계속하여6월이상휴업한때)(영업허가취소)<NA>21.7
197231800001994071605600440100153미용업일반미용업영은<NA>서울특별시 영등포구 양평동1가 산 241번지 1호19940716처분확정(영업허가취소)공중위생법19940716(정당한사유없이계속하여6월이상휴업한때)0(영업허가취소)<NA>11.76
197331800001994011905600440100776미용업일반미용업원미용실<NA>서울특별시 영등포구 대림동 산 967번지 1호19940119처분확정(영업허가취소)공중위생법19940119(정당한사유없이계속하여6월이상휴업한때)(영업허가취소)<NA>21.53
197431800001994011905600440100185미용업일반미용업박<NA>서울특별시 영등포구 문래동4가 산 6번지 3호19940119처분확정(영업허가취소)공중위생법19940119(정당한사유없이계속하여6월이상휴업한때)(영업허가취소)<NA>12.0
197531800001994011905600440100676미용업일반미용업아씨<NA>서울특별시 영등포구 대림동 산 759번지 9호19940119처분확정(영업허가취소)공중위생법19940119(정당한사유없이계속하여6월이상휴업한때)(영업허가취소)<NA>14.23
197631800001994011905600440100694미용업일반미용업경진미용실<NA>서울특별시 영등포구 신길동 산 329번지 23호19940119처분확정(영업허가취소)공중위생법19940119(정당한사유없이계속하여6월이상휴업한때)(영업허가취소)<NA>18.6
197731800001992101705600440100070미용업일반미용업김애경<NA>서울특별시 영등포구 영등포동7가 산 83번지 5호19921017처분확정(영업허가취소)공중위생법19921017(정당한사유없이계속하여6월이상휴업한때)0(영업허가취소)<NA>15.9
197831800001992101705600440100170미용업일반미용업두리<NA>서울특별시 영등포구 양평동4가 산 153번지 32호19921017처분확정(영업허가취소)공중위생법19921017(정당한사유없이계속하여6월이상휴업한때)(영업허가취소)<NA>20.53

Duplicate rows

Most frequently occurring

시군구코드처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자행정처분상태처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)# duplicates
181318000020170929282위생관리용역업위생관리용역업 기타(주)제이엘티엠서울특별시 영등포구 여의나루로 67, 1205호 (여의도동, 신송빌딩)서울특별시 영등포구 여의도동 25번지 4호 신송빌딩-120520170809처분확정영업소폐쇄법 제11조제3항제1호20170809공중위생영업자가 정당한 사유 없이 6개월 이상 계속 휴업영업소폐쇄<NA>39.08
151318000020120817479일반미용업일반미용업제이엔에스헤어클럽<NA>서울특별시 영등포구 여의도동 36번지 롯데캐슬엠파이어 206호20120716처분확정경고공중위생관리법 제4조제4항20120716소독기구 미구비경고<NA>44.465
5031800002009081789목욕장업공동탕업신길한증막서울특별시 영등포구 영등포로62나길 4, (신길동)서울특별시 영등포구 신길동 47번지 10호20090429처분확정과태료(30만원)부과공중위생관리법 제3조제2항20090429시설물을멸실하고폐업신고를하지아니함.과태료(30만원)부과<NA><NA>4
5131800002009081789목욕장업공동탕업신길한증막서울특별시 영등포구 영등포로62나길 4, (신길동)서울특별시 영등포구 신길동 47번지 10호20090429처분확정영업소폐쇄및과태료(30만원)부과공중위생관리법 제3조제2항20090429시설물을멸실하고폐업신고를하지아니함.영업소폐쇄및과태료(30만원)부과<NA><NA>4
152318000020120820610일반미용업일반미용업플래르미용실<NA>서울특별시 영등포구 여의도동 54번지 6호 영창빌딩101호20120726처분확정경고공중위생관리법 제4조제4항20120726소독기구 미구비경고<NA>31.14
153318000020120820610일반미용업일반미용업플래르미용실<NA>서울특별시 영등포구 여의도동 54번지 6호 영창빌딩101호20120726처분확정과태료 40만원부과공중위생관리법 제4조제4항20120726소독기구 미구비과태료 40만원부과<NA>31.14
1843180000201804111위생관리용역업위생관리용역업(주)한성세영비엔에이서울특별시 영등포구 시흥대로173길 13, 101동 110호 (대림동, 신대림자이 상가)서울특별시 영등포구 대림동 1121번지 101 신대림자이 상가-11020180220처분확정과태료 16만원법 제17조201801022017년 정기 위생교육 미수료과태료 16만원<NA>36.04
14318000020050325<NA>이용업일반이용업대신서울특별시 영등포구 영등포로 375-1, (신길동)서울특별시 영등포구 신길동 96번지 11호20050325처분확정개선명령공중위생관리법 제11조 및 동법시행규칙 제19조20050301면허대여(1차)개선명령053.03
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