Overview

Dataset statistics

Number of variables17
Number of observations665
Missing cells773
Missing cells (%)6.8%
Duplicate rows21
Duplicate rows (%)3.2%
Total size in memory92.3 KiB
Average record size in memory142.2 B

Variable types

Categorical4
Numeric5
Text8

Dataset

Description시군구코드,처분일자,교부번호,업종명,업태명,업소명,소재지도로명,소재지지번,지도점검일자,행정처분상태,처분명,법적근거,위반일자,위반내용,처분내용,처분기간,영업장면적(㎡)
Author마포구
URLhttps://data.seoul.go.kr/dataList/OA-11375/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
행정처분상태 has constant value ""Constant
Dataset has 21 (3.2%) duplicate rowsDuplicates
처분일자 is highly overall correlated with 지도점검일자 and 1 other fieldsHigh correlation
지도점검일자 is highly overall correlated with 처분일자 and 1 other fieldsHigh correlation
위반일자 is highly overall correlated with 처분일자 and 1 other fieldsHigh correlation
업종명 is highly overall correlated with 업태명High correlation
업태명 is highly overall correlated with 업종명High correlation
교부번호 has 52 (7.8%) missing valuesMissing
소재지도로명 has 61 (9.2%) missing valuesMissing
처분기간 has 609 (91.6%) missing valuesMissing
영업장면적(㎡) has 51 (7.7%) missing valuesMissing
처분일자 is highly skewed (γ1 = 25.68517701)Skewed
지도점검일자 is highly skewed (γ1 = 25.68579627)Skewed
처분기간 has 11 (1.7%) zerosZeros
영업장면적(㎡) has 17 (2.6%) zerosZeros

Reproduction

Analysis started2024-05-11 09:13:10.720237
Analysis finished2024-05-11 09:13:21.659017
Duration10.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
3130000
665 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3130000
2nd row3130000
3rd row3130000
4th row3130000
5th row3130000

Common Values

ValueCountFrequency (%)
3130000 665
100.0%

Length

2024-05-11T09:13:21.879676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:13:22.210752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3130000 665
100.0%

처분일자
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct181
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20175878
Minimum20030807
Maximum50020609
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-05-11T09:13:22.555084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20030807
5-th percentile20040428
Q120070402
median20141204
Q320181023
95-th percentile20210553
Maximum50020609
Range29989802
Interquartile range (IQR)110621

Descriptive statistics

Standard deviation1160606
Coefficient of variation (CV)0.057524438
Kurtosis661.4752
Mean20175878
Median Absolute Deviation (MAD)50013
Skewness25.685177
Sum1.3416959 × 1010
Variance1.3470064 × 1012
MonotonicityNot monotonic
2024-05-11T09:13:23.125365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20191217 25
 
3.8%
20141204 25
 
3.8%
20050916 23
 
3.5%
20141013 19
 
2.9%
20180430 18
 
2.7%
20160113 16
 
2.4%
20040406 16
 
2.4%
20130816 16
 
2.4%
20211230 15
 
2.3%
20171207 14
 
2.1%
Other values (171) 478
71.9%
ValueCountFrequency (%)
20030807 1
 
0.2%
20040201 4
 
0.6%
20040202 4
 
0.6%
20040213 4
 
0.6%
20040315 1
 
0.2%
20040406 16
2.4%
20040428 8
1.2%
20040512 1
 
0.2%
20040519 1
 
0.2%
20040614 1
 
0.2%
ValueCountFrequency (%)
50020609 1
 
0.2%
20500504 1
 
0.2%
20240104 1
 
0.2%
20230421 3
 
0.5%
20230208 2
 
0.3%
20220325 1
 
0.2%
20211230 15
2.3%
20211125 4
 
0.6%
20211102 1
 
0.2%
20210702 1
 
0.2%

교부번호
Text

MISSING 

Distinct340
Distinct (%)55.5%
Missing52
Missing (%)7.8%
Memory size5.3 KiB
2024-05-11T09:13:23.884540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.0163132
Min length1

Characters and Unicode

Total characters2462
Distinct characters13
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

Unique206 ?
Unique (%)33.6%

Sample

1st row1
2nd row1
3rd row3
4th row2
5th row5
ValueCountFrequency (%)
2014-2 19
 
3.1%
81 8
 
1.3%
91 8
 
1.3%
60 7
 
1.1%
69 7
 
1.1%
364 6
 
1.0%
730 6
 
1.0%
245 6
 
1.0%
2016-23 5
 
0.8%
79 5
 
0.8%
Other values (330) 536
87.4%
2024-05-11T09:13:25.204597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 444
18.0%
0 440
17.9%
2 396
16.1%
- 177
 
7.2%
7 154
 
6.3%
3 153
 
6.2%
5 149
 
6.1%
4 144
 
5.8%
6 143
 
5.8%
8 138
 
5.6%
Other values (3) 124
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2281
92.6%
Dash Punctuation 177
 
7.2%
Other Letter 4
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 444
19.5%
0 440
19.3%
2 396
17.4%
7 154
 
6.8%
3 153
 
6.7%
5 149
 
6.5%
4 144
 
6.3%
6 143
 
6.3%
8 138
 
6.0%
9 120
 
5.3%
Other Letter
ValueCountFrequency (%)
제 2
50.0%
호 2
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 177
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2458
99.8%
Hangul 4
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 444
18.1%
0 440
17.9%
2 396
16.1%
- 177
 
7.2%
7 154
 
6.3%
3 153
 
6.2%
5 149
 
6.1%
4 144
 
5.9%
6 143
 
5.8%
8 138
 
5.6%
Hangul
ValueCountFrequency (%)
제 2
50.0%
호 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2458
99.8%
Hangul 4
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 444
18.1%
0 440
17.9%
2 396
16.1%
- 177
 
7.2%
7 154
 
6.3%
3 153
 
6.2%
5 149
 
6.1%
4 144
 
5.9%
6 143
 
5.8%
8 138
 
5.6%
Hangul
ValueCountFrequency (%)
제 2
50.0%
호 2
50.0%

업종명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
숙박업(일반)
146 
위생관리용역업
97 
피부미용업
93 
목욕장업
89 
이용업
62 
Other values (12)
178 

Length

Max length23
Median length16
Mean length5.5203008
Min length3

Unique

Unique3 ?
Unique (%)0.5%

Sample

1st row숙박업(일반)
2nd row숙박업(일반)
3rd row숙박업(일반)
4th row숙박업(일반)
5th row숙박업(일반)

Common Values

ValueCountFrequency (%)
숙박업(일반) 146
22.0%
위생관리용역업 97
14.6%
피부미용업 93
14.0%
목욕장업 89
13.4%
이용업 62
9.3%
미용업 48
 
7.2%
일반미용업 43
 
6.5%
종합미용업 24
 
3.6%
네일미용업 17
 
2.6%
세탁업 15
 
2.3%
Other values (7) 31
 
4.7%

Length

2024-05-11T09:13:25.670619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
숙박업(일반 146
20.9%
피부미용업 107
15.3%
위생관리용역업 97
13.9%
목욕장업 89
12.7%
미용업 64
9.1%
이용업 62
8.9%
일반미용업 46
 
6.6%
네일미용업 32
 
4.6%
종합미용업 24
 
3.4%
화장ㆍ분장 16
 
2.3%
Other values (3) 17
 
2.4%

업태명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
피부미용업
118 
여관업
108 
위생관리용역업
97 
일반미용업
85 
일반이용업
62 
Other values (13)
195 

Length

Max length14
Median length9
Mean length5.4165414
Min length2

Unique

Unique3 ?
Unique (%)0.5%

Sample

1st row여인숙업
2nd row여인숙업
3rd row여인숙업
4th row여관업
5th row여관업

Common Values

ValueCountFrequency (%)
피부미용업 118
17.7%
여관업 108
16.2%
위생관리용역업 97
14.6%
일반미용업 85
12.8%
일반이용업 62
9.3%
공동탕업 44
 
6.6%
공동탕업+찜질시설서비스영업 44
 
6.6%
네일아트업 30
 
4.5%
여인숙업 21
 
3.2%
메이크업업 14
 
2.1%
Other values (8) 42
 
6.3%

Length

2024-05-11T09:13:26.111266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
피부미용업 118
17.7%
여관업 108
16.2%
위생관리용역업 97
14.6%
일반미용업 85
12.8%
일반이용업 62
9.3%
공동탕업 44
 
6.6%
공동탕업+찜질시설서비스영업 44
 
6.6%
네일아트업 30
 
4.5%
여인숙업 21
 
3.2%
메이크업업 14
 
2.1%
Other values (8) 42
 
6.3%

업소명
Text

Distinct448
Distinct (%)67.4%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
2024-05-11T09:13:26.607476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length22
Mean length5.875188
Min length1

Characters and Unicode

Total characters3907
Distinct characters423
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique328 ?
Unique (%)49.3%

Sample

1st row행화
2nd row행화
3rd row행화
4th row귀빈장여관
5th row귀빈장여관
ValueCountFrequency (%)
뷰티프렌즈 19
 
2.5%
허준미용실 6
 
0.8%
수정탕 6
 
0.8%
준오헤어 6
 
0.8%
성지 6
 
0.8%
주식회사 6
 
0.8%
스킨앤제이(skin&j 5
 
0.7%
라세느 5
 
0.7%
로타리 5
 
0.7%
헤어 5
 
0.7%
Other values (491) 698
91.0%
2024-05-11T09:13:27.590452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 116
 
3.0%
( 105
 
2.7%
) 105
 
2.7%
102
 
2.6%
이 94
 
2.4%
주 82
 
2.1%
사 64
 
1.6%
리 50
 
1.3%
텔 48
 
1.2%
우 47
 
1.2%
Other values (413) 3094
79.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3267
83.6%
Uppercase Letter 167
 
4.3%
Lowercase Letter 110
 
2.8%
Open Punctuation 105
 
2.7%
Close Punctuation 105
 
2.7%
Space Separator 102
 
2.6%
Decimal Number 35
 
0.9%
Other Punctuation 15
 
0.4%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 116
 
3.6%
이 94
 
2.9%
주 82
 
2.5%
사 64
 
2.0%
리 50
 
1.5%
텔 48
 
1.5%
우 47
 
1.4%
탕 45
 
1.4%
미 45
 
1.4%
관 45
 
1.4%
Other values (359) 2631
80.5%
Uppercase Letter
ValueCountFrequency (%)
I 19
 
11.4%
S 14
 
8.4%
N 13
 
7.8%
A 11
 
6.6%
V 11
 
6.6%
J 11
 
6.6%
M 10
 
6.0%
T 9
 
5.4%
L 9
 
5.4%
K 9
 
5.4%
Other values (12) 51
30.5%
Lowercase Letter
ValueCountFrequency (%)
a 13
11.8%
i 12
10.9%
o 12
10.9%
e 12
10.9%
n 10
9.1%
h 7
 
6.4%
t 6
 
5.5%
y 5
 
4.5%
r 5
 
4.5%
s 4
 
3.6%
Other values (9) 24
21.8%
Other Punctuation
ValueCountFrequency (%)
& 10
66.7%
# 2
 
13.3%
' 1
 
6.7%
？ 1
 
6.7%
. 1
 
6.7%
Decimal Number
ValueCountFrequency (%)
2 15
42.9%
4 13
37.1%
0 4
 
11.4%
1 3
 
8.6%
Open Punctuation
ValueCountFrequency (%)
( 105
100.0%
Close Punctuation
ValueCountFrequency (%)
) 105
100.0%
Space Separator
ValueCountFrequency (%)
102
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3267
83.6%
Common 363
 
9.3%
Latin 277
 
7.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 116
 
3.6%
이 94
 
2.9%
주 82
 
2.5%
사 64
 
2.0%
리 50
 
1.5%
텔 48
 
1.5%
우 47
 
1.4%
탕 45
 
1.4%
미 45
 
1.4%
관 45
 
1.4%
Other values (359) 2631
80.5%
Latin
ValueCountFrequency (%)
I 19
 
6.9%
S 14
 
5.1%
N 13
 
4.7%
a 13
 
4.7%
i 12
 
4.3%
o 12
 
4.3%
e 12
 
4.3%
A 11
 
4.0%
V 11
 
4.0%
J 11
 
4.0%
Other values (31) 149
53.8%
Common
ValueCountFrequency (%)
( 105
28.9%
) 105
28.9%
102
28.1%
2 15
 
4.1%
4 13
 
3.6%
& 10
 
2.8%
0 4
 
1.1%
1 3
 
0.8%
# 2
 
0.6%
' 1
 
0.3%
Other values (3) 3
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3266
83.6%
ASCII 639
 
16.4%
None 1
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 116
 
3.6%
이 94
 
2.9%
주 82
 
2.5%
사 64
 
2.0%
리 50
 
1.5%
텔 48
 
1.5%
우 47
 
1.4%
탕 45
 
1.4%
미 45
 
1.4%
관 45
 
1.4%
Other values (358) 2630
80.5%
ASCII
ValueCountFrequency (%)
( 105
16.4%
) 105
16.4%
102
16.0%
I 19
 
3.0%
2 15
 
2.3%
S 14
 
2.2%
4 13
 
2.0%
N 13
 
2.0%
a 13
 
2.0%
i 12
 
1.9%
Other values (43) 228
35.7%
None
ValueCountFrequency (%)
？ 1
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅇ 1
100.0%

소재지도로명
Text

MISSING 

Distinct386
Distinct (%)63.9%
Missing61
Missing (%)9.2%
Memory size5.3 KiB
2024-05-11T09:13:28.291348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length46
Mean length30.135762
Min length22

Characters and Unicode

Total characters18202
Distinct characters224
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique274 ?
Unique (%)45.4%

Sample

1st row서울특별시 마포구 마포대로20길 8-3, (아현동)
2nd row서울특별시 마포구 마포대로20길 8-3, (아현동)
3rd row서울특별시 마포구 마포대로20길 8-3, (아현동)
4th row서울특별시 마포구 만리재옛8길 4-1, (공덕동)
5th row서울특별시 마포구 새창로6가길 4, (도화동)
ValueCountFrequency (%)
서울특별시 604
 
17.6%
마포구 604
 
17.6%
노고산동 73
 
2.1%
서교동 63
 
1.8%
동교동 61
 
1.8%
1층 49
 
1.4%
성산동 39
 
1.1%
4층 36
 
1.1%
월드컵북로 34
 
1.0%
2층 32
 
0.9%
Other values (565) 1832
53.5%
2024-05-11T09:13:29.523312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2824
 
15.5%
, 864
 
4.7%
서 745
 
4.1%
동 743
 
4.1%
마 679
 
3.7%
포 668
 
3.7%
( 637
 
3.5%
) 636
 
3.5%
울 625
 
3.4%
시 608
 
3.3%
Other values (214) 9173
50.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10569
58.1%
Space Separator 2824
 
15.5%
Decimal Number 2536
 
13.9%
Other Punctuation 867
 
4.8%
Open Punctuation 637
 
3.5%
Close Punctuation 636
 
3.5%
Dash Punctuation 94
 
0.5%
Uppercase Letter 36
 
0.2%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 745
 
7.0%
동 743
 
7.0%
마 679
 
6.4%
포 668
 
6.3%
울 625
 
5.9%
시 608
 
5.8%
구 606
 
5.7%
별 604
 
5.7%
특 604
 
5.7%
로 567
 
5.4%
Other values (184) 4120
39.0%
Uppercase Letter
ValueCountFrequency (%)
B 12
33.3%
S 4
 
11.1%
K 4
 
11.1%
L 3
 
8.3%
A 3
 
8.3%
D 2
 
5.6%
G 2
 
5.6%
E 1
 
2.8%
C 1
 
2.8%
M 1
 
2.8%
Other values (3) 3
 
8.3%
Decimal Number
ValueCountFrequency (%)
1 601
23.7%
2 426
16.8%
3 272
10.7%
0 219
 
8.6%
4 208
 
8.2%
6 201
 
7.9%
8 186
 
7.3%
5 179
 
7.1%
7 140
 
5.5%
9 104
 
4.1%
Other Punctuation
ValueCountFrequency (%)
, 864
99.7%
. 3
 
0.3%
Space Separator
ValueCountFrequency (%)
2824
100.0%
Open Punctuation
ValueCountFrequency (%)
( 637
100.0%
Close Punctuation
ValueCountFrequency (%)
) 636
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 94
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10569
58.1%
Common 7597
41.7%
Latin 36
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 745
 
7.0%
동 743
 
7.0%
마 679
 
6.4%
포 668
 
6.3%
울 625
 
5.9%
시 608
 
5.8%
구 606
 
5.7%
별 604
 
5.7%
특 604
 
5.7%
로 567
 
5.4%
Other values (184) 4120
39.0%
Common
ValueCountFrequency (%)
2824
37.2%
, 864
 
11.4%
( 637
 
8.4%
) 636
 
8.4%
1 601
 
7.9%
2 426
 
5.6%
3 272
 
3.6%
0 219
 
2.9%
4 208
 
2.7%
6 201
 
2.6%
Other values (7) 709
 
9.3%
Latin
ValueCountFrequency (%)
B 12
33.3%
S 4
 
11.1%
K 4
 
11.1%
L 3
 
8.3%
A 3
 
8.3%
D 2
 
5.6%
G 2
 
5.6%
E 1
 
2.8%
C 1
 
2.8%
M 1
 
2.8%
Other values (3) 3
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10569
58.1%
ASCII 7633
41.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2824
37.0%
, 864
 
11.3%
( 637
 
8.3%
) 636
 
8.3%
1 601
 
7.9%
2 426
 
5.6%
3 272
 
3.6%
0 219
 
2.9%
4 208
 
2.7%
6 201
 
2.6%
Other values (20) 745
 
9.8%
Hangul
ValueCountFrequency (%)
서 745
 
7.0%
동 743
 
7.0%
마 679
 
6.4%
포 668
 
6.3%
울 625
 
5.9%
시 608
 
5.8%
구 606
 
5.7%
별 604
 
5.7%
특 604
 
5.7%
로 567
 
5.4%
Other values (184) 4120
39.0%

소재지지번
Text

Distinct428
Distinct (%)64.4%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
2024-05-11T09:13:30.188826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length43
Mean length28.242105
Min length21

Characters and Unicode

Total characters18781
Distinct characters199
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

Unique301 ?
Unique (%)45.3%

Sample

1st row서울특별시 마포구 아현동 425번지 11호
2nd row서울특별시 마포구 아현동 425번지 11호
3rd row서울특별시 마포구 아현동 425번지 11호
4th row서울특별시 마포구 대흥동 12번지 42호
5th row서울특별시 마포구 대흥동 12번지 42호
ValueCountFrequency (%)
서울특별시 665
 
18.1%
마포구 665
 
18.1%
서교동 88
 
2.4%
노고산동 86
 
2.3%
동교동 70
 
1.9%
성산동 63
 
1.7%
1호 60
 
1.6%
도화동 56
 
1.5%
1층 50
 
1.4%
아현동 39
 
1.1%
Other values (503) 1825
49.8%
2024-05-11T09:13:31.841421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4650
24.8%
1 812
 
4.3%
서 758
 
4.0%
동 754
 
4.0%
지 747
 
4.0%
마 681
 
3.6%
포 681
 
3.6%
시 671
 
3.6%
울 668
 
3.6%
구 667
 
3.6%
Other values (189) 7692
41.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10526
56.0%
Space Separator 4650
24.8%
Decimal Number 3403
 
18.1%
Close Punctuation 43
 
0.2%
Open Punctuation 42
 
0.2%
Dash Punctuation 41
 
0.2%
Uppercase Letter 38
 
0.2%
Other Punctuation 30
 
0.2%
Math Symbol 6
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 758
 
7.2%
동 754
 
7.2%
지 747
 
7.1%
마 681
 
6.5%
포 681
 
6.5%
시 671
 
6.4%
울 668
 
6.3%
구 667
 
6.3%
번 666
 
6.3%
별 665
 
6.3%
Other values (157) 3568
33.9%
Uppercase Letter
ValueCountFrequency (%)
B 12
31.6%
L 6
15.8%
G 5
13.2%
A 3
 
7.9%
D 2
 
5.3%
K 2
 
5.3%
S 2
 
5.3%
M 1
 
2.6%
E 1
 
2.6%
V 1
 
2.6%
Other values (3) 3
 
7.9%
Decimal Number
ValueCountFrequency (%)
1 812
23.9%
2 393
11.5%
3 374
11.0%
4 357
10.5%
5 310
 
9.1%
0 305
 
9.0%
6 284
 
8.3%
7 234
 
6.9%
8 167
 
4.9%
9 167
 
4.9%
Other Punctuation
ValueCountFrequency (%)
, 26
86.7%
@ 4
 
13.3%
Lowercase Letter
ValueCountFrequency (%)
e 1
50.0%
b 1
50.0%
Space Separator
ValueCountFrequency (%)
4650
100.0%
Close Punctuation
ValueCountFrequency (%)
) 43
100.0%
Open Punctuation
ValueCountFrequency (%)
( 42
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10526
56.0%
Common 8215
43.7%
Latin 40
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 758
 
7.2%
동 754
 
7.2%
지 747
 
7.1%
마 681
 
6.5%
포 681
 
6.5%
시 671
 
6.4%
울 668
 
6.3%
구 667
 
6.3%
번 666
 
6.3%
별 665
 
6.3%
Other values (157) 3568
33.9%
Common
ValueCountFrequency (%)
4650
56.6%
1 812
 
9.9%
2 393
 
4.8%
3 374
 
4.6%
4 357
 
4.3%
5 310
 
3.8%
0 305
 
3.7%
6 284
 
3.5%
7 234
 
2.8%
8 167
 
2.0%
Other values (7) 329
 
4.0%
Latin
ValueCountFrequency (%)
B 12
30.0%
L 6
15.0%
G 5
12.5%
A 3
 
7.5%
D 2
 
5.0%
K 2
 
5.0%
S 2
 
5.0%
e 1
 
2.5%
b 1
 
2.5%
M 1
 
2.5%
Other values (5) 5
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10526
56.0%
ASCII 8255
44.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4650
56.3%
1 812
 
9.8%
2 393
 
4.8%
3 374
 
4.5%
4 357
 
4.3%
5 310
 
3.8%
0 305
 
3.7%
6 284
 
3.4%
7 234
 
2.8%
8 167
 
2.0%
Other values (22) 369
 
4.5%
Hangul
ValueCountFrequency (%)
서 758
 
7.2%
동 754
 
7.2%
지 747
 
7.1%
마 681
 
6.5%
포 681
 
6.5%
시 671
 
6.4%
울 668
 
6.3%
구 667
 
6.3%
번 666
 
6.3%
별 665
 
6.3%
Other values (157) 3568
33.9%

지도점검일자
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct217
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20174606
Minimum20030719
Maximum50020609
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-05-11T09:13:32.671576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20030719
5-th percentile20040428
Q120061215
median20140729
Q320180720
95-th percentile20210383
Maximum50020609
Range29989890
Interquartile range (IQR)119505

Descriptive statistics

Standard deviation1160646.3
Coefficient of variation (CV)0.05753006
Kurtosis661.49642
Mean20174606
Median Absolute Deviation (MAD)49372
Skewness25.685796
Sum1.3416113 × 1010
Variance1.3470997 × 1012
MonotonicityNot monotonic
2024-05-11T09:13:33.463701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20181002 37
 
5.6%
20140101 37
 
5.6%
20150101 30
 
4.5%
20190101 25
 
3.8%
20200101 20
 
3.0%
20180430 18
 
2.7%
20040406 16
 
2.4%
20211203 14
 
2.1%
20171117 13
 
2.0%
20180720 13
 
2.0%
Other values (207) 442
66.5%
ValueCountFrequency (%)
20030719 1
 
0.2%
20040201 4
 
0.6%
20040202 4
 
0.6%
20040213 4
 
0.6%
20040315 1
 
0.2%
20040406 16
2.4%
20040419 1
 
0.2%
20040428 8
1.2%
20040512 1
 
0.2%
20040614 1
 
0.2%
ValueCountFrequency (%)
50020609 1
 
0.2%
20500504 1
 
0.2%
20231214 1
 
0.2%
20230421 3
 
0.5%
20230111 2
 
0.3%
20220303 1
 
0.2%
20211203 14
2.1%
20211021 1
 
0.2%
20210701 5
 
0.8%
20210602 1
 
0.2%

행정처분상태
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
처분확정
665 

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 665
100.0%

Length

2024-05-11T09:13:34.334183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:13:34.720120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
처분확정 665
100.0%

처분명
Text

Distinct108
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
2024-05-11T09:13:35.459864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length30
Mean length8.9398496
Min length2

Characters and Unicode

Total characters5945
Distinct characters98
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)9.5%

Sample

1st row경고
2nd row개선명령
3rd row과징금부과
4th row개선명령
5th row개선명령(즉시)
ValueCountFrequency (%)
과태료부과 165
 
15.8%
개선명령 147
 
14.1%
경고 76
 
7.3%
과태료 44
 
4.2%
과징금부과 41
 
3.9%
20만원 34
 
3.3%
과태료부과(의견제출기한내 25
 
2.4%
영업소폐쇄 24
 
2.3%
16만원 23
 
2.2%
부과 22
 
2.1%
Other values (124) 442
42.4%
2024-05-11T09:13:37.135991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 746
 
12.5%
부 407
 
6.8%
378
 
6.4%
료 305
 
5.1%
태 292
 
4.9%
0 252
 
4.2%
원 213
 
3.6%
만 189
 
3.2%
2 171
 
2.9%
개 170
 
2.9%
Other values (88) 2822
47.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4488
75.5%
Decimal Number 705
 
11.9%
Space Separator 378
 
6.4%
Open Punctuation 142
 
2.4%
Close Punctuation 142
 
2.4%
Other Punctuation 64
 
1.1%
Dash Punctuation 25
 
0.4%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 746
16.6%
부 407
 
9.1%
료 305
 
6.8%
태 292
 
6.5%
원 213
 
4.7%
만 189
 
4.2%
개 170
 
3.8%
령 167
 
3.7%
명 167
 
3.7%
선 158
 
3.5%
Other values (69) 1674
37.3%
Decimal Number
ValueCountFrequency (%)
0 252
35.7%
2 171
24.3%
1 102
14.5%
6 72
 
10.2%
4 32
 
4.5%
5 29
 
4.1%
3 23
 
3.3%
8 12
 
1.7%
9 7
 
1.0%
7 5
 
0.7%
Other Punctuation
ValueCountFrequency (%)
, 33
51.6%
% 16
25.0%
. 14
21.9%
: 1
 
1.6%
Space Separator
ValueCountFrequency (%)
378
100.0%
Open Punctuation
ValueCountFrequency (%)
( 142
100.0%
Close Punctuation
ValueCountFrequency (%)
) 142
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4488
75.5%
Common 1457
 
24.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 746
16.6%
부 407
 
9.1%
료 305
 
6.8%
태 292
 
6.5%
원 213
 
4.7%
만 189
 
4.2%
개 170
 
3.8%
령 167
 
3.7%
명 167
 
3.7%
선 158
 
3.5%
Other values (69) 1674
37.3%
Common
ValueCountFrequency (%)
378
25.9%
0 252
17.3%
2 171
11.7%
( 142
 
9.7%
) 142
 
9.7%
1 102
 
7.0%
6 72
 
4.9%
, 33
 
2.3%
4 32
 
2.2%
5 29
 
2.0%
Other values (9) 104
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4488
75.5%
ASCII 1457
 
24.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 746
16.6%
부 407
 
9.1%
료 305
 
6.8%
태 292
 
6.5%
원 213
 
4.7%
만 189
 
4.2%
개 170
 
3.8%
령 167
 
3.7%
명 167
 
3.7%
선 158
 
3.5%
Other values (69) 1674
37.3%
ASCII
ValueCountFrequency (%)
378
25.9%
0 252
17.3%
2 171
11.7%
( 142
 
9.7%
) 142
 
9.7%
1 102
 
7.0%
6 72
 
4.9%
, 33
 
2.3%
4 32
 
2.2%
5 29
 
2.0%
Other values (9) 104
 
7.1%

법적근거
Text

Distinct129
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
2024-05-11T09:13:37.755854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length61
Median length55
Mean length14.303759
Min length5

Characters and Unicode

Total characters9512
Distinct characters73
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)9.9%

Sample

1st row공중위생관리법 제4조제7항 및 동법시행규칙 제19조
2nd row공중위생관리법 제10조제2호, 동법시행규칙 제17조제1항, 제2항 및 동법시행규칙 제19조
3rd row공중위생법제11조제1항
4th row공중위생관리법 제4조제7항 및 동법시행규칙 제19조
5th row공중위생관리법 제4조 제7항 동법시행규칙 제7조
ValueCountFrequency (%)
법 310
17.9%
제17조 216
 
12.5%
공중위생관리법 201
 
11.6%
및 101
 
5.8%
동법시행규칙 96
 
5.5%
제19조 50
 
2.9%
제22조제2항제6호 39
 
2.3%
제4조 38
 
2.2%
제2항 28
 
1.6%
제4조제7항 27
 
1.6%
Other values (121) 627
36.2%
2024-05-11T09:13:38.952928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 1192
 
12.5%
1085
 
11.4%
조 800
 
8.4%
법 784
 
8.2%
1 754
 
7.9%
7 398
 
4.2%
위 386
 
4.1%
항 376
 
4.0%
공 352
 
3.7%
생 348
 
3.7%
Other values (63) 3037
31.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6257
65.8%
Decimal Number 1978
 
20.8%
Space Separator 1085
 
11.4%
Other Punctuation 116
 
1.2%
Open Punctuation 38
 
0.4%
Close Punctuation 38
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 1192
19.1%
조 800
12.8%
법 784
12.5%
위 386
 
6.2%
항 376
 
6.0%
공 352
 
5.6%
생 348
 
5.6%
중 326
 
5.2%
관 322
 
5.1%
리 318
 
5.1%
Other values (47) 1053
16.8%
Decimal Number
ValueCountFrequency (%)
1 754
38.1%
7 398
20.1%
2 302
15.3%
4 254
 
12.8%
3 83
 
4.2%
0 65
 
3.3%
9 63
 
3.2%
6 51
 
2.6%
8 8
 
0.4%
Other Punctuation
ValueCountFrequency (%)
, 78
67.2%
. 38
32.8%
Open Punctuation
ValueCountFrequency (%)
[ 19
50.0%
( 19
50.0%
Close Punctuation
ValueCountFrequency (%)
] 19
50.0%
) 19
50.0%
Space Separator
ValueCountFrequency (%)
1085
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6257
65.8%
Common 3255
34.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 1192
19.1%
조 800
12.8%
법 784
12.5%
위 386
 
6.2%
항 376
 
6.0%
공 352
 
5.6%
생 348
 
5.6%
중 326
 
5.2%
관 322
 
5.1%
리 318
 
5.1%
Other values (47) 1053
16.8%
Common
ValueCountFrequency (%)
1085
33.3%
1 754
23.2%
7 398
 
12.2%
2 302
 
9.3%
4 254
 
7.8%
3 83
 
2.5%
, 78
 
2.4%
0 65
 
2.0%
9 63
 
1.9%
6 51
 
1.6%
Other values (6) 122
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6257
65.8%
ASCII 3255
34.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 1192
19.1%
조 800
12.8%
법 784
12.5%
위 386
 
6.2%
항 376
 
6.0%
공 352
 
5.6%
생 348
 
5.6%
중 326
 
5.2%
관 322
 
5.1%
리 318
 
5.1%
Other values (47) 1053
16.8%
ASCII
ValueCountFrequency (%)
1085
33.3%
1 754
23.2%
7 398
 
12.2%
2 302
 
9.3%
4 254
 
7.8%
3 83
 
2.5%
, 78
 
2.4%
0 65
 
2.0%
9 63
 
1.9%
6 51
 
1.6%
Other values (6) 122
 
3.7%

위반일자
Real number (ℝ)

HIGH CORRELATION 

Distinct226
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20127817
Minimum20030719
Maximum20231214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-05-11T09:13:39.603866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20030719
5-th percentile20040406
Q120061018
median20140716
Q320180430
95-th percentile20200714
Maximum20231214
Range200495
Interquartile range (IQR)119412

Descriptive statistics

Standard deviation57435.487
Coefficient of variation (CV)0.0028535378
Kurtosis-1.4103714
Mean20127817
Median Absolute Deviation (MAD)49385
Skewness-0.25524129
Sum1.3384998 × 1010
Variance3.2988352 × 109
MonotonicityNot monotonic
2024-05-11T09:13:40.213860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20190101 37
 
5.6%
20140101 36
 
5.4%
20150101 35
 
5.3%
20181002 23
 
3.5%
20180101 21
 
3.2%
20200101 19
 
2.9%
20160101 18
 
2.7%
20040303 16
 
2.4%
20171207 14
 
2.1%
20130101 13
 
2.0%
Other values (216) 433
65.1%
ValueCountFrequency (%)
20030719 1
 
0.2%
20031204 4
 
0.6%
20040113 4
 
0.6%
20040114 4
 
0.6%
20040218 1
 
0.2%
20040303 16
2.4%
20040323 1
 
0.2%
20040406 7
1.1%
20040419 1
 
0.2%
20040420 1
 
0.2%
ValueCountFrequency (%)
20231214 1
 
0.2%
20230309 2
 
0.3%
20230308 1
 
0.2%
20230111 2
 
0.3%
20220303 1
 
0.2%
20210908 1
 
0.2%
20210701 6
0.9%
20210511 1
 
0.2%
20210423 4
0.6%
20210223 2
 
0.3%

위반내용
Text

Distinct235
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
2024-05-11T09:13:40.849149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length194
Median length60
Mean length15.768421
Min length3

Characters and Unicode

Total characters10486
Distinct characters272
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique151 ?
Unique (%)22.7%

Sample

1st row출입?검사 등의 기록부 미비치
2nd row숙박요금표미게시
3rd row청소년이성혼숙
4th row숙박요금표 미게시
5th row숙박요금표 미게시
ValueCountFrequency (%)
위생교육 173
 
8.4%
미수료 105
 
5.1%
받지 71
 
3.4%
위생교육을 69
 
3.3%
때 64
 
3.1%
아니한 55
 
2.7%
미이수 48
 
2.3%
미게시 46
 
2.2%
미필 36
 
1.7%
위반 29
 
1.4%
Other values (454) 1372
66.3%
2024-05-11T09:13:42.198858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1447
 
13.8%
위 446
 
4.3%
미 405
 
3.9%
생 349
 
3.3%
교 289
 
2.8%
육 285
 
2.7%
2 263
 
2.5%
0 260
 
2.5%
수 235
 
2.2%
1 232
 
2.2%
Other values (262) 6275
59.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7657
73.0%
Space Separator 1447
 
13.8%
Decimal Number 1018
 
9.7%
Other Punctuation 145
 
1.4%
Open Punctuation 109
 
1.0%
Close Punctuation 108
 
1.0%
Uppercase Letter 1
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
위 446
 
5.8%
미 405
 
5.3%
생 349
 
4.6%
교 289
 
3.8%
육 285
 
3.7%
수 235
 
3.1%
년 202
 
2.6%
시 161
 
2.1%
소 160
 
2.1%
업 155
 
2.0%
Other values (238) 4970
64.9%
Decimal Number
ValueCountFrequency (%)
2 263
25.8%
0 260
25.5%
1 232
22.8%
4 72
 
7.1%
3 50
 
4.9%
8 45
 
4.4%
9 32
 
3.1%
5 30
 
2.9%
7 17
 
1.7%
6 17
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 72
49.7%
, 44
30.3%
: 12
 
8.3%
？ 9
 
6.2%
? 5
 
3.4%
* 2
 
1.4%
; 1
 
0.7%
Open Punctuation
ValueCountFrequency (%)
( 103
94.5%
[ 6
 
5.5%
Close Punctuation
ValueCountFrequency (%)
) 102
94.4%
] 6
 
5.6%
Space Separator
ValueCountFrequency (%)
1447
100.0%
Uppercase Letter
ValueCountFrequency (%)
Γ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7657
73.0%
Common 2828
 
27.0%
Greek 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
위 446
 
5.8%
미 405
 
5.3%
생 349
 
4.6%
교 289
 
3.8%
육 285
 
3.7%
수 235
 
3.1%
년 202
 
2.6%
시 161
 
2.1%
소 160
 
2.1%
업 155
 
2.0%
Other values (238) 4970
64.9%
Common
ValueCountFrequency (%)
1447
51.2%
2 263
 
9.3%
0 260
 
9.2%
1 232
 
8.2%
( 103
 
3.6%
) 102
 
3.6%
. 72
 
2.5%
4 72
 
2.5%
3 50
 
1.8%
8 45
 
1.6%
Other values (13) 182
 
6.4%
Greek
ValueCountFrequency (%)
Γ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7657
73.0%
ASCII 2819
 
26.9%
None 10
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1447
51.3%
2 263
 
9.3%
0 260
 
9.2%
1 232
 
8.2%
( 103
 
3.7%
) 102
 
3.6%
. 72
 
2.6%
4 72
 
2.6%
3 50
 
1.8%
8 45
 
1.6%
Other values (12) 173
 
6.1%
Hangul
ValueCountFrequency (%)
위 446
 
5.8%
미 405
 
5.3%
생 349
 
4.6%
교 289
 
3.8%
육 285
 
3.7%
수 235
 
3.1%
년 202
 
2.6%
시 161
 
2.1%
소 160
 
2.1%
업 155
 
2.0%
Other values (238) 4970
64.9%
None
ValueCountFrequency (%)
？ 9
90.0%
Γ 1
 
10.0%

처분내용
Text

Distinct108
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
2024-05-11T09:13:42.829432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length30
Mean length8.9398496
Min length2

Characters and Unicode

Total characters5945
Distinct characters98
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)9.5%

Sample

1st row경고
2nd row개선명령
3rd row과징금부과
4th row개선명령
5th row개선명령(즉시)
ValueCountFrequency (%)
과태료부과 165
 
15.8%
개선명령 147
 
14.1%
경고 76
 
7.3%
과태료 44
 
4.2%
과징금부과 41
 
3.9%
20만원 34
 
3.3%
과태료부과(의견제출기한내 25
 
2.4%
영업소폐쇄 24
 
2.3%
16만원 23
 
2.2%
부과 22
 
2.1%
Other values (124) 442
42.4%
2024-05-11T09:13:44.104218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 746
 
12.5%
부 407
 
6.8%
378
 
6.4%
료 305
 
5.1%
태 292
 
4.9%
0 252
 
4.2%
원 213
 
3.6%
만 189
 
3.2%
2 171
 
2.9%
개 170
 
2.9%
Other values (88) 2822
47.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4488
75.5%
Decimal Number 705
 
11.9%
Space Separator 378
 
6.4%
Open Punctuation 142
 
2.4%
Close Punctuation 142
 
2.4%
Other Punctuation 64
 
1.1%
Dash Punctuation 25
 
0.4%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 746
16.6%
부 407
 
9.1%
료 305
 
6.8%
태 292
 
6.5%
원 213
 
4.7%
만 189
 
4.2%
개 170
 
3.8%
령 167
 
3.7%
명 167
 
3.7%
선 158
 
3.5%
Other values (69) 1674
37.3%
Decimal Number
ValueCountFrequency (%)
0 252
35.7%
2 171
24.3%
1 102
14.5%
6 72
 
10.2%
4 32
 
4.5%
5 29
 
4.1%
3 23
 
3.3%
8 12
 
1.7%
9 7
 
1.0%
7 5
 
0.7%
Other Punctuation
ValueCountFrequency (%)
, 33
51.6%
% 16
25.0%
. 14
21.9%
: 1
 
1.6%
Space Separator
ValueCountFrequency (%)
378
100.0%
Open Punctuation
ValueCountFrequency (%)
( 142
100.0%
Close Punctuation
ValueCountFrequency (%)
) 142
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4488
75.5%
Common 1457
 
24.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 746
16.6%
부 407
 
9.1%
료 305
 
6.8%
태 292
 
6.5%
원 213
 
4.7%
만 189
 
4.2%
개 170
 
3.8%
령 167
 
3.7%
명 167
 
3.7%
선 158
 
3.5%
Other values (69) 1674
37.3%
Common
ValueCountFrequency (%)
378
25.9%
0 252
17.3%
2 171
11.7%
( 142
 
9.7%
) 142
 
9.7%
1 102
 
7.0%
6 72
 
4.9%
, 33
 
2.3%
4 32
 
2.2%
5 29
 
2.0%
Other values (9) 104
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4488
75.5%
ASCII 1457
 
24.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 746
16.6%
부 407
 
9.1%
료 305
 
6.8%
태 292
 
6.5%
원 213
 
4.7%
만 189
 
4.2%
개 170
 
3.8%
령 167
 
3.7%
명 167
 
3.7%
선 158
 
3.5%
Other values (69) 1674
37.3%
ASCII
ValueCountFrequency (%)
378
25.9%
0 252
17.3%
2 171
11.7%
( 142
 
9.7%
) 142
 
9.7%
1 102
 
7.0%
6 72
 
4.9%
, 33
 
2.3%
4 32
 
2.2%
5 29
 
2.0%
Other values (9) 104
 
7.1%

처분기간
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)10.7%
Missing609
Missing (%)91.6%
Infinite0
Infinite (%)0.0%
Mean8.3392857
Minimum0
Maximum25
Zeros11
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-05-11T09:13:44.581703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q325
95-th percentile25
Maximum25
Range25
Interquartile range (IQR)24

Descriptive statistics

Standard deviation11.047463
Coefficient of variation (CV)1.3247493
Kurtosis-1.3100359
Mean8.3392857
Median Absolute Deviation (MAD)1
Skewness0.81230358
Sum467
Variance122.04643
MonotonicityNot monotonic
2024-05-11T09:13:45.051515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 26
 
3.9%
25 16
 
2.4%
0 11
 
1.7%
15 1
 
0.2%
16 1
 
0.2%
10 1
 
0.2%
(Missing) 609
91.6%
ValueCountFrequency (%)
0 11
1.7%
1 26
3.9%
10 1
 
0.2%
15 1
 
0.2%
16 1
 
0.2%
25 16
2.4%
ValueCountFrequency (%)
25 16
2.4%
16 1
 
0.2%
15 1
 
0.2%
10 1
 
0.2%
1 26
3.9%
0 11
1.7%

영업장면적(㎡)
Real number (ℝ)

MISSING  ZEROS 

Distinct337
Distinct (%)54.9%
Missing51
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean367.75832
Minimum0
Maximum11385
Zeros17
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-05-11T09:13:45.676213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.426
Q133
median76.435
Q3211.2
95-th percentile1693
Maximum11385
Range11385
Interquartile range (IQR)178.2

Descriptive statistics

Standard deviation1134.3267
Coefficient of variation (CV)3.0844352
Kurtosis61.831028
Mean367.75832
Median Absolute Deviation (MAD)52.31
Skewness7.1954453
Sum225803.61
Variance1286697.1
MonotonicityNot monotonic
2024-05-11T09:13:46.324283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101.96 19
 
2.9%
0.0 17
 
2.6%
33.0 12
 
1.8%
60.0 8
 
1.2%
3151.18 8
 
1.2%
1693.0 7
 
1.1%
662.0 6
 
0.9%
211.2 6
 
0.9%
252.0 6
 
0.9%
1635.49 6
 
0.9%
Other values (327) 519
78.0%
(Missing) 51
 
7.7%
ValueCountFrequency (%)
0.0 17
2.6%
3.0 1
 
0.2%
3.2 2
 
0.3%
6.06 1
 
0.2%
6.6 1
 
0.2%
8.5 1
 
0.2%
10.0 1
 
0.2%
10.01 2
 
0.3%
10.49 2
 
0.3%
10.8 2
 
0.3%
ValueCountFrequency (%)
11385.0 4
0.6%
8582.0 1
 
0.2%
4932.84 1
 
0.2%
4704.38 1
 
0.2%
3868.0 1
 
0.2%
3151.18 8
1.2%
2772.0 1
 
0.2%
2681.0 5
0.8%
2466.74 1
 
0.2%
2017.38 1
 
0.2%

Interactions

2024-05-11T09:13:18.658385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:12.835810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:14.444303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:15.954526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:17.396476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:18.984283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:13.116112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:14.803266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:16.264282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:17.634658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:19.275652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:13.396538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:15.102112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:16.542448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:17.885525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:19.700048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:13.921000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:15.408850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:16.849659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:18.155895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:19.867962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:14.171475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:15.670698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:17.113624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:13:18.397053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T09:13:46.659542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분일자업종명업태명지도점검일자위반일자처분기간영업장면적(㎡)
처분일자1.0000.0000.0000.7050.0000.0000.000
업종명0.0001.0000.9690.0000.6750.2520.395
업태명0.0000.9691.0000.0000.6640.2320.706
지도점검일자0.7050.0000.0001.0000.0000.0000.000
위반일자0.0000.6750.6640.0001.0000.7940.308
처분기간0.0000.2520.2320.0000.7941.0000.254
영업장면적(㎡)0.0000.3950.7060.0000.3080.2541.000
2024-05-11T09:13:47.028131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명업종명
업태명1.0000.774
업종명0.7741.000
2024-05-11T09:13:47.388111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분일자지도점검일자위반일자처분기간영업장면적(㎡)업종명업태명
처분일자1.0000.9980.976-0.379-0.2560.0000.000
지도점검일자0.9981.0000.978-0.397-0.2470.0000.000
위반일자0.9760.9781.000-0.369-0.2340.3380.324
처분기간-0.379-0.397-0.3691.000-0.1710.1930.082
영업장면적(㎡)-0.256-0.247-0.234-0.1711.0000.1890.407
업종명0.0000.0000.3380.1930.1891.0000.774
업태명0.0000.0000.3240.0820.4070.7741.000

Missing values

2024-05-11T09:13:20.321993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T09:13:21.041873image/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.
2024-05-11T09:13:21.461263image/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(㎡)
03130000200511011숙박업(일반)여인숙업행화서울특별시 마포구 마포대로20길 8-3, (아현동)서울특별시 마포구 아현동 425번지 11호20051028처분확정경고공중위생관리법 제4조제7항 및 동법시행규칙 제19조20051028출입?검사 등의 기록부 미비치경고<NA><NA>
1313000020040406<NA>숙박업(일반)여인숙업행화서울특별시 마포구 마포대로20길 8-3, (아현동)서울특별시 마포구 아현동 425번지 11호20040406처분확정개선명령공중위생관리법 제10조제2호, 동법시행규칙 제17조제1항, 제2항 및 동법시행규칙 제19조20040303숙박요금표미게시개선명령25<NA>
23130000200611271숙박업(일반)여인숙업행화서울특별시 마포구 마포대로20길 8-3, (아현동)서울특별시 마포구 아현동 425번지 11호20060814처분확정과징금부과공중위생법제11조제1항20060605청소년이성혼숙과징금부과<NA><NA>
33130000200505043숙박업(일반)여관업귀빈장여관<NA>서울특별시 마포구 대흥동 12번지 42호20050420처분확정개선명령공중위생관리법 제4조제7항 및 동법시행규칙 제19조20050420숙박요금표 미게시개선명령<NA>200.0
4313000020500504<NA>숙박업(일반)여관업귀빈장여관<NA>서울특별시 마포구 대흥동 12번지 42호20500504처분확정개선명령(즉시)공중위생관리법 제4조 제7항 동법시행규칙 제7조20050504숙박요금표 미게시개선명령(즉시)0200.0
53130000200511012숙박업(일반)여인숙업한성여인숙서울특별시 마포구 만리재옛8길 4-1, (공덕동)서울특별시 마포구 공덕동 22번지 34호20051028처분확정경고공중위생관리법 제4조제7항 및 동법시행규칙 제19조20051028출입검사기록부 미비치경고<NA>0.0
63130000200505045숙박업(일반)여관업동성여관<NA>서울특별시 마포구 신공덕동 25번지 24호20050425처분확정경고공중위생관리법 제4조제7항 및 동법시행규칙 제19조20050425출입?검사 등의 기록부 미비치경고<NA>149.0
7313000020050504<NA>숙박업(일반)여관업동성여관<NA>서울특별시 마포구 신공덕동 25번지 24호20050504처분확정경고공중위생관리법 제4조 제7항 동법시행규칙 제7조20050504출입검사 등 기록부 미비치경고0149.0
8313000020040428<NA>숙박업(일반)여관업경보서울특별시 마포구 새창로6가길 4, (도화동)서울특별시 마포구 도화동 4번지 94호20040428처분확정영업장폐쇄명령공중위생관리법 제11조제1항 및 동법시행규칙 제19조20040406신고를 하지아니하고 영업소 소재지를 변경(공중위생관리법제3조제1항위반)영업장폐쇄명령166.0
9313000020040406<NA>숙박업(일반)여인숙업대원여관<NA>서울특별시 마포구 아현동 463번지 92호20040406처분확정개선명령공중위생관리법 제10조제2호, 동법시행규칙 제17조제1항, 제2항 및 동법시행규칙 제19조20040303숙박요금미게시개선명령2575.0
시군구코드처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자행정처분상태처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)
6553130000202001072020-00002화장ㆍ분장 미용업메이크업업은 뷰티<NA>서울특별시 마포구 동교동 165번지 8호 LG팰리스빌딩 지하1층-11020191215처분확정과태료50만원 부과법 제11조제1항제4호20190613의료법 위반으로 눈썹문신 등 의료행위를 한 경우과태료50만원 부과<NA>24.57
6563130000202111022019-00002화장ㆍ분장 미용업메이크업업더 스튜디오 터칭(The Studio Touching)서울특별시 마포구 와우산로 162-14, 지층일부 (서교동)서울특별시 마포구 서교동 325번지 7호 지층일부20210701처분확정과태료 30만원(자진납부완료 : 24만원)법 제22조제2항제6호202107012020년도 공중위생업소 위생교육 미 수료과태료 30만원(자진납부완료 : 24만원)<NA>50.54
6573130000202008212019-00006화장ㆍ분장 미용업메이크업업햇님언니서울특별시 마포구 잔다리로6길 20-12, 지층 (서교동)서울특별시 마포구 서교동 368번지 14호 햇님언니(큰)내부의 샵인샵20200101처분확정과태료20만원 부과(의견제출기한내 미납)법 제22조제2항제6호202001012019년 위생교육 미수료과태료20만원 부과(의견제출기한내 미납)<NA>3.2
6583130000202111252019-00006화장ㆍ분장 미용업메이크업업햇님언니서울특별시 마포구 잔다리로6길 20-12, 지층 (서교동)서울특별시 마포구 서교동 368번지 14호 햇님언니(큰)내부의 샵인샵20210701처분확정과태료 30만원법 제22조제2항제6호202107012020년도 공중위생업소 위생교육 미 수료과태료 30만원<NA>3.2
6593130000202112302019-00009화장ㆍ분장 미용업메이크업업임유눈썹가게서울특별시 마포구 망원로7길 31-18, 1층일부 (망원동)서울특별시 마포구 망원동 425번지 18호20211203처분확정영업소폐쇄법 제11조제3항제2호202005262020. 5.26. 사업자등록폐업에 따른 영업소 폐쇄(직권말소)영업소폐쇄<NA>25.0
6603130000202007312019-00017화장ㆍ분장 미용업메이크업업데씨네서울특별시 마포구 독막로19길 7, 홍현빌딩 2층 (상수동)서울특별시 마포구 상수동 93번지 6호 홍현빌딩20191231처분확정과태료부과16만원법 제22조제2항제6호20191231위생교육 미수료과태료부과16만원<NA>42.99
6613130000201912172016-7일반미용업, 화장ㆍ분장 미용업일반미용업투페이스 스튜디오서울특별시 마포구 어울마당로5길 26, 지층 (서교동)서울특별시 마포구 서교동 398번지 11호20190101처분확정과태료부과(의견제출기한내 납부20%감경-16만원부과)법 제17조201901012018위생교육 미수료과태료부과(의견제출기한내 납부20%감경-16만원부과)<NA>175.2
6623130000202008212018-15일반미용업, 화장ㆍ분장 미용업메이크업업더끌레르 뷰티스토리서울특별시 마포구 동교로 175, 3층 (동교동)서울특별시 마포구 동교동 201번지 32호 3층20200101처분확정과태료 20만원 부과(의견제출기한내 미납)법 제22조제2항제6호202001012019년 위생교육 미수료과태료 20만원 부과(의견제출기한내 미납)<NA>19.8
6633130000201811192017-1피부미용업, 네일미용업, 화장ㆍ분장 미용업네일아트업MYJJU Nail(마이쮸네일)서울특별시 마포구 양화로23길 10-8, 1층 (동교동)서울특별시 마포구 동교동 147번지 50호20181002처분확정과태료부과법 제17조20181002위생교육을 받지 아니한때과태료부과<NA>39.0
6643130000201912172017-1피부미용업, 네일미용업, 화장ㆍ분장 미용업네일아트업MYJJU Nail(마이쮸네일)서울특별시 마포구 양화로23길 10-8, 1층 (동교동)서울특별시 마포구 동교동 147번지 50호20190101처분확정과태료부과(의견제출기한내 미납-20만원부과)법 제17조201901012018위생교육 미수료과태료부과(의견제출기한내 미납-20만원부과)<NA>39.0

Duplicate rows

Most frequently occurring

시군구코드처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자행정처분상태처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)# duplicates
83130000201410132013-00042피부미용업피부미용업비타 C 피부비만서울특별시 마포구 양화로12길 6, (서교동, 4층)서울특별시 마포구 서교동 395번지 152호 4층20140806처분확정개선명령공중위생관리법 제4조 및 제10조, 동법시행규칙 제7조[별표4]4.아.20140806옥외가격표시 미게시개선명령<NA>11385.04
133130000201804302014-2종합미용업피부미용업뷰티프렌즈서울특별시 마포구 홍익로6길 38, 4층 (동교동)서울특별시 마포구 동교동 164번지 17호 4층20180430처분확정과징금부과(영업정지2개월 갈음)법 제4조제4항20170222법 제4조제4항제1호(공중위생영업자의 위생관리의무 등 위반) 눈썹문신 시술 등(유사한 의료행위)과징금부과(영업정지2개월 갈음)<NA>101.963
153130000201804302014-2종합미용업피부미용업뷰티프렌즈서울특별시 마포구 홍익로6길 38, 4층 (동교동)서울특별시 마포구 동교동 164번지 17호 4층20180430처분확정과태료부과(기한내사전납부40만원)법 제4조제4항20170222법 제4조제4항제1호(공중위생영업자의 위생관리의무 등 위반) 눈썹문신 시술 등(유사한 의료행위)과태료부과(기한내사전납부40만원)<NA>101.963
193130000201811192016-30네일미용업네일아트업비비드글램(VIVIDGLAM)서울특별시 마포구 월드컵로13길 34, 1층 (망원동, 두드림빌딩)서울특별시 마포구 망원동 375번지 8호 1층20181002처분확정과태료부과법 제17조20181002위생교육을 받지 아니한때과태료부과<NA>42.973
0313000020040201<NA>목욕장업공동탕업+찜질시설서비스영업월드컵24시보석불가마사우나서울특별시 마포구 월드컵로 240, (성산동,월드컵경기장)서울특별시 마포구 성산동 515번지 월드컵경기장20040201처분확정경고공중위생관리법 제17조 제1항 및 동법시행규칙 제23조2항 위반, 공중위생관리법 제2항 제6호 적용20040114위생교육미필경고13151.182
1313000020040201<NA>목욕장업공동탕업+찜질시설서비스영업월드컵24시보석불가마사우나서울특별시 마포구 월드컵로 240, (성산동,월드컵경기장)서울특별시 마포구 성산동 515번지 월드컵경기장20040201처분확정과태료 300,000원 부과공중위생관리법 제17조 제1항 및 동법시행규칙 제23조2항 위반, 공중위생관리법 제2항 제6호 적용20040114위생교육미필과태료 300,000원 부과13151.182
2313000020040202<NA>목욕장업공동탕업필사우나서울특별시 마포구 월드컵로32길 55, (성산동)서울특별시 마포구 성산동 277번지 41호20040202처분확정경고공중위생관리법 제17조 및 동법시행규칙 제23조2항 위반, 공중위생관리법 제2항 제6호 적용20040113위생교육 미필경고1332.02
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