Overview

Dataset statistics

Number of variables17
Number of observations557
Missing cells1022
Missing cells (%)10.8%
Duplicate rows28
Duplicate rows (%)5.0%
Total size in memory77.4 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(㎡)
Author중구
URLhttps://data.seoul.go.kr/dataList/OA-10220/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
행정처분상태 has constant value ""Constant
Dataset has 28 (5.0%) 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
처분기간 is highly overall correlated with 업종명High correlation
업종명 is highly overall correlated with 처분기간 and 1 other fieldsHigh correlation
업태명 is highly overall correlated with 업종명High correlation
소재지도로명 has 377 (67.7%) missing valuesMissing
처분기간 has 534 (95.9%) missing valuesMissing
영업장면적(㎡) has 108 (19.4%) missing valuesMissing
영업장면적(㎡) has 6 (1.1%) zerosZeros

Reproduction

Analysis started2024-05-11 08:01:38.928576
Analysis finished2024-05-11 08:01:52.815810
Duration13.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
3010000
557 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3010000
2nd row3010000
3rd row3010000
4th row3010000
5th row3010000

Common Values

ValueCountFrequency (%)
3010000 557
100.0%

Length

2024-05-11T08:01:52.994508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:01:53.324639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3010000 557
100.0%

처분일자
Real number (ℝ)

HIGH CORRELATION 

Distinct251
Distinct (%)45.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20098540
Minimum20030108
Maximum20200728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-05-11T08:01:53.734040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20030108
5-th percentile20030907
Q120060515
median20090731
Q320130612
95-th percentile20190214
Maximum20200728
Range170620
Interquartile range (IQR)70097

Descriptive statistics

Standard deviation49463.201
Coefficient of variation (CV)0.0024610345
Kurtosis-0.96320667
Mean20098540
Median Absolute Deviation (MAD)30428
Skewness0.38274546
Sum1.1194887 × 1010
Variance2.4466082 × 109
MonotonicityNot monotonic
2024-05-11T08:01:54.375717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20120412 25
 
4.5%
20110914 19
 
3.4%
20171218 14
 
2.5%
20070226 14
 
2.5%
20080305 14
 
2.5%
20070319 12
 
2.2%
20070116 8
 
1.4%
20051118 7
 
1.3%
20190211 6
 
1.1%
20190214 6
 
1.1%
Other values (241) 432
77.6%
ValueCountFrequency (%)
20030108 3
0.5%
20030128 3
0.5%
20030206 1
 
0.2%
20030219 1
 
0.2%
20030310 3
0.5%
20030312 1
 
0.2%
20030324 1
 
0.2%
20030404 1
 
0.2%
20030422 4
0.7%
20030425 1
 
0.2%
ValueCountFrequency (%)
20200728 1
 
0.2%
20200717 1
 
0.2%
20200218 1
 
0.2%
20191121 1
 
0.2%
20190730 1
 
0.2%
20190715 1
 
0.2%
20190710 4
0.7%
20190618 1
 
0.2%
20190514 1
 
0.2%
20190423 1
 
0.2%

교부번호
Text

Distinct261
Distinct (%)46.9%
Missing1
Missing (%)0.2%
Memory size4.5 KiB
2024-05-11T08:01:55.077719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length3.0503597
Min length2

Characters and Unicode

Total characters1696
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

Unique135 ?
Unique (%)24.3%

Sample

1st row001
2nd row001
3rd row004
4th row005
5th row005
ValueCountFrequency (%)
084 14
 
2.5%
811 12
 
2.2%
071 11
 
2.0%
053 11
 
2.0%
248 9
 
1.6%
056 8
 
1.4%
057 7
 
1.3%
031 7
 
1.3%
006 7
 
1.3%
246 6
 
1.1%
Other values (251) 464
83.5%
2024-05-11T08:01:56.536117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 327
19.3%
1 299
17.6%
2 206
12.1%
3 141
8.3%
6 128
 
7.5%
5 126
 
7.4%
4 122
 
7.2%
7 122
 
7.2%
8 119
 
7.0%
9 100
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1690
99.6%
Dash Punctuation 6
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 327
19.3%
1 299
17.7%
2 206
12.2%
3 141
8.3%
6 128
 
7.6%
5 126
 
7.5%
4 122
 
7.2%
7 122
 
7.2%
8 119
 
7.0%
9 100
 
5.9%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1696
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 327
19.3%
1 299
17.6%
2 206
12.1%
3 141
8.3%
6 128
 
7.5%
5 126
 
7.4%
4 122
 
7.2%
7 122
 
7.2%
8 119
 
7.0%
9 100
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 327
19.3%
1 299
17.6%
2 206
12.1%
3 141
8.3%
6 128
 
7.5%
5 126
 
7.4%
4 122
 
7.2%
7 122
 
7.2%
8 119
 
7.0%
9 100
 
5.9%

업종명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
숙박업(일반)
209 
이용업
124 
목욕장업
75 
위생관리용역업
46 
미용업
33 
Other values (6)
70 

Length

Max length12
Median length7
Mean length5.2064632
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
숙박업(일반) 209
37.5%
이용업 124
22.3%
목욕장업 75
 
13.5%
위생관리용역업 46
 
8.3%
미용업 33
 
5.9%
일반미용업 31
 
5.6%
피부미용업 20
 
3.6%
세탁업 11
 
2.0%
종합미용업 5
 
0.9%
피부미용업, 네일미용업 2
 
0.4%

Length

2024-05-11T08:01:57.042712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
숙박업(일반 209
37.4%
이용업 124
22.2%
목욕장업 75
 
13.4%
위생관리용역업 46
 
8.2%
미용업 33
 
5.9%
일반미용업 31
 
5.5%
피부미용업 22
 
3.9%
세탁업 11
 
2.0%
종합미용업 5
 
0.9%
네일미용업 2
 
0.4%

업태명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
여관업
155 
일반이용업
124 
일반미용업
64 
공동탕업
62 
위생관리용역업
46 
Other values (11)
106 

Length

Max length14
Median length7
Mean length4.4398564
Min length2

Unique

Unique3 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
여관업 155
27.8%
일반이용업 124
22.3%
일반미용업 64
11.5%
공동탕업 62
 
11.1%
위생관리용역업 46
 
8.3%
관광호텔 30
 
5.4%
피부미용업 25
 
4.5%
일반호텔 21
 
3.8%
일반세탁업 11
 
2.0%
한증막업 8
 
1.4%
Other values (6) 11
 
2.0%

Length

2024-05-11T08:01:57.467062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
여관업 155
27.7%
일반이용업 124
22.2%
일반미용업 64
11.4%
공동탕업 62
 
11.1%
위생관리용역업 46
 
8.2%
관광호텔 30
 
5.4%
피부미용업 25
 
4.5%
일반호텔 21
 
3.8%
일반세탁업 11
 
2.0%
한증막업 8
 
1.4%
Other values (6) 13
 
2.3%

업소명
Text

Distinct337
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2024-05-11T08:01:58.132377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length19
Mean length5.481149
Min length1

Characters and Unicode

Total characters3053
Distinct characters340
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

Unique235 ?
Unique (%)42.2%

Sample

1st row궁전여관
2nd row궁전여관
3rd row화성여관
4th row경남여관
5th row경남여관
ValueCountFrequency (%)
준오헤어명동4호점 12
 
1.9%
실로암목욕탕 11
 
1.7%
호텔 11
 
1.7%
하트여관 9
 
1.4%
진양건강찜질사우나 9
 
1.4%
이화장여관 8
 
1.2%
미용실 8
 
1.2%
미도 8
 
1.2%
코리아목욕탕 6
 
0.9%
중앙 6
 
0.9%
Other values (372) 559
86.4%
2024-05-11T08:01:59.284133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
관 121
 
4.0%
여 114
 
3.7%
90
 
2.9%
스 86
 
2.8%
텔 84
 
2.8%
이 75
 
2.5%
동 75
 
2.5%
호 66
 
2.2%
( 55
 
1.8%
) 55
 
1.8%
Other values (330) 2232
73.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2729
89.4%
Space Separator 90
 
2.9%
Lowercase Letter 59
 
1.9%
Open Punctuation 56
 
1.8%
Close Punctuation 56
 
1.8%
Uppercase Letter 42
 
1.4%
Decimal Number 17
 
0.6%
Other Punctuation 3
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 121
 
4.4%
여 114
 
4.2%
스 86
 
3.2%
텔 84
 
3.1%
이 75
 
2.7%
동 75
 
2.7%
호 66
 
2.4%
미 53
 
1.9%
장 51
 
1.9%
주 51
 
1.9%
Other values (284) 1953
71.6%
Lowercase Letter
ValueCountFrequency (%)
e 7
11.9%
n 7
11.9%
a 6
10.2%
l 5
8.5%
s 5
8.5%
o 5
8.5%
t 5
8.5%
r 4
 
6.8%
i 2
 
3.4%
y 2
 
3.4%
Other values (8) 11
18.6%
Uppercase Letter
ValueCountFrequency (%)
S 6
14.3%
N 6
14.3%
F 5
11.9%
C 4
9.5%
I 3
7.1%
G 3
7.1%
M 3
7.1%
E 2
 
4.8%
K 2
 
4.8%
P 2
 
4.8%
Other values (6) 6
14.3%
Decimal Number
ValueCountFrequency (%)
4 13
76.5%
2 2
 
11.8%
8 1
 
5.9%
7 1
 
5.9%
Open Punctuation
ValueCountFrequency (%)
( 55
98.2%
[ 1
 
1.8%
Close Punctuation
ValueCountFrequency (%)
) 55
98.2%
] 1
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 2
66.7%
& 1
33.3%
Space Separator
ValueCountFrequency (%)
90
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2729
89.4%
Common 223
 
7.3%
Latin 101
 
3.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 121
 
4.4%
여 114
 
4.2%
스 86
 
3.2%
텔 84
 
3.1%
이 75
 
2.7%
동 75
 
2.7%
호 66
 
2.4%
미 53
 
1.9%
장 51
 
1.9%
주 51
 
1.9%
Other values (284) 1953
71.6%
Latin
ValueCountFrequency (%)
e 7
 
6.9%
n 7
 
6.9%
S 6
 
5.9%
a 6
 
5.9%
N 6
 
5.9%
l 5
 
5.0%
F 5
 
5.0%
s 5
 
5.0%
o 5
 
5.0%
t 5
 
5.0%
Other values (24) 44
43.6%
Common
ValueCountFrequency (%)
90
40.4%
( 55
24.7%
) 55
24.7%
4 13
 
5.8%
2 2
 
0.9%
. 2
 
0.9%
[ 1
 
0.4%
] 1
 
0.4%
8 1
 
0.4%
- 1
 
0.4%
Other values (2) 2
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2729
89.4%
ASCII 324
 
10.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
관 121
 
4.4%
여 114
 
4.2%
스 86
 
3.2%
텔 84
 
3.1%
이 75
 
2.7%
동 75
 
2.7%
호 66
 
2.4%
미 53
 
1.9%
장 51
 
1.9%
주 51
 
1.9%
Other values (284) 1953
71.6%
ASCII
ValueCountFrequency (%)
90
27.8%
( 55
17.0%
) 55
17.0%
4 13
 
4.0%
e 7
 
2.2%
n 7
 
2.2%
S 6
 
1.9%
a 6
 
1.9%
N 6
 
1.9%
l 5
 
1.5%
Other values (36) 74
22.8%

소재지도로명
Text

MISSING 

Distinct127
Distinct (%)70.6%
Missing377
Missing (%)67.7%
Memory size4.5 KiB
2024-05-11T08:02:00.045241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length41.5
Mean length29.283333
Min length21

Characters and Unicode

Total characters5271
Distinct characters161
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

Unique101 ?
Unique (%)56.1%

Sample

1st row서울특별시 중구 퇴계로8길 2-1, (회현동1가)
2nd row서울특별시 중구 퇴계로10길 20-3, (회현동1가)
3rd row서울특별시 중구 퇴계로10길 36, (회현동1가)
4th row서울특별시 중구 명동8나길 10, (충무로1가)
5th row서울특별시 중구 퇴계로28길 12, (남학동)
ValueCountFrequency (%)
서울특별시 180
 
18.0%
중구 180
 
18.0%
퇴계로 22
 
2.2%
명동2가 20
 
2.0%
4층 19
 
1.9%
회현동1가 19
 
1.9%
명동8길 14
 
1.4%
신당동 14
 
1.4%
14 13
 
1.3%
황학동 12
 
1.2%
Other values (283) 509
50.8%
2024-05-11T08:02:01.404115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
823
 
15.6%
, 264
 
5.0%
1 216
 
4.1%
중 192
 
3.6%
로 190
 
3.6%
서 189
 
3.6%
( 188
 
3.6%
) 188
 
3.6%
동 186
 
3.5%
시 185
 
3.5%
Other values (151) 2650
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2861
54.3%
Decimal Number 888
 
16.8%
Space Separator 823
 
15.6%
Other Punctuation 264
 
5.0%
Open Punctuation 188
 
3.6%
Close Punctuation 188
 
3.6%
Dash Punctuation 36
 
0.7%
Math Symbol 13
 
0.2%
Uppercase Letter 10
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 192
 
6.7%
로 190
 
6.6%
서 189
 
6.6%
동 186
 
6.5%
시 185
 
6.5%
별 181
 
6.3%
구 181
 
6.3%
울 181
 
6.3%
특 180
 
6.3%
가 116
 
4.1%
Other values (133) 1080
37.7%
Decimal Number
ValueCountFrequency (%)
1 216
24.3%
2 178
20.0%
4 105
11.8%
3 76
 
8.6%
8 68
 
7.7%
0 65
 
7.3%
5 57
 
6.4%
6 52
 
5.9%
7 42
 
4.7%
9 29
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
B 9
90.0%
D 1
 
10.0%
Space Separator
ValueCountFrequency (%)
823
100.0%
Other Punctuation
ValueCountFrequency (%)
, 264
100.0%
Open Punctuation
ValueCountFrequency (%)
( 188
100.0%
Close Punctuation
ValueCountFrequency (%)
) 188
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%
Math Symbol
ValueCountFrequency (%)
~ 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2861
54.3%
Common 2400
45.5%
Latin 10
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 192
 
6.7%
로 190
 
6.6%
서 189
 
6.6%
동 186
 
6.5%
시 185
 
6.5%
별 181
 
6.3%
구 181
 
6.3%
울 181
 
6.3%
특 180
 
6.3%
가 116
 
4.1%
Other values (133) 1080
37.7%
Common
ValueCountFrequency (%)
823
34.3%
, 264
 
11.0%
1 216
 
9.0%
( 188
 
7.8%
) 188
 
7.8%
2 178
 
7.4%
4 105
 
4.4%
3 76
 
3.2%
8 68
 
2.8%
0 65
 
2.7%
Other values (6) 229
 
9.5%
Latin
ValueCountFrequency (%)
B 9
90.0%
D 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2861
54.3%
ASCII 2410
45.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
823
34.1%
, 264
 
11.0%
1 216
 
9.0%
( 188
 
7.8%
) 188
 
7.8%
2 178
 
7.4%
4 105
 
4.4%
3 76
 
3.2%
8 68
 
2.8%
0 65
 
2.7%
Other values (8) 239
 
9.9%
Hangul
ValueCountFrequency (%)
중 192
 
6.7%
로 190
 
6.6%
서 189
 
6.6%
동 186
 
6.5%
시 185
 
6.5%
별 181
 
6.3%
구 181
 
6.3%
울 181
 
6.3%
특 180
 
6.3%
가 116
 
4.1%
Other values (133) 1080
37.7%

소재지지번
Text

Distinct329
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2024-05-11T08:02:02.131452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length43
Mean length26.719928
Min length19

Characters and Unicode

Total characters14883
Distinct characters180
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

Unique222 ?
Unique (%)39.9%

Sample

1st row서울특별시 중구 남대문로5가 14번지 5호
2nd row서울특별시 중구 남대문로5가 14번지 5호
3rd row서울특별시 중구 산림동 84번지 4호
4th row서울특별시 중구 회현동1가 185번지 2호
5th row서울특별시 중구 회현동1가 185번지 2호
ValueCountFrequency (%)
서울특별시 557
 
18.6%
중구 557
 
18.6%
1호 98
 
3.3%
신당동 67
 
2.2%
회현동1가 62
 
2.1%
명동2가 47
 
1.6%
2호 45
 
1.5%
0호 44
 
1.5%
3호 40
 
1.3%
중림동 33
 
1.1%
Other values (424) 1449
48.3%
2024-05-11T08:02:03.450660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3937
26.5%
1 718
 
4.8%
지 648
 
4.4%
중 590
 
4.0%
서 567
 
3.8%
시 563
 
3.8%
별 560
 
3.8%
울 558
 
3.7%
번 557
 
3.7%
특 557
 
3.7%
Other values (170) 5628
37.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8150
54.8%
Space Separator 3937
26.5%
Decimal Number 2683
 
18.0%
Other Punctuation 26
 
0.2%
Close Punctuation 24
 
0.2%
Open Punctuation 24
 
0.2%
Uppercase Letter 18
 
0.1%
Math Symbol 14
 
0.1%
Dash Punctuation 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 648
 
8.0%
중 590
 
7.2%
서 567
 
7.0%
시 563
 
6.9%
별 560
 
6.9%
울 558
 
6.8%
번 557
 
6.8%
특 557
 
6.8%
구 557
 
6.8%
호 537
 
6.6%
Other values (150) 2456
30.1%
Decimal Number
ValueCountFrequency (%)
1 718
26.8%
2 431
16.1%
3 301
11.2%
4 233
 
8.7%
5 219
 
8.2%
0 211
 
7.9%
7 162
 
6.0%
8 160
 
6.0%
6 136
 
5.1%
9 112
 
4.2%
Other Punctuation
ValueCountFrequency (%)
, 23
88.5%
. 2
 
7.7%
/ 1
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
B 14
77.8%
D 4
 
22.2%
Space Separator
ValueCountFrequency (%)
3937
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Math Symbol
ValueCountFrequency (%)
~ 14
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8150
54.8%
Common 6715
45.1%
Latin 18
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 648
 
8.0%
중 590
 
7.2%
서 567
 
7.0%
시 563
 
6.9%
별 560
 
6.9%
울 558
 
6.8%
번 557
 
6.8%
특 557
 
6.8%
구 557
 
6.8%
호 537
 
6.6%
Other values (150) 2456
30.1%
Common
ValueCountFrequency (%)
3937
58.6%
1 718
 
10.7%
2 431
 
6.4%
3 301
 
4.5%
4 233
 
3.5%
5 219
 
3.3%
0 211
 
3.1%
7 162
 
2.4%
8 160
 
2.4%
6 136
 
2.0%
Other values (8) 207
 
3.1%
Latin
ValueCountFrequency (%)
B 14
77.8%
D 4
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8150
54.8%
ASCII 6733
45.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3937
58.5%
1 718
 
10.7%
2 431
 
6.4%
3 301
 
4.5%
4 233
 
3.5%
5 219
 
3.3%
0 211
 
3.1%
7 162
 
2.4%
8 160
 
2.4%
6 136
 
2.0%
Other values (10) 225
 
3.3%
Hangul
ValueCountFrequency (%)
지 648
 
8.0%
중 590
 
7.2%
서 567
 
7.0%
시 563
 
6.9%
별 560
 
6.9%
울 558
 
6.8%
번 557
 
6.8%
특 557
 
6.8%
구 557
 
6.8%
호 537
 
6.6%
Other values (150) 2456
30.1%

지도점검일자
Real number (ℝ)

HIGH CORRELATION 

Distinct300
Distinct (%)53.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20096420
Minimum20021210
Maximum20200716
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-05-11T08:02:03.851543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20021210
5-th percentile20030728
Q120060327
median20090709
Q320130312
95-th percentile20190123
Maximum20200716
Range179506
Interquartile range (IQR)69985

Descriptive statistics

Standard deviation49571.6
Coefficient of variation (CV)0.0024666881
Kurtosis-0.92630383
Mean20096420
Median Absolute Deviation (MAD)30382
Skewness0.42920275
Sum1.1193706 × 1010
Variance2.4573436 × 109
MonotonicityNot monotonic
2024-05-11T08:02:04.376885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20120102 25
 
4.5%
20110101 19
 
3.4%
20061228 14
 
2.5%
20171204 12
 
2.2%
20060327 9
 
1.6%
20081105 8
 
1.4%
20120323 6
 
1.1%
20190111 6
 
1.1%
20091125 6
 
1.1%
20040428 5
 
0.9%
Other values (290) 447
80.3%
ValueCountFrequency (%)
20021210 2
0.4%
20021213 1
0.2%
20030101 2
0.4%
20030114 2
0.4%
20030128 1
0.2%
20030201 2
0.4%
20030207 1
0.2%
20030213 1
0.2%
20030220 2
0.4%
20030303 1
0.2%
ValueCountFrequency (%)
20200716 1
 
0.2%
20200706 1
 
0.2%
20200115 1
 
0.2%
20190715 4
0.7%
20190711 1
 
0.2%
20190621 1
 
0.2%
20190619 1
 
0.2%
20190522 1
 
0.2%
20190428 1
 
0.2%
20190425 1
 
0.2%

행정처분상태
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
처분확정
557 

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 557
100.0%

Length

2024-05-11T08:02:04.821608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:02:05.140576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
처분확정 557
100.0%

처분명
Text

Distinct127
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2024-05-11T08:02:05.642380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length45
Mean length8.8850987
Min length2

Characters and Unicode

Total characters4949
Distinct characters98
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)15.8%

Sample

1st row개선명령
2nd row개선명령 및 과태료부과
3rd row영업정지
4th row영업정지
5th row영업정지2월(영업정지기간:2007.1.10~2007.3.9)
ValueCountFrequency (%)
개선명령 157
21.8%
영업정지 104
14.5%
경고 69
 
9.6%
과태료부과 28
 
3.9%
영업소폐쇄 25
 
3.5%
경고및과태료부과 20
 
2.8%
과태료 17
 
2.4%
경감 15
 
2.1%
과태료부과(자진납부에 15
 
2.1%
의한 14
 
1.9%
Other values (150) 255
35.5%
2024-05-11T08:02:06.679599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 328
 
6.6%
과 314
 
6.3%
2 225
 
4.5%
개 193
 
3.9%
. 188
 
3.8%
업 186
 
3.8%
선 185
 
3.7%
령 185
 
3.7%
명 185
 
3.7%
영 184
 
3.7%
Other values (88) 2776
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3116
63.0%
Decimal Number 1053
 
21.3%
Other Punctuation 277
 
5.6%
Space Separator 164
 
3.3%
Open Punctuation 146
 
3.0%
Close Punctuation 145
 
2.9%
Math Symbol 45
 
0.9%
Dash Punctuation 2
 
< 0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 314
 
10.1%
개 193
 
6.2%
업 186
 
6.0%
선 185
 
5.9%
령 185
 
5.9%
명 185
 
5.9%
영 184
 
5.9%
지 171
 
5.5%
정 161
 
5.2%
부 147
 
4.7%
Other values (66) 1205
38.7%
Decimal Number
ValueCountFrequency (%)
0 328
31.1%
2 225
21.4%
1 179
17.0%
3 84
 
8.0%
9 57
 
5.4%
8 47
 
4.5%
7 40
 
3.8%
4 38
 
3.6%
5 32
 
3.0%
6 23
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 188
67.9%
, 67
 
24.2%
: 20
 
7.2%
/ 1
 
0.4%
* 1
 
0.4%
Math Symbol
ValueCountFrequency (%)
~ 44
97.8%
= 1
 
2.2%
Space Separator
ValueCountFrequency (%)
164
100.0%
Open Punctuation
ValueCountFrequency (%)
( 146
100.0%
Close Punctuation
ValueCountFrequency (%)
) 145
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3116
63.0%
Common 1833
37.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 314
 
10.1%
개 193
 
6.2%
업 186
 
6.0%
선 185
 
5.9%
령 185
 
5.9%
명 185
 
5.9%
영 184
 
5.9%
지 171
 
5.5%
정 161
 
5.2%
부 147
 
4.7%
Other values (66) 1205
38.7%
Common
ValueCountFrequency (%)
0 328
17.9%
2 225
12.3%
. 188
10.3%
1 179
9.8%
164
8.9%
( 146
8.0%
) 145
7.9%
3 84
 
4.6%
, 67
 
3.7%
9 57
 
3.1%
Other values (12) 250
13.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3116
63.0%
ASCII 1833
37.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 328
17.9%
2 225
12.3%
. 188
10.3%
1 179
9.8%
164
8.9%
( 146
8.0%
) 145
7.9%
3 84
 
4.6%
, 67
 
3.7%
9 57
 
3.1%
Other values (12) 250
13.6%
Hangul
ValueCountFrequency (%)
과 314
 
10.1%
개 193
 
6.2%
업 186
 
6.0%
선 185
 
5.9%
령 185
 
5.9%
명 185
 
5.9%
영 184
 
5.9%
지 171
 
5.5%
정 161
 
5.2%
부 147
 
4.7%
Other values (66) 1205
38.7%

법적근거
Text

Distinct121
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2024-05-11T08:02:07.267665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length37
Mean length14.578097
Min length5

Characters and Unicode

Total characters8120
Distinct characters68
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

Unique65 ?
Unique (%)11.7%

Sample

1st row공중위생관리법 제 10조
2nd row공중위생관리법제10조
3rd row공중위생관리법 제11조 1항
4th row공중위생관리법제11조제1항
5th row공중위생관리법 제11조제1항 및 같은법시행규칙 제19조(행정처분기준)
ValueCountFrequency (%)
공중위생관리법 303
21.8%
법 120
 
8.6%
제4조제7항 65
 
4.7%
제11조제1항 52
 
3.7%
및 48
 
3.5%
제3조제1항 45
 
3.2%
제1항 45
 
3.2%
제11조 43
 
3.1%
제3조 42
 
3.0%
제17조 33
 
2.4%
Other values (104) 595
42.8%
2024-05-11T08:02:08.288486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 978
12.0%
835
 
10.3%
1 679
 
8.4%
법 616
 
7.6%
조 612
 
7.5%
위 429
 
5.3%
관 420
 
5.2%
중 403
 
5.0%
생 403
 
5.0%
공 402
 
5.0%
Other values (58) 2343
28.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5828
71.8%
Decimal Number 1354
 
16.7%
Space Separator 835
 
10.3%
Other Punctuation 77
 
0.9%
Close Punctuation 13
 
0.2%
Open Punctuation 13
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 978
16.8%
법 616
10.6%
조 612
10.5%
위 429
7.4%
관 420
7.2%
중 403
6.9%
생 403
6.9%
공 402
6.9%
리 393
6.7%
항 373
 
6.4%
Other values (42) 799
13.7%
Decimal Number
ValueCountFrequency (%)
1 679
50.1%
7 168
 
12.4%
4 161
 
11.9%
2 137
 
10.1%
3 123
 
9.1%
0 44
 
3.2%
9 18
 
1.3%
8 9
 
0.7%
5 8
 
0.6%
6 7
 
0.5%
Close Punctuation
ValueCountFrequency (%)
] 7
53.8%
) 6
46.2%
Open Punctuation
ValueCountFrequency (%)
[ 7
53.8%
( 6
46.2%
Space Separator
ValueCountFrequency (%)
835
100.0%
Other Punctuation
ValueCountFrequency (%)
, 77
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5828
71.8%
Common 2292
 
28.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 978
16.8%
법 616
10.6%
조 612
10.5%
위 429
7.4%
관 420
7.2%
중 403
6.9%
생 403
6.9%
공 402
6.9%
리 393
6.7%
항 373
 
6.4%
Other values (42) 799
13.7%
Common
ValueCountFrequency (%)
835
36.4%
1 679
29.6%
7 168
 
7.3%
4 161
 
7.0%
2 137
 
6.0%
3 123
 
5.4%
, 77
 
3.4%
0 44
 
1.9%
9 18
 
0.8%
8 9
 
0.4%
Other values (6) 41
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5828
71.8%
ASCII 2292
 
28.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 978
16.8%
법 616
10.6%
조 612
10.5%
위 429
7.4%
관 420
7.2%
중 403
6.9%
생 403
6.9%
공 402
6.9%
리 393
6.7%
항 373
 
6.4%
Other values (42) 799
13.7%
ASCII
ValueCountFrequency (%)
835
36.4%
1 679
29.6%
7 168
 
7.3%
4 161
 
7.0%
2 137
 
6.0%
3 123
 
5.4%
, 77
 
3.4%
0 44
 
1.9%
9 18
 
0.8%
8 9
 
0.4%
Other values (6) 41
 
1.8%

위반일자
Real number (ℝ)

HIGH CORRELATION 

Distinct315
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19992304
Minimum200703
Maximum20200706
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-05-11T08:02:08.774460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200703
5-th percentile20030723
Q120060327
median20090407
Q320130312
95-th percentile20190122
Maximum20200706
Range20000003
Interquartile range (IQR)69985

Descriptive statistics

Standard deviation1415714.2
Coefficient of variation (CV)0.07081296
Kurtosis183.26894
Mean19992304
Median Absolute Deviation (MAD)30306
Skewness-13.566231
Sum1.1135713 × 1010
Variance2.0042467 × 1012
MonotonicityNot monotonic
2024-05-11T08:02:09.279975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20120101 25
 
4.5%
20101221 15
 
2.7%
20061228 14
 
2.5%
20171116 14
 
2.5%
20060327 9
 
1.6%
20061205 6
 
1.1%
20120323 6
 
1.1%
20091125 6
 
1.1%
20190111 6
 
1.1%
20101117 6
 
1.1%
Other values (305) 450
80.8%
ValueCountFrequency (%)
200703 2
0.4%
2003109 1
0.2%
20021001 1
0.2%
20021210 2
0.4%
20021213 1
0.2%
20030101 1
0.2%
20030106 1
0.2%
20030120 2
0.4%
20030130 1
0.2%
20030201 1
0.2%
ValueCountFrequency (%)
20200706 1
0.2%
20200115 1
0.2%
20190809 1
0.2%
20190621 1
0.2%
20190619 1
0.2%
20190604 2
0.4%
20190522 1
0.2%
20190428 1
0.2%
20190425 1
0.2%
20190329 1
0.2%

위반내용
Text

Distinct265
Distinct (%)47.7%
Missing2
Missing (%)0.4%
Memory size4.5 KiB
2024-05-11T08:02:09.949692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length205
Median length137
Mean length17.003604
Min length4

Characters and Unicode

Total characters9437
Distinct characters302
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique170 ?
Unique (%)30.6%

Sample

1st row소독 청소 미실시
2nd row소독청소 미실시
3rd row윤락행위 알선 및 장소제공
4th row윤락행위 알선 및 장소제공
5th row윤락행위(성매매)알선
ValueCountFrequency (%)
및 94
 
4.5%
위반 58
 
2.8%
미이수 41
 
2.0%
미게시 37
 
1.8%
위생교육 35
 
1.7%
35
 
1.7%
위생교육미필 34
 
1.6%
설치 33
 
1.6%
욕조수 27
 
1.3%
장소제공 27
 
1.3%
Other values (629) 1652
79.7%
2024-05-11T08:02:11.180145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1584
 
16.8%
위 281
 
3.0%
0 219
 
2.3%
미 210
 
2.2%
이 177
 
1.9%
1 173
 
1.8%
소 172
 
1.8%
설 155
 
1.6%
2 150
 
1.6%
매 142
 
1.5%
Other values (292) 6174
65.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6774
71.8%
Space Separator 1584
 
16.8%
Decimal Number 714
 
7.6%
Other Punctuation 140
 
1.5%
Close Punctuation 87
 
0.9%
Open Punctuation 87
 
0.9%
Dash Punctuation 32
 
0.3%
Uppercase Letter 12
 
0.1%
Lowercase Letter 4
 
< 0.1%
Other Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
위 281
 
4.1%
미 210
 
3.1%
이 177
 
2.6%
소 172
 
2.5%
설 155
 
2.3%
매 142
 
2.1%
년 127
 
1.9%
시 127
 
1.9%
수 124
 
1.8%
성 124
 
1.8%
Other values (265) 5135
75.8%
Decimal Number
ValueCountFrequency (%)
0 219
30.7%
1 173
24.2%
2 150
21.0%
6 59
 
8.3%
3 30
 
4.2%
5 22
 
3.1%
8 21
 
2.9%
4 19
 
2.7%
7 12
 
1.7%
9 9
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 76
54.3%
: 26
 
18.6%
, 24
 
17.1%
/ 10
 
7.1%
' 2
 
1.4%
？ 2
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
C 6
50.0%
T 3
25.0%
V 3
25.0%
Lowercase Letter
ValueCountFrequency (%)
c 2
50.0%
t 1
25.0%
v 1
25.0%
Space Separator
ValueCountFrequency (%)
1584
100.0%
Close Punctuation
ValueCountFrequency (%)
) 87
100.0%
Open Punctuation
ValueCountFrequency (%)
( 87
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%
Other Symbol
ValueCountFrequency (%)
○ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6774
71.8%
Common 2647
 
28.0%
Latin 16
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
위 281
 
4.1%
미 210
 
3.1%
이 177
 
2.6%
소 172
 
2.5%
설 155
 
2.3%
매 142
 
2.1%
년 127
 
1.9%
시 127
 
1.9%
수 124
 
1.8%
성 124
 
1.8%
Other values (265) 5135
75.8%
Common
ValueCountFrequency (%)
1584
59.8%
0 219
 
8.3%
1 173
 
6.5%
2 150
 
5.7%
) 87
 
3.3%
( 87
 
3.3%
. 76
 
2.9%
6 59
 
2.2%
- 32
 
1.2%
3 30
 
1.1%
Other values (11) 150
 
5.7%
Latin
ValueCountFrequency (%)
C 6
37.5%
T 3
18.8%
V 3
18.8%
c 2
 
12.5%
t 1
 
6.2%
v 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6774
71.8%
ASCII 2658
 
28.2%
Geometric Shapes 3
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1584
59.6%
0 219
 
8.2%
1 173
 
6.5%
2 150
 
5.6%
) 87
 
3.3%
( 87
 
3.3%
. 76
 
2.9%
6 59
 
2.2%
- 32
 
1.2%
3 30
 
1.1%
Other values (15) 161
 
6.1%
Hangul
ValueCountFrequency (%)
위 281
 
4.1%
미 210
 
3.1%
이 177
 
2.6%
소 172
 
2.5%
설 155
 
2.3%
매 142
 
2.1%
년 127
 
1.9%
시 127
 
1.9%
수 124
 
1.8%
성 124
 
1.8%
Other values (265) 5135
75.8%
Geometric Shapes
ValueCountFrequency (%)
○ 3
100.0%
None
ValueCountFrequency (%)
？ 2
100.0%

처분내용
Text

Distinct127
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2024-05-11T08:02:11.665720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length45
Mean length8.8850987
Min length2

Characters and Unicode

Total characters4949
Distinct characters98
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)15.8%

Sample

1st row개선명령
2nd row개선명령 및 과태료부과
3rd row영업정지
4th row영업정지
5th row영업정지2월(영업정지기간:2007.1.10~2007.3.9)
ValueCountFrequency (%)
개선명령 157
21.8%
영업정지 104
14.5%
경고 69
 
9.6%
과태료부과 28
 
3.9%
영업소폐쇄 25
 
3.5%
경고및과태료부과 20
 
2.8%
과태료 17
 
2.4%
경감 15
 
2.1%
과태료부과(자진납부에 15
 
2.1%
의한 14
 
1.9%
Other values (150) 255
35.5%
2024-05-11T08:02:12.651891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 328
 
6.6%
과 314
 
6.3%
2 225
 
4.5%
개 193
 
3.9%
. 188
 
3.8%
업 186
 
3.8%
선 185
 
3.7%
령 185
 
3.7%
명 185
 
3.7%
영 184
 
3.7%
Other values (88) 2776
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3116
63.0%
Decimal Number 1053
 
21.3%
Other Punctuation 277
 
5.6%
Space Separator 164
 
3.3%
Open Punctuation 146
 
3.0%
Close Punctuation 145
 
2.9%
Math Symbol 45
 
0.9%
Dash Punctuation 2
 
< 0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 314
 
10.1%
개 193
 
6.2%
업 186
 
6.0%
선 185
 
5.9%
령 185
 
5.9%
명 185
 
5.9%
영 184
 
5.9%
지 171
 
5.5%
정 161
 
5.2%
부 147
 
4.7%
Other values (66) 1205
38.7%
Decimal Number
ValueCountFrequency (%)
0 328
31.1%
2 225
21.4%
1 179
17.0%
3 84
 
8.0%
9 57
 
5.4%
8 47
 
4.5%
7 40
 
3.8%
4 38
 
3.6%
5 32
 
3.0%
6 23
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 188
67.9%
, 67
 
24.2%
: 20
 
7.2%
/ 1
 
0.4%
* 1
 
0.4%
Math Symbol
ValueCountFrequency (%)
~ 44
97.8%
= 1
 
2.2%
Space Separator
ValueCountFrequency (%)
164
100.0%
Open Punctuation
ValueCountFrequency (%)
( 146
100.0%
Close Punctuation
ValueCountFrequency (%)
) 145
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3116
63.0%
Common 1833
37.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 314
 
10.1%
개 193
 
6.2%
업 186
 
6.0%
선 185
 
5.9%
령 185
 
5.9%
명 185
 
5.9%
영 184
 
5.9%
지 171
 
5.5%
정 161
 
5.2%
부 147
 
4.7%
Other values (66) 1205
38.7%
Common
ValueCountFrequency (%)
0 328
17.9%
2 225
12.3%
. 188
10.3%
1 179
9.8%
164
8.9%
( 146
8.0%
) 145
7.9%
3 84
 
4.6%
, 67
 
3.7%
9 57
 
3.1%
Other values (12) 250
13.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3116
63.0%
ASCII 1833
37.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 328
17.9%
2 225
12.3%
. 188
10.3%
1 179
9.8%
164
8.9%
( 146
8.0%
) 145
7.9%
3 84
 
4.6%
, 67
 
3.7%
9 57
 
3.1%
Other values (12) 250
13.6%
Hangul
ValueCountFrequency (%)
과 314
 
10.1%
개 193
 
6.2%
업 186
 
6.0%
선 185
 
5.9%
령 185
 
5.9%
명 185
 
5.9%
영 184
 
5.9%
지 171
 
5.5%
정 161
 
5.2%
부 147
 
4.7%
Other values (66) 1205
38.7%

처분기간
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)30.4%
Missing534
Missing (%)95.9%
Infinite0
Infinite (%)0.0%
Mean9.7391304
Minimum0
Maximum20
Zeros3
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-05-11T08:02:13.007360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median10
Q315
95-th percentile15
Maximum20
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.824848
Coefficient of variation (CV)0.59808707
Kurtosis-0.89559268
Mean9.7391304
Median Absolute Deviation (MAD)5
Skewness-0.33488526
Sum224
Variance33.928854
MonotonicityNot monotonic
2024-05-11T08:02:13.392164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
15 8
 
1.4%
10 6
 
1.1%
0 3
 
0.5%
5 3
 
0.5%
20 1
 
0.2%
2 1
 
0.2%
7 1
 
0.2%
(Missing) 534
95.9%
ValueCountFrequency (%)
0 3
 
0.5%
2 1
 
0.2%
5 3
 
0.5%
7 1
 
0.2%
10 6
1.1%
15 8
1.4%
20 1
 
0.2%
ValueCountFrequency (%)
20 1
 
0.2%
15 8
1.4%
10 6
1.1%
7 1
 
0.2%
5 3
 
0.5%
2 1
 
0.2%
0 3
 
0.5%

영업장면적(㎡)
Real number (ℝ)

MISSING  ZEROS 

Distinct231
Distinct (%)51.4%
Missing108
Missing (%)19.4%
Infinite0
Infinite (%)0.0%
Mean1284.7247
Minimum0
Maximum25524.92
Zeros6
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-05-11T08:02:13.817134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.8
Q160
median150
Q3498.4
95-th percentile5905.96
Maximum25524.92
Range25524.92
Interquartile range (IQR)438.4

Descriptive statistics

Standard deviation3564.8284
Coefficient of variation (CV)2.77478
Kurtosis19.915874
Mean1284.7247
Median Absolute Deviation (MAD)117
Skewness4.2923616
Sum576841.39
Variance12708002
MonotonicityNot monotonic
2024-05-11T08:02:14.327012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165.0 13
 
2.3%
347.0 12
 
2.2%
1213.0 12
 
2.2%
5905.96 11
 
2.0%
66.0 10
 
1.8%
99.0 9
 
1.6%
90.0 8
 
1.4%
30.74 6
 
1.1%
82.75 6
 
1.1%
19.8 6
 
1.1%
Other values (221) 356
63.9%
(Missing) 108
 
19.4%
ValueCountFrequency (%)
0.0 6
1.1%
8.0 2
 
0.4%
10.0 1
 
0.2%
10.1 1
 
0.2%
13.2 2
 
0.4%
14.33 1
 
0.2%
15.0 1
 
0.2%
16.5 1
 
0.2%
16.53 1
 
0.2%
16.66 1
 
0.2%
ValueCountFrequency (%)
25524.92 1
 
0.2%
22041.0 2
0.4%
20574.41 1
 
0.2%
20558.67 4
0.7%
18598.8 1
 
0.2%
15016.24 1
 
0.2%
12388.79 2
0.4%
12062.82 1
 
0.2%
11271.0 1
 
0.2%
11077.0 2
0.4%

Interactions

2024-05-11T08:01:48.736747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:41.261987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:43.230165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:44.990141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:46.885928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:49.174281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:41.719122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:43.576278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:45.333641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:47.351417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:49.542134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:42.189738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:43.978381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:45.666282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:47.773010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:49.965818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:42.571177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:44.384254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:46.097136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:48.056778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:50.373543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:42.924227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:44.717566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:46.437660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:01:48.394516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T08:02:14.723856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분일자업종명업태명지도점검일자위반일자처분기간영업장면적(㎡)
처분일자1.0000.5530.6610.9860.0000.7810.486
업종명0.5531.0000.9660.5520.0000.7040.302
업태명0.6610.9661.0000.6600.0000.5810.639
지도점검일자0.9860.5520.6601.0000.0000.5460.494
위반일자0.0000.0000.0000.0001.000NaNNaN
처분기간0.7810.7040.5810.546NaN1.0000.881
영업장면적(㎡)0.4860.3020.6390.494NaN0.8811.000
2024-05-11T08:02:15.055784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명업종명
업태명1.0000.831
업종명0.8311.000
2024-05-11T08:02:15.317275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분일자지도점검일자위반일자처분기간영업장면적(㎡)업종명업태명
처분일자1.0000.9980.995-0.2450.2840.2730.329
지도점검일자0.9981.0000.996-0.2830.2810.2720.328
위반일자0.9950.9961.000-0.2830.2830.0000.000
처분기간-0.245-0.283-0.2831.0000.0520.5310.413
영업장면적(㎡)0.2840.2810.2830.0521.0000.1410.324
업종명0.2730.2720.0000.5310.1411.0000.831
업태명0.3290.3280.0000.4130.3240.8311.000

Missing values

2024-05-11T08:01:50.992029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T08:01:51.967087image/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.
2024-05-11T08:01:52.451928image/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(㎡)
0301000020040609001숙박업(일반)여관업궁전여관<NA>서울특별시 중구 남대문로5가 14번지 5호20040510처분확정개선명령공중위생관리법 제 10조20040510소독 청소 미실시개선명령<NA>165.0
1301000020040609001숙박업(일반)여관업궁전여관<NA>서울특별시 중구 남대문로5가 14번지 5호20040520처분확정개선명령 및 과태료부과공중위생관리법제10조20040520소독청소 미실시개선명령 및 과태료부과<NA>165.0
2301000020040311004숙박업(일반)여관업화성여관<NA>서울특별시 중구 산림동 84번지 4호20040211처분확정영업정지공중위생관리법 제11조 1항20040211윤락행위 알선 및 장소제공영업정지<NA>165.0
3301000020040813005숙박업(일반)여관업경남여관<NA>서울특별시 중구 회현동1가 185번지 2호20040708처분확정영업정지공중위생관리법제11조제1항20040709윤락행위 알선 및 장소제공영업정지<NA>75.0
4301000020061227005숙박업(일반)여관업경남여관<NA>서울특별시 중구 회현동1가 185번지 2호20061004처분확정영업정지2월(영업정지기간:2007.1.10~2007.3.9)공중위생관리법 제11조제1항 및 같은법시행규칙 제19조(행정처분기준)20061004윤락행위(성매매)알선영업정지2월(영업정지기간:2007.1.10~2007.3.9)<NA>75.0
5301000020110520005숙박업(일반)여관업경남여관<NA>서울특별시 중구 회현동1가 185번지 2호20091030처분확정영업정지성매매알선등행위의 처벌에 관한 법률 위반20091030성매매알선행위영업정지<NA>75.0
6301000020130108005숙박업(일반)여관업경남여관<NA>서울특별시 중구 회현동1가 185번지 2호20120921처분확정과징금부과(영업정지2월 갈음-과징금246만원처분)공중위생관리법제11조제1항20120921청소년이성혼숙과징금부과(영업정지2월 갈음-과징금246만원처분)<NA>75.0
7301000020170330005숙박업(일반)여관업명동 킹콩호텔(KingKong Hotel)서울특별시 중구 퇴계로8길 2-1, (회현동1가)서울특별시 중구 회현동1가 185번지 2호20170214처분확정경고법 제4조제7항20170214숙박요금표 미게시경고<NA>75.0
8301000020080122006숙박업(일반)일반호텔미도여관<NA>서울특별시 중구 회현동1가 70번지 0호20071122처분확정영업정지2월(2008.2.4~2008.4.3일까지)풍속영업의규제에관한법률제3조제1호, 공중위생관리법제11조제1항, 동법시행규칙제19조20070729윤락행위(성매매)알선 및 장소제공(1차)영업정지2월(2008.2.4~2008.4.3일까지)<NA>280.77
9301000020080122006숙박업(일반)일반호텔미도여관<NA>서울특별시 중구 회현동1가 70번지 0호20071122처분확정영업정지2월(2008.2.4~2008.4.3일까지)풍속영업의규제에관한법률제3조제1호, 공중위생관리법제11조제1항, 동법시행규칙제19조20070729윤락행위(성매매)알선 및 장소제공(1차)영업정지2월(2008.2.4~2008.4.3일까지)<NA>280.77
시군구코드처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자행정처분상태처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)
547301000020150715738피부미용업피부미용업황후몽에스떼서울특별시 중구 명동8나길 10, 5,6층 (충무로1가, 사보이호텔 증축동)서울특별시 중구 충무로1가 23번지 1호 사보이호텔 증축동 5,6층20150123처분확정과징금부과 189만원법 제11조제1항20150123무자격안마과징금부과 189만원<NA>343.5
548301000020140306822피부미용업피부미용업정스킨앤바디<NA>서울특별시 중구 신당동 110번지 6호 에이 지상2층20140305처분확정영업정지 1월공중위생관리법 제11조 제1항20131010무자격 안마 행위영업정지 1월<NA>26.0
549301000020070319014종합미용업일반미용업마샬미용실<NA>서울특별시 중구 명동2가 3번지 6호20070222처분확정경고공중위생관리법 제4조제7항20070222영업신고증미게시(1차)경고<NA>450.0
550301000020070319014종합미용업일반미용업마샬미용실<NA>서울특별시 중구 명동2가 3번지 6호20070222처분확정경고공중위생관리법 제4조제7항20070222영업신고증미게시(1차)경고<NA>450.0
551301000020070319014종합미용업일반미용업마샬미용실<NA>서울특별시 중구 명동2가 3번지 6호20070222처분확정과태료부과(50만원)공중위생관리법 제4조제7항20070222영업신고증미게시(1차)과태료부과(50만원)<NA>450.0
552301000020070319014종합미용업일반미용업마샬미용실<NA>서울특별시 중구 명동2가 3번지 6호20070222처분확정과태료부과(50만원)공중위생관리법 제4조제7항20070222영업신고증미게시(1차)과태료부과(50만원)<NA>450.0
5533010000201707281291종합미용업기타손톱마녀서울특별시 중구 소공로 지하 58, (충무로1가, 회현지하상가 바열27호,사열1호)서울특별시 중구 충무로1가 52번지 41호 회현지하상가 바열27호,사열1호20170706처분확정개선명령법 제10조20170706최종지불요금표 미게시개선명령<NA>14.33
5543010000201904032016-00002숙박업(생활)숙박업(생활)현대 서비스드 레지던스서울특별시 중구 마른내로12길 7-4, (충무로5가, 현대프레스타워 5~15층)서울특별시 중구 충무로5가 22번지 5호 현대프레스타워 5~15층20190313처분확정개선명령(2019.4.3~2019.5.2)법 제4조제7항20190313접객대 숙박요금표 미게시개선명령(2019.4.3~2019.5.2)<NA>7224.48
5553010000201411141221피부미용업, 네일미용업피부미용업얼짱몸짱 명동점<NA>서울특별시 중구 명동2가 54번지 31호 2,3,4층20141023처분확정개선명령공중위생관리법 제4조 및 시행규칙 7조20141023옥외가격 5개항목 미표시개선명령<NA>145.46
5563010000201511021278피부미용업, 네일미용업피부미용업지호썬탠<NA>서울특별시 중구 흥인동 156번지 5층20150903처분확정개선명령법 제3조제1항,법 제4조20150903면허증, 신고증미게시 출입문1/3이상 불투명개선명령<NA>81.1

Duplicate rows

Most frequently occurring

시군구코드처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자행정처분상태처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)# duplicates
21301000020160415685피부미용업피부미용업드림베스서울특별시 중구 명동2길 16, 4층 (명동2가, 진주빌딩)서울특별시 중구 명동2가 91번지 1호 진주빌딩 4층20160303처분확정개선명령법 제3조제1항20160303신고를 하지 아니하고 영업장 면적의 3분의 1이상을 변경개선명령<NA>165.04
23301000020171218811일반미용업일반미용업준오헤어명동4호점서울특별시 중구 명동8길 14, (명동2가, 4층)서울특별시 중구 명동2가 51번지 3호 4층20171204처분확정개선명령법 제3조제1항20171116소독기구 미비치개선명령<NA>347.04
16301000020080305295이용업일반이용업경일이용원<NA>서울특별시 중구 북창동 71번지 4호20080105처분확정개선명령공중위생관리법 제3조제1항 및 동법시행규칙 제2조20080105칸막이와 커텐을 설치개선명령<NA>66.03
27301000020180321623피부미용업피부미용업남산에스테서울특별시 중구 퇴계로18길 24, (남산동1가,2층)서울특별시 중구 남산동1가 10번지 1호 2층20180327처분확정영업정지1월(2018.03.29~2018.04.27)법 제11조제1항20171224무자격안마사로하여금 안마사의 업무에 관한 행위를 하게 한때영업정지1월(2018.03.29~2018.04.27)<NA>90.03
0301000020070116044숙박업(일반)관광호텔(주)서울프린스호텔<NA>서울특별시 중구 남산동2가 1번지 1호20061205처분확정경고공중위생관리법 제17조제1항200612052006년도 위생교육미필경고<NA>4762.612
1301000020070116044숙박업(일반)관광호텔(주)서울프린스호텔<NA>서울특별시 중구 남산동2가 1번지 1호20061205처분확정과태료부과공중위생관리법 제17조제1항200612052006년도 위생교육미필과태료부과<NA>4762.612
2301000020070319014종합미용업일반미용업마샬미용실<NA>서울특별시 중구 명동2가 3번지 6호20070222처분확정경고공중위생관리법 제4조제7항20070222영업신고증미게시(1차)경고<NA>450.02
3301000020070319014종합미용업일반미용업마샬미용실<NA>서울특별시 중구 명동2가 3번지 6호20070222처분확정과태료부과(50만원)공중위생관리법 제4조제7항20070222영업신고증미게시(1차)과태료부과(50만원)<NA>450.02
4301000020070319069미용업일반미용업중앙<NA>서울특별시 중구 황학동 73번지 3호20061228처분확정경고공중위생관리법 제17조제1항200612282006년도 위생교육미필경고<NA><NA>2
5301000020070319069미용업일반미용업중앙<NA>서울특별시 중구 황학동 73번지 3호20061228처분확정과태료부과(20만원)공중위생관리법 제17조제1항200612282006년도 위생교육미필과태료부과(20만원)<NA><NA>2