Overview

Dataset statistics

Number of variables17
Number of observations683
Missing cells1048
Missing cells (%)9.0%
Duplicate rows33
Duplicate rows (%)4.8%
Total size in memory94.8 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(㎡)
Author광진구
URLhttps://data.seoul.go.kr/dataList/OA-9912/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
행정처분상태 has constant value ""Constant
Dataset has 33 (4.8%) 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 44 (6.4%) missing valuesMissing
소재지도로명 has 337 (49.3%) missing valuesMissing
처분기간 has 598 (87.6%) missing valuesMissing
영업장면적(㎡) has 68 (10.0%) missing valuesMissing
처분기간 has 57 (8.3%) zerosZeros
영업장면적(㎡) has 19 (2.8%) zerosZeros

Reproduction

Analysis started2024-05-11 03:02:26.275255
Analysis finished2024-05-11 03:02:39.486391
Duration13.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
3040000
683 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3040000
2nd row3040000
3rd row3040000
4th row3040000
5th row3040000

Common Values

ValueCountFrequency (%)
3040000 683
100.0%

Length

2024-05-11T03:02:39.881478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:02:40.340244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3040000 683
100.0%

처분일자
Real number (ℝ)

HIGH CORRELATION 

Distinct300
Distinct (%)43.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20133027
Minimum20040119
Maximum20240325
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-05-11T03:02:41.110608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20040119
5-th percentile20052129
Q120080520
median20120619
Q320191120
95-th percentile20211229
Maximum20240325
Range200206
Interquartile range (IQR)110600

Descriptive statistics

Standard deviation57284.895
Coefficient of variation (CV)0.0028453195
Kurtosis-1.4070064
Mean20133027
Median Absolute Deviation (MAD)49703
Skewness0.15940421
Sum1.3750857 × 1010
Variance3.2815591 × 109
MonotonicityNot monotonic
2024-05-11T03:02:41.884424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20201110 26
 
3.8%
20211110 24
 
3.5%
20230508 22
 
3.2%
20060519 20
 
2.9%
20191120 15
 
2.2%
20170322 12
 
1.8%
20200417 9
 
1.3%
20200915 9
 
1.3%
20070226 9
 
1.3%
20191126 8
 
1.2%
Other values (290) 529
77.5%
ValueCountFrequency (%)
20040119 1
0.1%
20040228 1
0.1%
20040315 1
0.1%
20040408 2
0.3%
20040409 1
0.1%
20040427 1
0.1%
20041105 1
0.1%
20041106 1
0.1%
20041108 2
0.3%
20041110 1
0.1%
ValueCountFrequency (%)
20240325 1
 
0.1%
20240223 1
 
0.1%
20231130 1
 
0.1%
20230915 1
 
0.1%
20230724 1
 
0.1%
20230508 22
3.2%
20230427 1
 
0.1%
20230210 1
 
0.1%
20220526 1
 
0.1%
20220216 2
 
0.3%

교부번호
Text

MISSING 

Distinct358
Distinct (%)56.0%
Missing44
Missing (%)6.4%
Memory size5.5 KiB
2024-05-11T03:02:42.896214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length3
Mean length4.2613459
Min length1

Characters and Unicode

Total characters2723
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

Unique247 ?
Unique (%)38.7%

Sample

1st row002
2nd row002
3rd row4
4th row8
5th row008
ValueCountFrequency (%)
030 13
 
2.0%
62 11
 
1.7%
063 9
 
1.4%
058 9
 
1.4%
055 9
 
1.4%
043 9
 
1.4%
018 9
 
1.4%
031 7
 
1.1%
035 6
 
0.9%
114 6
 
0.9%
Other values (348) 551
86.2%
2024-05-11T03:02:44.522732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 751
27.6%
1 445
16.3%
2 354
13.0%
3 193
 
7.1%
5 158
 
5.8%
- 150
 
5.5%
8 146
 
5.4%
4 144
 
5.3%
6 140
 
5.1%
7 123
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2573
94.5%
Dash Punctuation 150
 
5.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 751
29.2%
1 445
17.3%
2 354
13.8%
3 193
 
7.5%
5 158
 
6.1%
8 146
 
5.7%
4 144
 
5.6%
6 140
 
5.4%
7 123
 
4.8%
9 119
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2723
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 751
27.6%
1 445
16.3%
2 354
13.0%
3 193
 
7.1%
5 158
 
5.8%
- 150
 
5.5%
8 146
 
5.4%
4 144
 
5.3%
6 140
 
5.1%
7 123
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2723
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 751
27.6%
1 445
16.3%
2 354
13.0%
3 193
 
7.1%
5 158
 
5.8%
- 150
 
5.5%
8 146
 
5.4%
4 144
 
5.3%
6 140
 
5.1%
7 123
 
4.5%

업종명
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
숙박업(일반)
159 
목욕장업
131 
위생관리용역업
75 
세탁업
61 
이용업
57 
Other values (14)
200 

Length

Max length23
Median length16
Mean length5.2298682
Min length3

Unique

Unique6 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
숙박업(일반) 159
23.3%
목욕장업 131
19.2%
위생관리용역업 75
11.0%
세탁업 61
 
8.9%
이용업 57
 
8.3%
일반미용업 56
 
8.2%
피부미용업 54
 
7.9%
미용업 46
 
6.7%
종합미용업 17
 
2.5%
피부미용업, 네일미용업 8
 
1.2%
Other values (9) 19
 
2.8%

Length

2024-05-11T03:02:45.091530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
숙박업(일반 159
22.5%
목욕장업 131
18.6%
위생관리용역업 75
10.6%
피부미용업 64
9.1%
세탁업 61
 
8.6%
일반미용업 60
 
8.5%
이용업 57
 
8.1%
미용업 52
 
7.4%
네일미용업 22
 
3.1%
종합미용업 17
 
2.4%
Other values (3) 8
 
1.1%

업태명
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
여관업
143 
일반미용업
112 
위생관리용역업
75 
공동탕업
68 
공동탕업+찜질시설서비스영업
62 
Other values (14)
223 

Length

Max length14
Median length9
Mean length5.5080527
Min length2

Unique

Unique5 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
여관업 143
20.9%
일반미용업 112
16.4%
위생관리용역업 75
11.0%
공동탕업 68
10.0%
공동탕업+찜질시설서비스영업 62
9.1%
일반세탁업 58
8.5%
피부미용업 58
8.5%
일반이용업 57
 
8.3%
네일아트업 23
 
3.4%
여인숙업 8
 
1.2%
Other values (9) 19
 
2.8%

Length

2024-05-11T03:02:45.576361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
여관업 143
20.9%
일반미용업 112
16.4%
위생관리용역업 75
11.0%
공동탕업 68
9.9%
공동탕업+찜질시설서비스영업 62
9.1%
일반세탁업 58
8.5%
피부미용업 58
8.5%
일반이용업 57
 
8.3%
네일아트업 23
 
3.4%
여인숙업 8
 
1.2%
Other values (9) 20
 
2.9%

업소명
Text

Distinct419
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
2024-05-11T03:02:46.279393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length23
Mean length5.9560761
Min length1

Characters and Unicode

Total characters4068
Distinct characters405
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

Unique298 ?
Unique (%)43.6%

Sample

1st row대원장여관
2nd row대원장여관
3rd row동궁여관
4th row도원여관
5th row도원여관
ValueCountFrequency (%)
해피데이스파 11
 
1.3%
24시 11
 
1.3%
불가마 11
 
1.3%
강변스파랜드 11
 
1.3%
매일온천탕 10
 
1.2%
메사빌보석사우나 9
 
1.1%
실로암 9
 
1.1%
목욕장 9
 
1.1%
헤어 7
 
0.9%
정 6
 
0.7%
Other values (477) 729
88.6%
2024-05-11T03:02:47.692587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
141
 
3.5%
사 95
 
2.3%
스 90
 
2.2%
이 84
 
2.1%
) 79
 
1.9%
( 79
 
1.9%
나 74
 
1.8%
텔 69
 
1.7%
장 65
 
1.6%
우 62
 
1.5%
Other values (395) 3230
79.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3481
85.6%
Lowercase Letter 156
 
3.8%
Space Separator 141
 
3.5%
Close Punctuation 79
 
1.9%
Open Punctuation 79
 
1.9%
Uppercase Letter 75
 
1.8%
Decimal Number 46
 
1.1%
Other Punctuation 11
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 95
 
2.7%
스 90
 
2.6%
이 84
 
2.4%
나 74
 
2.1%
텔 69
 
2.0%
장 65
 
1.9%
우 62
 
1.8%
대 61
 
1.8%
어 61
 
1.8%
주 59
 
1.7%
Other values (333) 2761
79.3%
Lowercase Letter
ValueCountFrequency (%)
e 19
12.2%
a 17
10.9%
i 16
10.3%
o 15
9.6%
l 14
9.0%
n 13
8.3%
h 11
 
7.1%
r 7
 
4.5%
c 6
 
3.8%
s 6
 
3.8%
Other values (13) 32
20.5%
Uppercase Letter
ValueCountFrequency (%)
T 7
 
9.3%
A 7
 
9.3%
L 7
 
9.3%
E 6
 
8.0%
H 5
 
6.7%
I 5
 
6.7%
R 4
 
5.3%
C 4
 
5.3%
S 3
 
4.0%
O 3
 
4.0%
Other values (12) 24
32.0%
Decimal Number
ValueCountFrequency (%)
4 19
41.3%
2 18
39.1%
5 3
 
6.5%
1 2
 
4.3%
0 2
 
4.3%
9 1
 
2.2%
7 1
 
2.2%
Other Punctuation
ValueCountFrequency (%)
' 2
18.2%
: 2
18.2%
& 2
18.2%
, 2
18.2%
? 1
9.1%
. 1
9.1%
？ 1
9.1%
Space Separator
ValueCountFrequency (%)
141
100.0%
Close Punctuation
ValueCountFrequency (%)
) 79
100.0%
Open Punctuation
ValueCountFrequency (%)
( 79
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3481
85.6%
Common 356
 
8.8%
Latin 231
 
5.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 95
 
2.7%
스 90
 
2.6%
이 84
 
2.4%
나 74
 
2.1%
텔 69
 
2.0%
장 65
 
1.9%
우 62
 
1.8%
대 61
 
1.8%
어 61
 
1.8%
주 59
 
1.7%
Other values (333) 2761
79.3%
Latin
ValueCountFrequency (%)
e 19
 
8.2%
a 17
 
7.4%
i 16
 
6.9%
o 15
 
6.5%
l 14
 
6.1%
n 13
 
5.6%
h 11
 
4.8%
T 7
 
3.0%
A 7
 
3.0%
r 7
 
3.0%
Other values (35) 105
45.5%
Common
ValueCountFrequency (%)
141
39.6%
) 79
22.2%
( 79
22.2%
4 19
 
5.3%
2 18
 
5.1%
5 3
 
0.8%
' 2
 
0.6%
: 2
 
0.6%
& 2
 
0.6%
, 2
 
0.6%
Other values (7) 9
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3481
85.6%
ASCII 586
 
14.4%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
141
24.1%
) 79
13.5%
( 79
13.5%
4 19
 
3.2%
e 19
 
3.2%
2 18
 
3.1%
a 17
 
2.9%
i 16
 
2.7%
o 15
 
2.6%
l 14
 
2.4%
Other values (51) 169
28.8%
Hangul
ValueCountFrequency (%)
사 95
 
2.7%
스 90
 
2.6%
이 84
 
2.4%
나 74
 
2.1%
텔 69
 
2.0%
장 65
 
1.9%
우 62
 
1.8%
대 61
 
1.8%
어 61
 
1.8%
주 59
 
1.7%
Other values (333) 2761
79.3%
None
ValueCountFrequency (%)
？ 1
100.0%

소재지도로명
Text

MISSING 

Distinct251
Distinct (%)72.5%
Missing337
Missing (%)49.3%
Memory size5.5 KiB
2024-05-11T03:02:48.517300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length61
Median length49
Mean length29.089595
Min length23

Characters and Unicode

Total characters10065
Distinct characters145
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

Unique199 ?
Unique (%)57.5%

Sample

1st row서울특별시 광진구 광나루로 517-5, (구의동)
2nd row서울특별시 광진구 광나루로 517-5, (구의동)
3rd row서울특별시 광진구 능동로19길 53, (화양동)
4th row서울특별시 광진구 동일로4길 2, (자양동)
5th row서울특별시 광진구 아차산로78길 147, (광장동)
ValueCountFrequency (%)
서울특별시 346
 
17.5%
광진구 346
 
17.5%
구의동 75
 
3.8%
자양동 74
 
3.7%
중곡동 65
 
3.3%
1층 60
 
3.0%
화양동 59
 
3.0%
아차산로 30
 
1.5%
자양로 28
 
1.4%
2층 25
 
1.3%
Other values (396) 872
44.0%
2024-05-11T03:02:50.038753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1634
 
16.2%
구 449
 
4.5%
동 434
 
4.3%
, 422
 
4.2%
광 420
 
4.2%
1 372
 
3.7%
) 356
 
3.5%
( 356
 
3.5%
진 348
 
3.5%
울 348
 
3.5%
Other values (135) 4926
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5714
56.8%
Space Separator 1634
 
16.2%
Decimal Number 1524
 
15.1%
Other Punctuation 423
 
4.2%
Close Punctuation 356
 
3.5%
Open Punctuation 356
 
3.5%
Dash Punctuation 45
 
0.4%
Uppercase Letter 11
 
0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 449
 
7.9%
동 434
 
7.6%
광 420
 
7.4%
진 348
 
6.1%
울 348
 
6.1%
서 348
 
6.1%
시 346
 
6.1%
별 346
 
6.1%
특 346
 
6.1%
로 345
 
6.0%
Other values (110) 1984
34.7%
Decimal Number
ValueCountFrequency (%)
1 372
24.4%
2 222
14.6%
3 170
11.2%
4 147
 
9.6%
5 145
 
9.5%
0 118
 
7.7%
7 99
 
6.5%
8 92
 
6.0%
6 88
 
5.8%
9 71
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
B 3
27.3%
A 2
18.2%
C 1
 
9.1%
F 1
 
9.1%
K 1
 
9.1%
S 1
 
9.1%
W 1
 
9.1%
Z 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
, 422
99.8%
& 1
 
0.2%
Space Separator
ValueCountFrequency (%)
1634
100.0%
Close Punctuation
ValueCountFrequency (%)
) 356
100.0%
Open Punctuation
ValueCountFrequency (%)
( 356
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5714
56.8%
Common 4340
43.1%
Latin 11
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 449
 
7.9%
동 434
 
7.6%
광 420
 
7.4%
진 348
 
6.1%
울 348
 
6.1%
서 348
 
6.1%
시 346
 
6.1%
별 346
 
6.1%
특 346
 
6.1%
로 345
 
6.0%
Other values (110) 1984
34.7%
Common
ValueCountFrequency (%)
1634
37.6%
, 422
 
9.7%
1 372
 
8.6%
) 356
 
8.2%
( 356
 
8.2%
2 222
 
5.1%
3 170
 
3.9%
4 147
 
3.4%
5 145
 
3.3%
0 118
 
2.7%
Other values (7) 398
 
9.2%
Latin
ValueCountFrequency (%)
B 3
27.3%
A 2
18.2%
C 1
 
9.1%
F 1
 
9.1%
K 1
 
9.1%
S 1
 
9.1%
W 1
 
9.1%
Z 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5714
56.8%
ASCII 4351
43.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1634
37.6%
, 422
 
9.7%
1 372
 
8.5%
) 356
 
8.2%
( 356
 
8.2%
2 222
 
5.1%
3 170
 
3.9%
4 147
 
3.4%
5 145
 
3.3%
0 118
 
2.7%
Other values (15) 409
 
9.4%
Hangul
ValueCountFrequency (%)
구 449
 
7.9%
동 434
 
7.6%
광 420
 
7.4%
진 348
 
6.1%
울 348
 
6.1%
서 348
 
6.1%
시 346
 
6.1%
별 346
 
6.1%
특 346
 
6.1%
로 345
 
6.0%
Other values (110) 1984
34.7%

소재지지번
Text

Distinct410
Distinct (%)60.1%
Missing1
Missing (%)0.1%
Memory size5.5 KiB
2024-05-11T03:02:51.077481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length47
Mean length26.73607
Min length21

Characters and Unicode

Total characters18234
Distinct characters141
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

Unique283 ?
Unique (%)41.5%

Sample

1st row서울특별시 광진구 화양동 115번지 8호
2nd row서울특별시 광진구 화양동 115번지 8호
3rd row서울특별시 광진구 자양동 236번지 64호
4th row서울특별시 광진구 중곡동 196번지 11호
5th row서울특별시 광진구 중곡동 196번지 11호
ValueCountFrequency (%)
서울특별시 682
18.7%
광진구 682
18.7%
자양동 176
 
4.8%
중곡동 141
 
3.9%
구의동 139
 
3.8%
화양동 123
 
3.4%
1호 79
 
2.2%
1층 53
 
1.5%
2호 47
 
1.3%
광장동 45
 
1.2%
Other values (438) 1475
40.5%
2024-05-11T03:02:52.525557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4802
26.3%
구 825
 
4.5%
광 735
 
4.0%
지 718
 
3.9%
동 690
 
3.8%
서 684
 
3.8%
진 684
 
3.8%
울 684
 
3.8%
시 683
 
3.7%
특 682
 
3.7%
Other values (131) 7047
38.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10076
55.3%
Space Separator 4802
26.3%
Decimal Number 3179
 
17.4%
Dash Punctuation 48
 
0.3%
Open Punctuation 36
 
0.2%
Close Punctuation 36
 
0.2%
Other Punctuation 25
 
0.1%
Uppercase Letter 21
 
0.1%
Lowercase Letter 8
 
< 0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 825
 
8.2%
광 735
 
7.3%
지 718
 
7.1%
동 690
 
6.8%
서 684
 
6.8%
진 684
 
6.8%
울 684
 
6.8%
시 683
 
6.8%
특 682
 
6.8%
별 682
 
6.8%
Other values (96) 3009
29.9%
Decimal Number
ValueCountFrequency (%)
1 680
21.4%
2 550
17.3%
5 306
9.6%
4 289
9.1%
3 283
8.9%
6 280
8.8%
0 234
 
7.4%
7 220
 
6.9%
8 173
 
5.4%
9 164
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
B 8
38.1%
A 3
 
14.3%
C 2
 
9.5%
D 2
 
9.5%
K 1
 
4.8%
S 1
 
4.8%
T 1
 
4.8%
F 1
 
4.8%
W 1
 
4.8%
Z 1
 
4.8%
Lowercase Letter
ValueCountFrequency (%)
s 2
25.0%
h 1
12.5%
e 1
12.5%
l 1
12.5%
a 1
12.5%
i 1
12.5%
c 1
12.5%
Other Punctuation
ValueCountFrequency (%)
, 22
88.0%
/ 2
 
8.0%
& 1
 
4.0%
Space Separator
ValueCountFrequency (%)
4802
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%
Open Punctuation
ValueCountFrequency (%)
( 36
100.0%
Close Punctuation
ValueCountFrequency (%)
) 36
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10076
55.3%
Common 8129
44.6%
Latin 29
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 825
 
8.2%
광 735
 
7.3%
지 718
 
7.1%
동 690
 
6.8%
서 684
 
6.8%
진 684
 
6.8%
울 684
 
6.8%
시 683
 
6.8%
특 682
 
6.8%
별 682
 
6.8%
Other values (96) 3009
29.9%
Common
ValueCountFrequency (%)
4802
59.1%
1 680
 
8.4%
2 550
 
6.8%
5 306
 
3.8%
4 289
 
3.6%
3 283
 
3.5%
6 280
 
3.4%
0 234
 
2.9%
7 220
 
2.7%
8 173
 
2.1%
Other values (8) 312
 
3.8%
Latin
ValueCountFrequency (%)
B 8
27.6%
A 3
 
10.3%
s 2
 
6.9%
C 2
 
6.9%
D 2
 
6.9%
K 1
 
3.4%
S 1
 
3.4%
T 1
 
3.4%
h 1
 
3.4%
e 1
 
3.4%
Other values (7) 7
24.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10076
55.3%
ASCII 8158
44.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4802
58.9%
1 680
 
8.3%
2 550
 
6.7%
5 306
 
3.8%
4 289
 
3.5%
3 283
 
3.5%
6 280
 
3.4%
0 234
 
2.9%
7 220
 
2.7%
8 173
 
2.1%
Other values (25) 341
 
4.2%
Hangul
ValueCountFrequency (%)
구 825
 
8.2%
광 735
 
7.3%
지 718
 
7.1%
동 690
 
6.8%
서 684
 
6.8%
진 684
 
6.8%
울 684
 
6.8%
시 683
 
6.8%
특 682
 
6.8%
별 682
 
6.8%
Other values (96) 3009
29.9%

지도점검일자
Real number (ℝ)

HIGH CORRELATION 

Distinct312
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20130619
Minimum20031105
Maximum20240217
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-05-11T03:02:53.174882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20031105
5-th percentile20051126
Q120080310
median20120327
Q320181231
95-th percentile20211007
Maximum20240217
Range209112
Interquartile range (IQR)100920.5

Descriptive statistics

Standard deviation56523.289
Coefficient of variation (CV)0.0028078267
Kurtosis-1.3406958
Mean20130619
Median Absolute Deviation (MAD)49895
Skewness0.16967605
Sum1.3749213 × 1010
Variance3.1948823 × 109
MonotonicityNot monotonic
2024-05-11T03:02:53.805027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20191231 35
 
5.1%
20181231 32
 
4.7%
20210630 28
 
4.1%
20230417 21
 
3.1%
20060519 20
 
2.9%
20170222 13
 
1.9%
20200708 10
 
1.5%
20110803 10
 
1.5%
20071009 10
 
1.5%
20150427 9
 
1.3%
Other values (302) 495
72.5%
ValueCountFrequency (%)
20031105 1
 
0.1%
20031124 1
 
0.1%
20031213 1
 
0.1%
20040226 1
 
0.1%
20040304 1
 
0.1%
20040306 1
 
0.1%
20040315 1
 
0.1%
20040613 4
0.6%
20040617 1
 
0.1%
20040619 1
 
0.1%
ValueCountFrequency (%)
20240217 1
 
0.1%
20240130 1
 
0.1%
20231023 1
 
0.1%
20230727 1
 
0.1%
20230610 1
 
0.1%
20230418 1
 
0.1%
20230417 21
3.1%
20230207 1
 
0.1%
20221120 1
 
0.1%
20220201 2
 
0.3%

행정처분상태
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
처분확정
683 

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 683
100.0%

Length

2024-05-11T03:02:54.327695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:02:54.723453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
처분확정 683
100.0%

처분명
Text

Distinct120
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
2024-05-11T03:02:55.278131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length48
Mean length7.9136164
Min length2

Characters and Unicode

Total characters5405
Distinct characters99
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)12.4%

Sample

1st row과징금부과
2nd row영업정지
3rd row영업정지 2월을 갈음한 과징금 1,800,000원
4th row과징금1,800,000원
5th row영업소폐쇄
ValueCountFrequency (%)
과태료부과 179
18.0%
경고 110
 
11.1%
개선명령 84
 
8.5%
영업소폐쇄 64
 
6.4%
영업정지 59
 
5.9%
과징금부과 55
 
5.5%
부과 38
 
3.8%
과징금 32
 
3.2%
과태료 32
 
3.2%
및 32
 
3.2%
Other values (138) 309
31.1%
2024-05-11T03:02:56.372831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 647
 
12.0%
0 364
 
6.7%
부 317
 
5.9%
313
 
5.8%
료 235
 
4.3%
태 235
 
4.3%
업 199
 
3.7%
영 188
 
3.5%
2 174
 
3.2%
1 127
 
2.3%
Other values (89) 2606
48.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3818
70.6%
Decimal Number 879
 
16.3%
Space Separator 313
 
5.8%
Other Punctuation 176
 
3.3%
Close Punctuation 100
 
1.9%
Open Punctuation 99
 
1.8%
Math Symbol 19
 
0.4%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 647
16.9%
부 317
 
8.3%
료 235
 
6.2%
태 235
 
6.2%
업 199
 
5.2%
영 188
 
4.9%
경 122
 
3.2%
지 119
 
3.1%
고 116
 
3.0%
소 112
 
2.9%
Other values (69) 1528
40.0%
Decimal Number
ValueCountFrequency (%)
0 364
41.4%
2 174
19.8%
1 127
 
14.4%
8 36
 
4.1%
3 35
 
4.0%
6 34
 
3.9%
9 31
 
3.5%
5 27
 
3.1%
7 26
 
3.0%
4 25
 
2.8%
Other Punctuation
ValueCountFrequency (%)
. 87
49.4%
, 85
48.3%
% 3
 
1.7%
? 1
 
0.6%
Math Symbol
ValueCountFrequency (%)
~ 16
84.2%
→ 3
 
15.8%
Space Separator
ValueCountFrequency (%)
313
100.0%
Close Punctuation
ValueCountFrequency (%)
) 100
100.0%
Open Punctuation
ValueCountFrequency (%)
( 99
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3818
70.6%
Common 1586
29.3%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 647
16.9%
부 317
 
8.3%
료 235
 
6.2%
태 235
 
6.2%
업 199
 
5.2%
영 188
 
4.9%
경 122
 
3.2%
지 119
 
3.1%
고 116
 
3.0%
소 112
 
2.9%
Other values (69) 1528
40.0%
Common
ValueCountFrequency (%)
0 364
23.0%
313
19.7%
2 174
11.0%
1 127
 
8.0%
) 100
 
6.3%
( 99
 
6.2%
. 87
 
5.5%
, 85
 
5.4%
8 36
 
2.3%
3 35
 
2.2%
Other values (9) 166
10.5%
Latin
ValueCountFrequency (%)
X 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3814
70.6%
ASCII 1584
29.3%
Compat Jamo 4
 
0.1%
Arrows 3
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 647
17.0%
부 317
 
8.3%
료 235
 
6.2%
태 235
 
6.2%
업 199
 
5.2%
영 188
 
4.9%
경 122
 
3.2%
지 119
 
3.1%
고 116
 
3.0%
소 112
 
2.9%
Other values (68) 1524
40.0%
ASCII
ValueCountFrequency (%)
0 364
23.0%
313
19.8%
2 174
11.0%
1 127
 
8.0%
) 100
 
6.3%
( 99
 
6.2%
. 87
 
5.5%
, 85
 
5.4%
8 36
 
2.3%
3 35
 
2.2%
Other values (9) 164
10.4%
Compat Jamo
ValueCountFrequency (%)
ㆍ 4
100.0%
Arrows
ValueCountFrequency (%)
→ 3
100.0%

법적근거
Text

Distinct106
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
2024-05-11T03:02:56.928239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length29
Mean length12.040996
Min length4

Characters and Unicode

Total characters8224
Distinct characters40
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

Unique46 ?
Unique (%)6.7%

Sample

1st row공중위생관리법 제11조
2nd row공중위생관리법 제11조
3rd row공중위생관리법 제11조,제12조
4th row공중위생관리법 제11조, 제12조
5th row공중위생관리법제3조
ValueCountFrequency (%)
공중위생관리법 290
20.8%
법 285
20.4%
제17조 114
 
8.2%
제22조제2항제6호 86
 
6.2%
제11조 61
 
4.4%
제4조 54
 
3.9%
제11조제3항제2호 32
 
2.3%
및 32
 
2.3%
제3조 31
 
2.2%
제22조 24
 
1.7%
Other values (83) 387
27.7%
2024-05-11T03:02:58.067587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 1184
14.4%
조 745
 
9.1%
724
 
8.8%
1 707
 
8.6%
법 701
 
8.5%
2 446
 
5.4%
생 401
 
4.9%
위 401
 
4.9%
공 396
 
4.8%
중 395
 
4.8%
Other values (30) 2124
25.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5684
69.1%
Decimal Number 1777
 
21.6%
Space Separator 724
 
8.8%
Other Punctuation 31
 
0.4%
Close Punctuation 4
 
< 0.1%
Open Punctuation 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 1184
20.8%
조 745
13.1%
법 701
12.3%
생 401
 
7.1%
위 401
 
7.1%
공 396
 
7.0%
중 395
 
6.9%
리 392
 
6.9%
관 392
 
6.9%
항 349
 
6.1%
Other values (16) 328
 
5.8%
Decimal Number
ValueCountFrequency (%)
1 707
39.8%
2 446
25.1%
7 190
 
10.7%
4 164
 
9.2%
3 132
 
7.4%
6 86
 
4.8%
8 24
 
1.4%
0 15
 
0.8%
9 12
 
0.7%
5 1
 
0.1%
Space Separator
ValueCountFrequency (%)
724
100.0%
Other Punctuation
ValueCountFrequency (%)
, 31
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5684
69.1%
Common 2540
30.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 1184
20.8%
조 745
13.1%
법 701
12.3%
생 401
 
7.1%
위 401
 
7.1%
공 396
 
7.0%
중 395
 
6.9%
리 392
 
6.9%
관 392
 
6.9%
항 349
 
6.1%
Other values (16) 328
 
5.8%
Common
ValueCountFrequency (%)
724
28.5%
1 707
27.8%
2 446
17.6%
7 190
 
7.5%
4 164
 
6.5%
3 132
 
5.2%
6 86
 
3.4%
, 31
 
1.2%
8 24
 
0.9%
0 15
 
0.6%
Other values (4) 21
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5684
69.1%
ASCII 2540
30.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 1184
20.8%
조 745
13.1%
법 701
12.3%
생 401
 
7.1%
위 401
 
7.1%
공 396
 
7.0%
중 395
 
6.9%
리 392
 
6.9%
관 392
 
6.9%
항 349
 
6.1%
Other values (16) 328
 
5.8%
ASCII
ValueCountFrequency (%)
724
28.5%
1 707
27.8%
2 446
17.6%
7 190
 
7.5%
4 164
 
6.5%
3 132
 
5.2%
6 86
 
3.4%
, 31
 
1.2%
8 24
 
0.9%
0 15
 
0.6%
Other values (4) 21
 
0.8%

위반일자
Real number (ℝ)

HIGH CORRELATION 

Distinct313
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20130073
Minimum20021231
Maximum20240217
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-05-11T03:02:58.497684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20021231
5-th percentile20051031
Q120080308
median20120412
Q320181231
95-th percentile20210799
Maximum20240217
Range218986
Interquartile range (IQR)100923

Descriptive statistics

Standard deviation55997.149
Coefficient of variation (CV)0.0027817658
Kurtosis-1.3755754
Mean20130073
Median Absolute Deviation (MAD)49689
Skewness0.14295295
Sum1.374884 × 1010
Variance3.1356807 × 109
MonotonicityNot monotonic
2024-05-11T03:02:59.053633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20191231 36
 
5.3%
20181231 36
 
5.3%
20210630 28
 
4.1%
20060401 20
 
2.9%
20221231 20
 
2.9%
20170101 15
 
2.2%
20120207 9
 
1.3%
20200228 9
 
1.3%
20090112 8
 
1.2%
20071114 8
 
1.2%
Other values (303) 494
72.3%
ValueCountFrequency (%)
20021231 1
0.1%
20031105 1
0.1%
20031124 1
0.1%
20031213 1
0.1%
20040226 1
0.1%
20040304 2
0.3%
20040306 1
0.1%
20040315 1
0.1%
20040617 1
0.1%
20040619 1
0.1%
ValueCountFrequency (%)
20240217 1
 
0.1%
20240130 1
 
0.1%
20231023 1
 
0.1%
20230727 1
 
0.1%
20230610 1
 
0.1%
20230207 1
 
0.1%
20221231 20
2.9%
20221230 1
 
0.1%
20221120 1
 
0.1%
20220201 2
 
0.3%

위반내용
Text

Distinct327
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
2024-05-11T03:02:59.770519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length127
Median length77
Mean length23.986823
Min length2

Characters and Unicode

Total characters16383
Distinct characters293
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique223 ?
Unique (%)32.7%

Sample

1st row청소년 이성 혼숙행위(광진경찰서장으로부터 2009.7.21경 통보 받음)
2nd row숙박자에게 성매매알선등 행위
3rd row청소년 이성혼숙 장소제공
4th row청소년 남녀 혼숙위반
5th row폐업신고 없이 시설물멸실
ValueCountFrequency (%)
미이수 138
 
4.4%
위생교육 111
 
3.5%
법정위생교육 75
 
2.4%
청소년 67
 
2.1%
폐업신고 56
 
1.8%
이성혼숙 42
 
1.3%
미이행 39
 
1.2%
미필 37
 
1.2%
장소제공 36
 
1.1%
36
 
1.1%
Other values (706) 2535
79.9%
2024-05-11T03:03:01.399219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2528
 
15.4%
0 558
 
3.4%
2 508
 
3.1%
위 496
 
3.0%
이 452
 
2.8%
1 405
 
2.5%
생 364
 
2.2%
업 356
 
2.2%
미 334
 
2.0%
수 309
 
1.9%
Other values (283) 10073
61.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10871
66.4%
Space Separator 2528
 
15.4%
Decimal Number 1899
 
11.6%
Other Punctuation 489
 
3.0%
Close Punctuation 248
 
1.5%
Open Punctuation 247
 
1.5%
Lowercase Letter 38
 
0.2%
Dash Punctuation 30
 
0.2%
Other Symbol 26
 
0.2%
Uppercase Letter 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
위 496
 
4.6%
이 452
 
4.2%
생 364
 
3.3%
업 356
 
3.3%
미 334
 
3.1%
수 309
 
2.8%
년 299
 
2.8%
육 276
 
2.5%
교 276
 
2.5%
소 209
 
1.9%
Other values (256) 7500
69.0%
Decimal Number
ValueCountFrequency (%)
0 558
29.4%
2 508
26.8%
1 405
21.3%
8 85
 
4.5%
9 75
 
3.9%
6 72
 
3.8%
7 61
 
3.2%
3 57
 
3.0%
5 56
 
2.9%
4 22
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 256
52.4%
: 108
22.1%
, 55
 
11.2%
/ 46
 
9.4%
* 24
 
4.9%
Lowercase Letter
ValueCountFrequency (%)
l 16
42.1%
m 16
42.1%
o 6
 
15.8%
Close Punctuation
ValueCountFrequency (%)
) 247
99.6%
」 1
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 246
99.6%
「 1
 
0.4%
Space Separator
ValueCountFrequency (%)
2528
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%
Other Symbol
ValueCountFrequency (%)
㎖ 26
100.0%
Uppercase Letter
ValueCountFrequency (%)
O 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10871
66.4%
Common 5470
33.4%
Latin 42
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
위 496
 
4.6%
이 452
 
4.2%
생 364
 
3.3%
업 356
 
3.3%
미 334
 
3.1%
수 309
 
2.8%
년 299
 
2.8%
육 276
 
2.5%
교 276
 
2.5%
소 209
 
1.9%
Other values (256) 7500
69.0%
Common
ValueCountFrequency (%)
2528
46.2%
0 558
 
10.2%
2 508
 
9.3%
1 405
 
7.4%
. 256
 
4.7%
) 247
 
4.5%
( 246
 
4.5%
: 108
 
2.0%
8 85
 
1.6%
9 75
 
1.4%
Other values (13) 454
 
8.3%
Latin
ValueCountFrequency (%)
l 16
38.1%
m 16
38.1%
o 6
 
14.3%
O 4
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10859
66.3%
ASCII 5484
33.5%
CJK Compat 26
 
0.2%
Compat Jamo 12
 
0.1%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2528
46.1%
0 558
 
10.2%
2 508
 
9.3%
1 405
 
7.4%
. 256
 
4.7%
) 247
 
4.5%
( 246
 
4.5%
: 108
 
2.0%
8 85
 
1.5%
9 75
 
1.4%
Other values (14) 468
 
8.5%
Hangul
ValueCountFrequency (%)
위 496
 
4.6%
이 452
 
4.2%
생 364
 
3.4%
업 356
 
3.3%
미 334
 
3.1%
수 309
 
2.8%
년 299
 
2.8%
육 276
 
2.5%
교 276
 
2.5%
소 209
 
1.9%
Other values (255) 7488
69.0%
CJK Compat
ValueCountFrequency (%)
㎖ 26
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅇ 12
100.0%
None
ValueCountFrequency (%)
」 1
50.0%
「 1
50.0%

처분내용
Text

Distinct120
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
2024-05-11T03:03:02.070639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length48
Mean length7.9136164
Min length2

Characters and Unicode

Total characters5405
Distinct characters99
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)12.4%

Sample

1st row과징금부과
2nd row영업정지
3rd row영업정지 2월을 갈음한 과징금 1,800,000원
4th row과징금1,800,000원
5th row영업소폐쇄
ValueCountFrequency (%)
과태료부과 179
18.0%
경고 110
 
11.1%
개선명령 84
 
8.5%
영업소폐쇄 64
 
6.4%
영업정지 59
 
5.9%
과징금부과 55
 
5.5%
부과 38
 
3.8%
과징금 32
 
3.2%
과태료 32
 
3.2%
및 32
 
3.2%
Other values (138) 309
31.1%
2024-05-11T03:03:03.244398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 647
 
12.0%
0 364
 
6.7%
부 317
 
5.9%
313
 
5.8%
료 235
 
4.3%
태 235
 
4.3%
업 199
 
3.7%
영 188
 
3.5%
2 174
 
3.2%
1 127
 
2.3%
Other values (89) 2606
48.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3818
70.6%
Decimal Number 879
 
16.3%
Space Separator 313
 
5.8%
Other Punctuation 176
 
3.3%
Close Punctuation 100
 
1.9%
Open Punctuation 99
 
1.8%
Math Symbol 19
 
0.4%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 647
16.9%
부 317
 
8.3%
료 235
 
6.2%
태 235
 
6.2%
업 199
 
5.2%
영 188
 
4.9%
경 122
 
3.2%
지 119
 
3.1%
고 116
 
3.0%
소 112
 
2.9%
Other values (69) 1528
40.0%
Decimal Number
ValueCountFrequency (%)
0 364
41.4%
2 174
19.8%
1 127
 
14.4%
8 36
 
4.1%
3 35
 
4.0%
6 34
 
3.9%
9 31
 
3.5%
5 27
 
3.1%
7 26
 
3.0%
4 25
 
2.8%
Other Punctuation
ValueCountFrequency (%)
. 87
49.4%
, 85
48.3%
% 3
 
1.7%
? 1
 
0.6%
Math Symbol
ValueCountFrequency (%)
~ 16
84.2%
→ 3
 
15.8%
Space Separator
ValueCountFrequency (%)
313
100.0%
Close Punctuation
ValueCountFrequency (%)
) 100
100.0%
Open Punctuation
ValueCountFrequency (%)
( 99
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3818
70.6%
Common 1586
29.3%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 647
16.9%
부 317
 
8.3%
료 235
 
6.2%
태 235
 
6.2%
업 199
 
5.2%
영 188
 
4.9%
경 122
 
3.2%
지 119
 
3.1%
고 116
 
3.0%
소 112
 
2.9%
Other values (69) 1528
40.0%
Common
ValueCountFrequency (%)
0 364
23.0%
313
19.7%
2 174
11.0%
1 127
 
8.0%
) 100
 
6.3%
( 99
 
6.2%
. 87
 
5.5%
, 85
 
5.4%
8 36
 
2.3%
3 35
 
2.2%
Other values (9) 166
10.5%
Latin
ValueCountFrequency (%)
X 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3814
70.6%
ASCII 1584
29.3%
Compat Jamo 4
 
0.1%
Arrows 3
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 647
17.0%
부 317
 
8.3%
료 235
 
6.2%
태 235
 
6.2%
업 199
 
5.2%
영 188
 
4.9%
경 122
 
3.2%
지 119
 
3.1%
고 116
 
3.0%
소 112
 
2.9%
Other values (68) 1524
40.0%
ASCII
ValueCountFrequency (%)
0 364
23.0%
313
19.8%
2 174
11.0%
1 127
 
8.0%
) 100
 
6.3%
( 99
 
6.2%
. 87
 
5.5%
, 85
 
5.4%
8 36
 
2.3%
3 35
 
2.2%
Other values (9) 164
10.4%
Compat Jamo
ValueCountFrequency (%)
ㆍ 4
100.0%
Arrows
ValueCountFrequency (%)
→ 3
100.0%

처분기간
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)10.6%
Missing598
Missing (%)87.6%
Infinite0
Infinite (%)0.0%
Mean9.2941176
Minimum0
Maximum61
Zeros57
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-05-11T03:03:03.664355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile61
Maximum61
Range61
Interquartile range (IQR)5

Descriptive statistics

Standard deviation19.593381
Coefficient of variation (CV)2.1081485
Kurtosis3.0933754
Mean9.2941176
Median Absolute Deviation (MAD)0
Skewness2.1658351
Sum790
Variance383.90056
MonotonicityNot monotonic
2024-05-11T03:03:04.101143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 57
 
8.3%
61 9
 
1.3%
5 8
 
1.2%
10 5
 
0.7%
14 2
 
0.3%
15 1
 
0.1%
20 1
 
0.1%
60 1
 
0.1%
28 1
 
0.1%
(Missing) 598
87.6%
ValueCountFrequency (%)
0 57
8.3%
5 8
 
1.2%
10 5
 
0.7%
14 2
 
0.3%
15 1
 
0.1%
20 1
 
0.1%
28 1
 
0.1%
60 1
 
0.1%
61 9
 
1.3%
ValueCountFrequency (%)
61 9
 
1.3%
60 1
 
0.1%
28 1
 
0.1%
20 1
 
0.1%
15 1
 
0.1%
14 2
 
0.3%
10 5
 
0.7%
5 8
 
1.2%
0 57
8.3%

영업장면적(㎡)
Real number (ℝ)

MISSING  ZEROS 

Distinct290
Distinct (%)47.2%
Missing68
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean403.6699
Minimum0
Maximum6907.83
Zeros19
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-05-11T03:03:04.582378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.934
Q131.5
median79.4
Q3230
95-th percentile1563
Maximum6907.83
Range6907.83
Interquartile range (IQR)198.5

Descriptive statistics

Standard deviation1073.3684
Coefficient of variation (CV)2.6590252
Kurtosis23.678464
Mean403.6699
Median Absolute Deviation (MAD)56.4
Skewness4.6994596
Sum248256.99
Variance1152119.8
MonotonicityNot monotonic
2024-05-11T03:03:05.002548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 19
 
2.8%
6907.83 11
 
1.6%
3228.74 11
 
1.6%
33.0 11
 
1.6%
24.0 10
 
1.5%
401.08 10
 
1.5%
1430.78 9
 
1.3%
20.0 9
 
1.3%
15.0 8
 
1.2%
100.0 7
 
1.0%
Other values (280) 510
74.7%
(Missing) 68
 
10.0%
ValueCountFrequency (%)
0.0 19
2.8%
2.5 1
 
0.1%
5.36 1
 
0.1%
6.82 1
 
0.1%
9.9 2
 
0.3%
10.39 1
 
0.1%
11.06 1
 
0.1%
11.18 1
 
0.1%
11.6 1
 
0.1%
12.0 1
 
0.1%
ValueCountFrequency (%)
6907.83 11
1.6%
5568.11 1
 
0.1%
3790.5 2
 
0.3%
3228.74 11
1.6%
3142.93 2
 
0.3%
2484.97 1
 
0.1%
2462.03 1
 
0.1%
1563.0 6
0.9%
1455.03 3
 
0.4%
1430.78 9
1.3%

Interactions

2024-05-11T03:02:35.143897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:28.337750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:29.782661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:32.039531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:33.550650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:35.580239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:28.647586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:30.382899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:32.441568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:33.879213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:35.966063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:28.940931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:30.906729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:32.718774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:34.254670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:36.401369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:29.222482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:31.252495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:32.980108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:34.531830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:36.759562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:29.471337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:31.634104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:33.258529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:02:34.816303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T03:03:05.360201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분일자업종명업태명지도점검일자위반일자처분기간영업장면적(㎡)
처분일자1.0000.5970.5590.9970.9870.6850.227
업종명0.5971.0000.9850.5830.6200.6720.383
업태명0.5590.9851.0000.5480.5880.5650.716
지도점검일자0.9970.5830.5481.0000.9930.7290.206
위반일자0.9870.6200.5880.9931.0000.5020.129
처분기간0.6850.6720.5650.7290.5021.0000.561
영업장면적(㎡)0.2270.3830.7160.2060.1290.5611.000
2024-05-11T03:03:05.804580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업태명
업종명1.0000.704
업태명0.7041.000
2024-05-11T03:03:06.149262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분일자지도점검일자위반일자처분기간영업장면적(㎡)업종명업태명
처분일자1.0000.9960.9880.187-0.1490.2690.245
지도점검일자0.9961.0000.9910.135-0.1430.2600.238
위반일자0.9880.9911.0000.197-0.1400.2830.262
처분기간0.1870.1350.1971.000-0.1390.2980.351
영업장면적(㎡)-0.149-0.143-0.140-0.1391.0000.1700.397
업종명0.2690.2600.2830.2980.1701.0000.704
업태명0.2450.2380.2620.3510.3970.7041.000

Missing values

2024-05-11T03:02:37.366594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T03:02:38.571936image/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.
2024-05-11T03:02:39.177588image/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(㎡)
0304000020091214002숙박업(일반)여관업대원장여관<NA>서울특별시 광진구 화양동 115번지 8호20090721처분확정과징금부과공중위생관리법 제11조20090721청소년 이성 혼숙행위(광진경찰서장으로부터 2009.7.21경 통보 받음)과징금부과<NA>52.82
1304000020101210002숙박업(일반)여관업대원장여관<NA>서울특별시 광진구 화양동 115번지 8호20100907처분확정영업정지공중위생관리법 제11조20100907숙박자에게 성매매알선등 행위영업정지<NA>52.82
23040000200411054숙박업(일반)여관업동궁여관<NA>서울특별시 광진구 자양동 236번지 64호20040619처분확정영업정지 2월을 갈음한 과징금 1,800,000원공중위생관리법 제11조,제12조20040619청소년 이성혼숙 장소제공영업정지 2월을 갈음한 과징금 1,800,000원068.0
33040000200404278숙박업(일반)여관업도원여관<NA>서울특별시 광진구 중곡동 196번지 11호20040315처분확정과징금1,800,000원공중위생관리법 제11조, 제12조20040315청소년 남녀 혼숙위반과징금1,800,000원050.0
4304000020080905008숙박업(일반)여관업도원여관<NA>서울특별시 광진구 중곡동 196번지 11호20080812처분확정영업소폐쇄공중위생관리법제3조20080812폐업신고 없이 시설물멸실영업소폐쇄<NA>50.0
5304000020080312013숙박업(일반)여관업세화장<NA>서울특별시 광진구 구의동 202번지 11호 ,1220080120처분확정과징금부과법11조20080120청소년에 대하여 이성혼숙행위 또는 장소 제공과징금부과<NA>100.0
6304000020090730013숙박업(일반)여관업세화장<NA>서울특별시 광진구 구의동 202번지 11호 ,1220090519처분확정영업정지 2월갈음 과징금 246만원 부과공중위생관리법 제11조20090519청소년 이성혼숙 행위영업정지 2월갈음 과징금 246만원 부과<NA>100.0
7304000020070511020숙박업(일반)여관업황금장여관<NA>서울특별시 광진구 구의동 67번지 21호20070405처분확정영업정지공중위생관리법 제11조20070405숙박자에게 윤락행위 또는 음란행위를 하게 하거나 이를 알선 또는 제공한때영업정지<NA>126.01
8304000020220216020숙박업(일반)여관업황금모텔서울특별시 광진구 광나루로 517-5, (구의동)서울특별시 광진구 구의동 67번지 21호20220201처분확정과태료부과법 제82조제2항20220201재난배상책임보험에 가입하지 않은 기간이 10일 이하인 경우과태료부과<NA>126.01
9304000020220216020숙박업(일반)여관업황금모텔서울특별시 광진구 광나루로 517-5, (구의동)서울특별시 광진구 구의동 67번지 21호20220201처분확정과태료부과법 제82조제2항20220201재난배상책임보험에 가입하지 않은 기간이 10일 이하인 경우과태료부과<NA>126.01
시군구코드처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자행정처분상태처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)
6733040000202011102017-00001피부미용업, 네일미용업네일아트업꼼꼼네일서울특별시 광진구 능동로50길 14, 1층 (중곡동, 플러스홈)서울특별시 광진구 중곡동 32번지 1호20191231처분확정과태료부과법 제22조제2항제6호201912312019년도 법정위생교육 미이수과태료부과<NA>0.0
6743040000202011102017-00001피부미용업, 네일미용업네일아트업꼼꼼네일서울특별시 광진구 능동로50길 14, 1층 (중곡동, 플러스홈)서울특별시 광진구 중곡동 32번지 1호20191231처분확정과태료부과법 제22조제2항제6호201912312019년도 법정위생교육 미이수과태료부과<NA>0.0
6753040000202011102017-00001피부미용업, 네일미용업네일아트업꼼꼼네일서울특별시 광진구 능동로50길 14, 1층 (중곡동, 플러스홈)서울특별시 광진구 중곡동 32번지 1호20191231처분확정과태료부과법 제22조제2항제6호201912312019년도 법정위생교육 미이수과태료부과<NA>0.0
6763040000202011102019-113피부미용업, 네일미용업네일아트업네일 모해서울특별시 광진구 구의로 29, 1층 (구의동)서울특별시 광진구 구의동 222번지 8호 1층20191231처분확정과태료부과법 제22조제2항제6호201912312019년도 법정위생교육 미이수과태료부과<NA>25.0
6773040000202111102017-0002화장ㆍ분장 미용업메이크업업스타시티아트홀미용실서울특별시 광진구 능동로 110, (화양동)서울특별시 광진구 화양동 4번지 20호 스타시티 5층20210630처분확정과태료부과법 제22조제2항제6호202106302020년도 위생교육 미이수과태료부과<NA>20.5
6783040000202305081056일반미용업, 화장ㆍ분장 미용업일반미용업메이크업하우스서울특별시 광진구 능동로 379, 2층 202호 (중곡동, 금용빌딩)서울특별시 광진구 중곡동 165번지 9호 금용빌딩20230417처분확정과태료부과법 제22조제2항제6호202212312022년도 공중위생업 법정 위생교육 미이수과태료부과<NA>109.45
6793040000201911202016-01피부미용업, 화장ㆍ분장 미용업피부미용업뷰티박스서울특별시 광진구 아차산로51길 96, 1층 (구의동)서울특별시 광진구 구의동 248번지 85호 1층20181231처분확정과태료부과법 제17조201812312018년 법정위생교육 미이수과태료부과<NA>22.0
6803040000202011102019-109네일미용업, 화장ㆍ분장 미용업네일아트업하라네일 앤 아이래쉬(HARA Nail & Eyelash)서울특별시 광진구 군자로 157, 1층 (군자동)서울특별시 광진구 군자동 53번지 13호 1층20191231처분확정과태료부과법 제22조제2항제6호201912312019년도 법정위생교육 미이수과태료부과<NA>28.3
6813040000202111102020-133네일미용업, 화장ㆍ분장 미용업네일아트업건대네일서울특별시 광진구 동일로20길 91, 1층 (자양동)서울특별시 광진구 자양동 7번지 9호20210630처분확정과태료부과법 제22조제2항제6호202106302020년도 위생교육 미이수과태료부과<NA>21.0
6823040000201910232017-011피부미용업, 네일미용업, 화장ㆍ분장 미용업피부미용업아이웰 뷰티서울특별시 광진구 능동로38길 11, 아지(AZ)0213 3층 (중곡동)서울특별시 광진구 중곡동 158번지 22호 아지(AZ)0213 3층20181231처분확정과태료부과법 제17조201812312018년 법정위생교육 미이수과태료부과<NA>55.53

Duplicate rows

Most frequently occurring

시군구코드처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자행정처분상태처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)# duplicates
273040000202011102010-00032피부미용업피부미용업헤라뷰티케어서울특별시 광진구 광나루로56길 85, 지하1층 75,76호 (구의동)서울특별시 광진구 구의동 546번지 4호 B 지하1층-75,7620191231처분확정과태료부과법 제22조제2항제6호201912312019년도 법정위생교육 미이수과태료부과<NA>66.113
283040000202011102017-00001피부미용업, 네일미용업네일아트업꼼꼼네일서울특별시 광진구 능동로50길 14, 1층 (중곡동, 플러스홈)서울특별시 광진구 중곡동 32번지 1호20191231처분확정과태료부과법 제22조제2항제6호201912312019년도 법정위생교육 미이수과태료부과<NA>0.03
303040000202305082018-012일반미용업일반미용업노이벨라(noibella)서울특별시 광진구 능동로11길 8-5, 7층 (화양동)서울특별시 광진구 화양동 5번지 16호 7층20230417처분확정과태료부과법 제22조제2항제6호202212312022년도 공중위생업 법정 위생교육 미이수과태료 부과과태료부과<NA>67.133
323040000202305082021-125일반미용업일반미용업한결살롱서울특별시 광진구 광나루로39길 11, 2층 216호 (구의동, 구의자이르네)서울특별시 광진구 구의동 671번지 구의자이르네20230417처분확정과태료부과법 제22조제2항제6호202212312022년도 공중위생업 법정 위생교육 미이수과태료부과<NA>46.333
0304000020050719<NA>이용업일반이용업금광이용원<NA>서울특별시 광진구 구의동 243번지 7호20040613처분확정면허정지2월공중위생관리법제11조20050611손님에게 성매매 알선면허정지2월6146.92
1304000020050719<NA>이용업일반이용업금광이용원<NA>서울특별시 광진구 구의동 243번지 7호20040613처분확정영업정지2월공중위생관리법제11조20050611손님에게 성매매 알선영업정지2월6146.92
2304000020060519<NA>미용업일반미용업최정희 미용피부관리<NA>서울특별시 광진구 광장동 145번지 8호20060519처분확정경고공중위생관리법 제22조20060401위생교육미필(기존영업자)경고055.972
3304000020060519<NA>미용업일반미용업최정희 미용피부관리<NA>서울특별시 광진구 광장동 145번지 8호20060519처분확정과태료 200,000원공중위생관리법 제22조20060401위생교육미필(기존영업자)과태료 200,000원055.972
4304000020060519<NA>세탁업일반세탁업대성세탁소<NA>서울특별시 광진구 구의동 636번지 5호20060519처분확정경고공중위생관리법 제22조20060401위생교육미필(기존영업자)경고0<NA>2
5304000020060519<NA>세탁업일반세탁업대성세탁소<NA>서울특별시 광진구 구의동 636번지 5호20060519처분확정과태료 200,000원공중위생관리법 제22조20060401위생교육미필(기존영업자)과태료 200,000원0<NA>2