Overview

Dataset statistics

Number of variables17
Number of observations586
Missing cells912
Missing cells (%)9.2%
Duplicate rows35
Duplicate rows (%)6.0%
Total size in memory81.4 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(㎡)
Author도봉구
URLhttps://data.seoul.go.kr/dataList/OA-10066/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
행정처분상태 has constant value ""Constant
Dataset has 35 (6.0%) duplicate rowsDuplicates
처분일자 is highly overall correlated with 지도점검일자 and 2 other fieldsHigh correlation
지도점검일자 is highly overall correlated with 처분일자 and 2 other fieldsHigh correlation
위반일자 is highly overall correlated with 처분일자 and 2 other fieldsHigh correlation
처분기간 is highly overall correlated with 처분일자 and 2 other fieldsHigh correlation
업종명 is highly overall correlated with 업태명High correlation
업태명 is highly overall correlated with 업종명High correlation
소재지도로명 has 424 (72.4%) missing valuesMissing
처분기간 has 472 (80.5%) missing valuesMissing
영업장면적(㎡) has 13 (2.2%) missing valuesMissing
처분기간 has 95 (16.2%) zerosZeros
영업장면적(㎡) has 12 (2.0%) zerosZeros

Reproduction

Analysis started2024-05-18 07:46:37.503718
Analysis finished2024-05-18 07:46:49.683996
Duration12.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
3090000
586 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3090000
2nd row3090000
3rd row3090000
4th row3090000
5th row3090000

Common Values

ValueCountFrequency (%)
3090000 586
100.0%

Length

2024-05-18T16:46:49.982963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T16:46:50.454973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3090000 586
100.0%

처분일자
Real number (ℝ)

HIGH CORRELATION 

Distinct226
Distinct (%)38.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20057529
Minimum19960905
Maximum20240314
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-05-18T16:46:50.899725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19960905
5-th percentile19961220
Q119973497
median20041111
Q320121184
95-th percentile20200709
Maximum20240314
Range279409
Interquartile range (IQR)147686.25

Descriptive statistics

Standard deviation81358.6
Coefficient of variation (CV)0.0040562625
Kurtosis-1.1545247
Mean20057529
Median Absolute Deviation (MAD)70148
Skewness0.39559258
Sum1.1753712 × 1010
Variance6.6192218 × 109
MonotonicityNot monotonic
2024-05-18T16:46:51.458458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19961220 28
 
4.8%
19990527 26
 
4.4%
19970829 18
 
3.1%
19990726 18
 
3.1%
19971120 16
 
2.7%
19990323 13
 
2.2%
19990601 13
 
2.2%
20090722 12
 
2.0%
19970515 11
 
1.9%
20140124 10
 
1.7%
Other values (216) 421
71.8%
ValueCountFrequency (%)
19960905 4
 
0.7%
19961031 3
 
0.5%
19961127 1
 
0.2%
19961210 8
 
1.4%
19961220 28
4.8%
19970121 1
 
0.2%
19970303 3
 
0.5%
19970317 1
 
0.2%
19970318 1
 
0.2%
19970322 1
 
0.2%
ValueCountFrequency (%)
20240314 1
 
0.2%
20240216 1
 
0.2%
20231208 1
 
0.2%
20230921 3
0.5%
20230912 1
 
0.2%
20230908 1
 
0.2%
20230508 1
 
0.2%
20230504 1
 
0.2%
20230420 1
 
0.2%
20230316 1
 
0.2%

교부번호
Text

Distinct347
Distinct (%)59.4%
Missing2
Missing (%)0.3%
Memory size4.7 KiB
2024-05-18T16:46:52.148174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length8.1917808
Min length1

Characters and Unicode

Total characters4784
Distinct characters17
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

Unique253 ?
Unique (%)43.3%

Sample

1st row23200410600029
2nd row1
3rd row23200410700009
4th row43
5th row43
ValueCountFrequency (%)
131 14
 
2.4%
48 10
 
1.7%
44 9
 
1.5%
34 9
 
1.5%
40 8
 
1.4%
54 7
 
1.2%
30 7
 
1.2%
23 7
 
1.2%
17 7
 
1.2%
31 6
 
1.0%
Other values (337) 500
85.6%
2024-05-18T16:46:53.111776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1706
35.7%
2 763
15.9%
1 601
 
12.6%
4 538
 
11.2%
3 511
 
10.7%
5 173
 
3.6%
6 103
 
2.2%
7 103
 
2.2%
9 92
 
1.9%
8 86
 
1.8%
Other values (7) 108
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4676
97.7%
Other Letter 62
 
1.3%
Dash Punctuation 46
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1706
36.5%
2 763
16.3%
1 601
 
12.9%
4 538
 
11.5%
3 511
 
10.9%
5 173
 
3.7%
6 103
 
2.2%
7 103
 
2.2%
9 92
 
2.0%
8 86
 
1.8%
Other Letter
ValueCountFrequency (%)
피 22
35.5%
부 22
35.5%
종 6
 
9.7%
합 6
 
9.7%
일 3
 
4.8%
반 3
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4722
98.7%
Hangul 62
 
1.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1706
36.1%
2 763
16.2%
1 601
 
12.7%
4 538
 
11.4%
3 511
 
10.8%
5 173
 
3.7%
6 103
 
2.2%
7 103
 
2.2%
9 92
 
1.9%
8 86
 
1.8%
Hangul
ValueCountFrequency (%)
피 22
35.5%
부 22
35.5%
종 6
 
9.7%
합 6
 
9.7%
일 3
 
4.8%
반 3
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4722
98.7%
Hangul 62
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1706
36.1%
2 763
16.2%
1 601
 
12.7%
4 538
 
11.4%
3 511
 
10.8%
5 173
 
3.7%
6 103
 
2.2%
7 103
 
2.2%
9 92
 
1.9%
8 86
 
1.8%
Hangul
ValueCountFrequency (%)
피 22
35.5%
부 22
35.5%
종 6
 
9.7%
합 6
 
9.7%
일 3
 
4.8%
반 3
 
4.8%

업종명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
목욕장업
138 
이용업
111 
미용업
92 
숙박업(일반)
72 
세탁업
57 
Other values (11)
116 

Length

Max length16
Median length12
Mean length4.3276451
Min length3

Unique

Unique2 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
목욕장업 138
23.5%
이용업 111
18.9%
미용업 92
15.7%
숙박업(일반) 72
12.3%
세탁업 57
9.7%
일반미용업 38
 
6.5%
피부미용업 29
 
4.9%
위생관리용역업 21
 
3.6%
종합미용업 7
 
1.2%
화장ㆍ분장 미용업 6
 
1.0%
Other values (6) 15
 
2.6%

Length

2024-05-18T16:46:53.606425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
목욕장업 138
23.0%
이용업 111
18.5%
미용업 99
16.5%
숙박업(일반 72
12.0%
세탁업 57
9.5%
일반미용업 42
 
7.0%
피부미용업 35
 
5.8%
위생관리용역업 21
 
3.5%
종합미용업 7
 
1.2%
화장ㆍ분장 7
 
1.2%
Other values (3) 10
 
1.7%

업태명
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
일반미용업
132 
일반이용업
110 
공동탕업
90 
일반세탁업
54 
여관업
52 
Other values (15)
148 

Length

Max length14
Median length5
Mean length5.4675768
Min length3

Unique

Unique5 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 132
22.5%
일반이용업 110
18.8%
공동탕업 90
15.4%
일반세탁업 54
9.2%
여관업 52
 
8.9%
공동탕업+찜질시설서비스영업 47
 
8.0%
피부미용업 37
 
6.3%
위생관리용역업 21
 
3.6%
여인숙업 8
 
1.4%
메이크업업 7
 
1.2%
Other values (10) 28
 
4.8%

Length

2024-05-18T16:46:54.064689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 132
22.4%
일반이용업 110
18.7%
공동탕업 90
15.3%
일반세탁업 54
9.2%
여관업 52
 
8.8%
공동탕업+찜질시설서비스영업 47
 
8.0%
피부미용업 37
 
6.3%
위생관리용역업 21
 
3.6%
여인숙업 8
 
1.4%
일반호텔 7
 
1.2%
Other values (11) 31
 
5.3%

업소명
Text

Distinct344
Distinct (%)58.7%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
2024-05-18T16:46:54.858099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length15
Mean length4.9010239
Min length1

Characters and Unicode

Total characters2872
Distinct characters329
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique233 ?
Unique (%)39.8%

Sample

1st row도원장
2nd row경남
3rd row삼산
4th row삼산
5th row삼산
ValueCountFrequency (%)
아영 12
 
1.9%
월드사우나 10
 
1.6%
한성탕 7
 
1.1%
벽산불한증사우나 7
 
1.1%
에스테덤 6
 
1.0%
뷰티프라자(창동점 6
 
1.0%
뉴스타 6
 
1.0%
현대사우나 6
 
1.0%
정한탕 6
 
1.0%
왕미래이발관 6
 
1.0%
Other values (355) 551
88.4%
2024-05-18T16:46:56.204773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 104
 
3.6%
사 93
 
3.2%
탕 76
 
2.6%
관 67
 
2.3%
우 66
 
2.3%
발 65
 
2.3%
나 63
 
2.2%
미 53
 
1.8%
대 53
 
1.8%
헤 53
 
1.8%
Other values (319) 2179
75.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2698
93.9%
Lowercase Letter 45
 
1.6%
Space Separator 37
 
1.3%
Open Punctuation 24
 
0.8%
Close Punctuation 24
 
0.8%
Uppercase Letter 21
 
0.7%
Decimal Number 12
 
0.4%
Other Punctuation 9
 
0.3%
Math Symbol 1
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 104
 
3.9%
사 93
 
3.4%
탕 76
 
2.8%
관 67
 
2.5%
우 66
 
2.4%
발 65
 
2.4%
나 63
 
2.3%
미 53
 
2.0%
대 53
 
2.0%
헤 53
 
2.0%
Other values (276) 2005
74.3%
Lowercase Letter
ValueCountFrequency (%)
e 6
13.3%
t 6
13.3%
o 5
11.1%
u 4
8.9%
a 4
8.9%
l 4
8.9%
b 3
6.7%
y 3
6.7%
r 2
 
4.4%
n 2
 
4.4%
Other values (6) 6
13.3%
Uppercase Letter
ValueCountFrequency (%)
O 4
19.0%
K 3
14.3%
M 3
14.3%
S 2
9.5%
C 2
9.5%
H 2
9.5%
W 1
 
4.8%
G 1
 
4.8%
A 1
 
4.8%
N 1
 
4.8%
Decimal Number
ValueCountFrequency (%)
1 3
25.0%
9 2
16.7%
8 2
16.7%
3 1
 
8.3%
5 1
 
8.3%
2 1
 
8.3%
4 1
 
8.3%
0 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
？ 4
44.4%
& 3
33.3%
. 2
22.2%
Space Separator
ValueCountFrequency (%)
37
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Math Symbol
ValueCountFrequency (%)
= 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2698
93.9%
Common 108
 
3.8%
Latin 66
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 104
 
3.9%
사 93
 
3.4%
탕 76
 
2.8%
관 67
 
2.5%
우 66
 
2.4%
발 65
 
2.4%
나 63
 
2.3%
미 53
 
2.0%
대 53
 
2.0%
헤 53
 
2.0%
Other values (276) 2005
74.3%
Latin
ValueCountFrequency (%)
e 6
 
9.1%
t 6
 
9.1%
o 5
 
7.6%
u 4
 
6.1%
a 4
 
6.1%
O 4
 
6.1%
l 4
 
6.1%
b 3
 
4.5%
K 3
 
4.5%
y 3
 
4.5%
Other values (17) 24
36.4%
Common
ValueCountFrequency (%)
37
34.3%
( 24
22.2%
) 24
22.2%
？ 4
 
3.7%
& 3
 
2.8%
1 3
 
2.8%
. 2
 
1.9%
9 2
 
1.9%
8 2
 
1.9%
3 1
 
0.9%
Other values (6) 6
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2698
93.9%
ASCII 170
 
5.9%
None 4
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 104
 
3.9%
사 93
 
3.4%
탕 76
 
2.8%
관 67
 
2.5%
우 66
 
2.4%
발 65
 
2.4%
나 63
 
2.3%
미 53
 
2.0%
대 53
 
2.0%
헤 53
 
2.0%
Other values (276) 2005
74.3%
ASCII
ValueCountFrequency (%)
37
21.8%
( 24
14.1%
) 24
14.1%
e 6
 
3.5%
t 6
 
3.5%
o 5
 
2.9%
u 4
 
2.4%
a 4
 
2.4%
O 4
 
2.4%
l 4
 
2.4%
Other values (32) 52
30.6%
None
ValueCountFrequency (%)
？ 4
100.0%

소재지도로명
Text

MISSING 

Distinct97
Distinct (%)59.9%
Missing424
Missing (%)72.4%
Memory size4.7 KiB
2024-05-18T16:46:56.990155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length46.5
Mean length31.462963
Min length23

Characters and Unicode

Total characters5097
Distinct characters109
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)39.5%

Sample

1st row서울특별시 도봉구 도봉로135길 18, (쌍문동)
2nd row서울특별시 도봉구 시루봉로29길 6, 도봉1동 (도봉동)
3rd row서울특별시 도봉구 시루봉로29길 6, 도봉1동 (도봉동)
4th row서울특별시 도봉구 도봉로 417, (쌍문동)
5th row서울특별시 도봉구 방학로6길 21, (방학동)
ValueCountFrequency (%)
서울특별시 162
 
16.9%
도봉구 162
 
16.9%
창동 52
 
5.4%
방학동 29
 
3.0%
쌍문동 28
 
2.9%
도봉동 26
 
2.7%
지상1층 16
 
1.7%
도봉로 15
 
1.6%
상가동 13
 
1.4%
마들로 11
 
1.1%
Other values (193) 443
46.3%
2024-05-18T16:46:58.225475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
795
 
15.6%
도 261
 
5.1%
봉 260
 
5.1%
, 216
 
4.2%
1 204
 
4.0%
동 204
 
4.0%
시 174
 
3.4%
) 168
 
3.3%
( 168
 
3.3%
울 165
 
3.2%
Other values (99) 2482
48.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2911
57.1%
Decimal Number 810
 
15.9%
Space Separator 795
 
15.6%
Other Punctuation 219
 
4.3%
Close Punctuation 168
 
3.3%
Open Punctuation 168
 
3.3%
Dash Punctuation 21
 
0.4%
Uppercase Letter 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 261
 
9.0%
봉 260
 
8.9%
동 204
 
7.0%
시 174
 
6.0%
울 165
 
5.7%
서 163
 
5.6%
구 162
 
5.6%
별 162
 
5.6%
특 162
 
5.6%
로 158
 
5.4%
Other values (82) 1040
35.7%
Decimal Number
ValueCountFrequency (%)
1 204
25.2%
2 106
13.1%
0 85
10.5%
6 75
 
9.3%
5 68
 
8.4%
3 68
 
8.4%
7 63
 
7.8%
9 50
 
6.2%
4 50
 
6.2%
8 41
 
5.1%
Other Punctuation
ValueCountFrequency (%)
, 216
98.6%
@ 3
 
1.4%
Space Separator
ValueCountFrequency (%)
795
100.0%
Close Punctuation
ValueCountFrequency (%)
) 168
100.0%
Open Punctuation
ValueCountFrequency (%)
( 168
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2911
57.1%
Common 2181
42.8%
Latin 5
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 261
 
9.0%
봉 260
 
8.9%
동 204
 
7.0%
시 174
 
6.0%
울 165
 
5.7%
서 163
 
5.6%
구 162
 
5.6%
별 162
 
5.6%
특 162
 
5.6%
로 158
 
5.4%
Other values (82) 1040
35.7%
Common
ValueCountFrequency (%)
795
36.5%
, 216
 
9.9%
1 204
 
9.4%
) 168
 
7.7%
( 168
 
7.7%
2 106
 
4.9%
0 85
 
3.9%
6 75
 
3.4%
5 68
 
3.1%
3 68
 
3.1%
Other values (6) 228
 
10.5%
Latin
ValueCountFrequency (%)
B 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2911
57.1%
ASCII 2186
42.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
795
36.4%
, 216
 
9.9%
1 204
 
9.3%
) 168
 
7.7%
( 168
 
7.7%
2 106
 
4.8%
0 85
 
3.9%
6 75
 
3.4%
5 68
 
3.1%
3 68
 
3.1%
Other values (7) 233
 
10.7%
Hangul
ValueCountFrequency (%)
도 261
 
9.0%
봉 260
 
8.9%
동 204
 
7.0%
시 174
 
6.0%
울 165
 
5.7%
서 163
 
5.6%
구 162
 
5.6%
별 162
 
5.6%
특 162
 
5.6%
로 158
 
5.4%
Other values (82) 1040
35.7%

소재지지번
Text

Distinct356
Distinct (%)60.8%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
2024-05-18T16:46:58.974249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length44
Mean length28.064846
Min length20

Characters and Unicode

Total characters16446
Distinct characters122
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

Unique245 ?
Unique (%)41.8%

Sample

1st row서울특별시 도봉구 도봉동 산 574번지 21호
2nd row서울특별시 도봉구 쌍문동 23번지 2호
3rd row서울특별시 도봉구 도봉동 산 95번지 35호
4th row서울특별시 도봉구 도봉동 95번지 35호
5th row서울특별시 도봉구 도봉동 95번지 35호
ValueCountFrequency (%)
서울특별시 586
17.3%
도봉구 586
17.3%
산 264
 
7.8%
창동 200
 
5.9%
쌍문동 137
 
4.0%
도봉동 131
 
3.9%
방학동 118
 
3.5%
1호 51
 
1.5%
2호 39
 
1.2%
0호 34
 
1.0%
Other values (370) 1239
36.6%
2024-05-18T16:47:00.418136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4370
26.6%
도 719
 
4.4%
봉 718
 
4.4%
지 677
 
4.1%
동 629
 
3.8%
울 589
 
3.6%
서 587
 
3.6%
번 587
 
3.6%
시 587
 
3.6%
구 587
 
3.6%
Other values (112) 6396
38.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9108
55.4%
Space Separator 4370
26.6%
Decimal Number 2848
 
17.3%
Dash Punctuation 45
 
0.3%
Uppercase Letter 23
 
0.1%
Other Punctuation 20
 
0.1%
Close Punctuation 15
 
0.1%
Open Punctuation 15
 
0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 719
 
7.9%
봉 718
 
7.9%
지 677
 
7.4%
동 629
 
6.9%
울 589
 
6.5%
서 587
 
6.4%
번 587
 
6.4%
시 587
 
6.4%
구 587
 
6.4%
특 586
 
6.4%
Other values (90) 2842
31.2%
Decimal Number
ValueCountFrequency (%)
1 478
16.8%
2 398
14.0%
6 344
12.1%
3 326
11.4%
0 313
11.0%
5 255
9.0%
4 206
7.2%
7 199
7.0%
8 196
6.9%
9 133
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
T 6
26.1%
P 6
26.1%
A 6
26.1%
B 5
21.7%
Other Punctuation
ValueCountFrequency (%)
, 12
60.0%
@ 8
40.0%
Math Symbol
ValueCountFrequency (%)
< 1
50.0%
> 1
50.0%
Space Separator
ValueCountFrequency (%)
4370
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9108
55.4%
Common 7315
44.5%
Latin 23
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 719
 
7.9%
봉 718
 
7.9%
지 677
 
7.4%
동 629
 
6.9%
울 589
 
6.5%
서 587
 
6.4%
번 587
 
6.4%
시 587
 
6.4%
구 587
 
6.4%
특 586
 
6.4%
Other values (90) 2842
31.2%
Common
ValueCountFrequency (%)
4370
59.7%
1 478
 
6.5%
2 398
 
5.4%
6 344
 
4.7%
3 326
 
4.5%
0 313
 
4.3%
5 255
 
3.5%
4 206
 
2.8%
7 199
 
2.7%
8 196
 
2.7%
Other values (8) 230
 
3.1%
Latin
ValueCountFrequency (%)
T 6
26.1%
P 6
26.1%
A 6
26.1%
B 5
21.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9108
55.4%
ASCII 7338
44.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4370
59.6%
1 478
 
6.5%
2 398
 
5.4%
6 344
 
4.7%
3 326
 
4.4%
0 313
 
4.3%
5 255
 
3.5%
4 206
 
2.8%
7 199
 
2.7%
8 196
 
2.7%
Other values (12) 253
 
3.4%
Hangul
ValueCountFrequency (%)
도 719
 
7.9%
봉 718
 
7.9%
지 677
 
7.4%
동 629
 
6.9%
울 589
 
6.5%
서 587
 
6.4%
번 587
 
6.4%
시 587
 
6.4%
구 587
 
6.4%
특 586
 
6.4%
Other values (90) 2842
31.2%

지도점검일자
Real number (ℝ)

HIGH CORRELATION 

Distinct231
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20056619
Minimum19960905
Maximum20240226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-05-18T16:47:00.886279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19960905
5-th percentile19961220
Q119973497
median20041018
Q320121017
95-th percentile20200406
Maximum20240226
Range279321
Interquartile range (IQR)147519.75

Descriptive statistics

Standard deviation80641.328
Coefficient of variation (CV)0.0040206841
Kurtosis-1.129229
Mean20056619
Median Absolute Deviation (MAD)70090
Skewness0.40709268
Sum1.1753179 × 1010
Variance6.5030237 × 109
MonotonicityNot monotonic
2024-05-18T16:47:01.559980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19961220 28
 
4.8%
19990527 26
 
4.4%
19970829 18
 
3.1%
19990726 18
 
3.1%
19971120 16
 
2.7%
19990601 13
 
2.2%
19990323 13
 
2.2%
20090519 12
 
2.0%
19970515 11
 
1.9%
20120112 10
 
1.7%
Other values (221) 421
71.8%
ValueCountFrequency (%)
19960905 4
 
0.7%
19961031 3
 
0.5%
19961127 1
 
0.2%
19961210 8
 
1.4%
19961220 28
4.8%
19970121 1
 
0.2%
19970303 3
 
0.5%
19970317 1
 
0.2%
19970318 1
 
0.2%
19970322 1
 
0.2%
ValueCountFrequency (%)
20240226 1
 
0.2%
20231010 1
 
0.2%
20230823 5
0.9%
20230403 1
 
0.2%
20230329 1
 
0.2%
20230216 1
 
0.2%
20230105 4
0.7%
20221212 1
 
0.2%
20220520 1
 
0.2%
20211022 1
 
0.2%

행정처분상태
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
처분확정
586 

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 586
100.0%

Length

2024-05-18T16:47:02.173270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T16:47:02.759403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
처분확정 586
100.0%

처분명
Text

Distinct132
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
2024-05-18T16:47:03.130753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length74
Median length46
Mean length9.1638225
Min length2

Characters and Unicode

Total characters5370
Distinct characters125
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

Unique85 ?
Unique (%)14.5%

Sample

1st row(경고)경고
2nd row과징금부과180만원(영업정지 2월 갈음)
3rd row영업정지3월(2002.4.12-7.11)
4th row영업정지2월을 과징금180만원으로 갈음(1일3만원*60일)=180만원
5th row영업정지 2월(2007.07.12~2007.09.11)
ValueCountFrequency (%)
경고 178
19.3%
개선명령 90
 
9.8%
영업정지 45
 
4.9%
과태료부과 44
 
4.8%
과태료 44
 
4.8%
및 35
 
3.8%
경고)경고 29
 
3.1%
과징금부과 24
 
2.6%
20만원 23
 
2.5%
16만원 20
 
2.2%
Other values (169) 389
42.2%
2024-05-18T16:47:04.825032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 363
 
6.8%
) 363
 
6.8%
338
 
6.3%
과 319
 
5.9%
경 286
 
5.3%
고 254
 
4.7%
0 181
 
3.4%
령 157
 
2.9%
명 157
 
2.9%
선 154
 
2.9%
Other values (115) 2798
52.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3556
66.2%
Decimal Number 624
 
11.6%
Open Punctuation 365
 
6.8%
Close Punctuation 365
 
6.8%
Space Separator 338
 
6.3%
Other Punctuation 101
 
1.9%
Math Symbol 12
 
0.2%
Dash Punctuation 9
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 319
 
9.0%
경 286
 
8.0%
고 254
 
7.1%
령 157
 
4.4%
명 157
 
4.4%
선 154
 
4.3%
개 154
 
4.3%
부 143
 
4.0%
원 133
 
3.7%
업 131
 
3.7%
Other values (93) 1668
46.9%
Decimal Number
ValueCountFrequency (%)
0 181
29.0%
2 139
22.3%
1 114
18.3%
6 45
 
7.2%
5 37
 
5.9%
3 31
 
5.0%
8 27
 
4.3%
7 20
 
3.2%
4 20
 
3.2%
9 10
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 62
61.4%
: 20
 
19.8%
, 14
 
13.9%
* 5
 
5.0%
Open Punctuation
ValueCountFrequency (%)
( 363
99.5%
[ 2
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 363
99.5%
] 2
 
0.5%
Math Symbol
ValueCountFrequency (%)
~ 8
66.7%
= 4
33.3%
Space Separator
ValueCountFrequency (%)
338
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3556
66.2%
Common 1814
33.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 319
 
9.0%
경 286
 
8.0%
고 254
 
7.1%
령 157
 
4.4%
명 157
 
4.4%
선 154
 
4.3%
개 154
 
4.3%
부 143
 
4.0%
원 133
 
3.7%
업 131
 
3.7%
Other values (93) 1668
46.9%
Common
ValueCountFrequency (%)
( 363
20.0%
) 363
20.0%
338
18.6%
0 181
10.0%
2 139
 
7.7%
1 114
 
6.3%
. 62
 
3.4%
6 45
 
2.5%
5 37
 
2.0%
3 31
 
1.7%
Other values (12) 141
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3556
66.2%
ASCII 1814
33.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 363
20.0%
) 363
20.0%
338
18.6%
0 181
10.0%
2 139
 
7.7%
1 114
 
6.3%
. 62
 
3.4%
6 45
 
2.5%
5 37
 
2.0%
3 31
 
1.7%
Other values (12) 141
 
7.8%
Hangul
ValueCountFrequency (%)
과 319
 
9.0%
경 286
 
8.0%
고 254
 
7.1%
령 157
 
4.4%
명 157
 
4.4%
선 154
 
4.3%
개 154
 
4.3%
부 143
 
4.0%
원 133
 
3.7%
업 131
 
3.7%
Other values (93) 1668
46.9%

법적근거
Text

Distinct101
Distinct (%)17.3%
Missing1
Missing (%)0.2%
Memory size4.7 KiB
2024-05-18T16:47:05.388019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length83
Median length57
Mean length10.688889
Min length3

Characters and Unicode

Total characters6253
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

Unique53 ?
Unique (%)9.1%

Sample

1st row공중위생법
2nd row공중위생관리법 제11조
3rd row공중위생관리법제11조제1항
4th row공중위생관리법 제11조 1항, 동법시행규칙 19조
5th row공중위생관리법 제11조 제1항
ValueCountFrequency (%)
공중위생법 261
24.0%
공중위생관리법 230
21.2%
제17조 56
 
5.2%
제11조 45
 
4.1%
법 44
 
4.1%
제4조 35
 
3.2%
제2항 32
 
2.9%
및 27
 
2.5%
동법시행규칙 27
 
2.5%
제4조제2항 23
 
2.1%
Other values (98) 306
28.2%
2024-05-18T16:47:06.350525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
법 625
10.0%
제 586
9.4%
위 543
8.7%
공 541
8.7%
생 537
8.6%
중 535
8.6%
501
 
8.0%
1 397
 
6.3%
조 390
 
6.2%
관 270
 
4.3%
Other values (58) 1328
21.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4813
77.0%
Decimal Number 897
 
14.3%
Space Separator 501
 
8.0%
Other Punctuation 38
 
0.6%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
법 625
13.0%
제 586
12.2%
위 543
11.3%
공 541
11.2%
생 537
11.2%
중 535
11.1%
조 390
8.1%
관 270
5.6%
리 269
5.6%
항 201
 
4.2%
Other values (44) 316
6.6%
Decimal Number
ValueCountFrequency (%)
1 397
44.3%
2 177
19.7%
4 110
 
12.3%
7 104
 
11.6%
9 35
 
3.9%
3 30
 
3.3%
0 20
 
2.2%
6 17
 
1.9%
8 5
 
0.6%
5 2
 
0.2%
Space Separator
ValueCountFrequency (%)
501
100.0%
Other Punctuation
ValueCountFrequency (%)
, 38
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4813
77.0%
Common 1440
 
23.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
법 625
13.0%
제 586
12.2%
위 543
11.3%
공 541
11.2%
생 537
11.2%
중 535
11.1%
조 390
8.1%
관 270
5.6%
리 269
5.6%
항 201
 
4.2%
Other values (44) 316
6.6%
Common
ValueCountFrequency (%)
501
34.8%
1 397
27.6%
2 177
 
12.3%
4 110
 
7.6%
7 104
 
7.2%
, 38
 
2.6%
9 35
 
2.4%
3 30
 
2.1%
0 20
 
1.4%
6 17
 
1.2%
Other values (4) 11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4813
77.0%
ASCII 1440
 
23.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
법 625
13.0%
제 586
12.2%
위 543
11.3%
공 541
11.2%
생 537
11.2%
중 535
11.1%
조 390
8.1%
관 270
5.6%
리 269
5.6%
항 201
 
4.2%
Other values (44) 316
6.6%
ASCII
ValueCountFrequency (%)
501
34.8%
1 397
27.6%
2 177
 
12.3%
4 110
 
7.6%
7 104
 
7.2%
, 38
 
2.6%
9 35
 
2.4%
3 30
 
2.1%
0 20
 
1.4%
6 17
 
1.2%
Other values (4) 11
 
0.8%

위반일자
Real number (ℝ)

HIGH CORRELATION 

Distinct225
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20056209
Minimum19960905
Maximum20240221
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-05-18T16:47:06.830215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19960905
5-th percentile19961220
Q119973497
median20041018
Q320121017
95-th percentile20200101
Maximum20240221
Range279316
Interquartile range (IQR)147519.75

Descriptive statistics

Standard deviation79984.767
Coefficient of variation (CV)0.0039880302
Kurtosis-1.1502967
Mean20056209
Median Absolute Deviation (MAD)70090
Skewness0.39290991
Sum1.1752939 × 1010
Variance6.3975629 × 109
MonotonicityNot monotonic
2024-05-18T16:47:07.277912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19961220 28
 
4.8%
19990527 26
 
4.4%
19970829 18
 
3.1%
19990726 18
 
3.1%
19971120 16
 
2.7%
19990601 13
 
2.2%
19990323 13
 
2.2%
20090519 12
 
2.0%
19970515 11
 
1.9%
20131220 10
 
1.7%
Other values (215) 421
71.8%
ValueCountFrequency (%)
19960905 4
 
0.7%
19961031 3
 
0.5%
19961127 1
 
0.2%
19961210 8
 
1.4%
19961220 28
4.8%
19970121 1
 
0.2%
19970303 3
 
0.5%
19970317 1
 
0.2%
19970318 1
 
0.2%
19970322 1
 
0.2%
ValueCountFrequency (%)
20240221 1
 
0.2%
20231005 1
 
0.2%
20230406 1
 
0.2%
20230329 1
 
0.2%
20230216 1
 
0.2%
20230105 4
0.7%
20221230 5
0.9%
20220825 1
 
0.2%
20220520 1
 
0.2%
20210725 1
 
0.2%

위반내용
Text

Distinct268
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
2024-05-18T16:47:07.791232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length107
Median length74
Mean length22.056314
Min length4

Characters and Unicode

Total characters12925
Distinct characters306
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique190 ?
Unique (%)32.4%

Sample

1st row(위생교육 미필)정기 위생교육 미필 1차
2nd row청소년 이성혼숙
3rd row청소년 남녀혼숙
4th row위반내용 : 청소년보호법위반(이성혼숙)-1차 위반법규 : 공중위생관리법제11조제1항 처분내용 : 영업정지2월을 과징금180만원으로 갈음(1일3만원*60일)=180만원
5th row청소년이성혼숙장소제공
ValueCountFrequency (%)
위생교육 214
 
9.4%
미필 104
 
4.6%
미필)위생교육미필 69
 
3.0%
위반 47
 
2.1%
영업자 38
 
1.7%
36
 
1.6%
수질기준 33
 
1.5%
욕조수 32
 
1.4%
욕수의 28
 
1.2%
기존영업자 25
 
1.1%
Other values (579) 1639
72.4%
2024-05-18T16:47:08.670422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1718
 
13.3%
위 526
 
4.1%
) 429
 
3.3%
( 428
 
3.3%
미 413
 
3.2%
생 349
 
2.7%
교 333
 
2.6%
육 333
 
2.6%
필 327
 
2.5%
기 255
 
2.0%
Other values (296) 7814
60.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9198
71.2%
Space Separator 1718
 
13.3%
Decimal Number 772
 
6.0%
Close Punctuation 442
 
3.4%
Open Punctuation 435
 
3.4%
Other Punctuation 271
 
2.1%
Uppercase Letter 36
 
0.3%
Dash Punctuation 19
 
0.1%
Other Symbol 16
 
0.1%
Math Symbol 12
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
위 526
 
5.7%
미 413
 
4.5%
생 349
 
3.8%
교 333
 
3.6%
육 333
 
3.6%
필 327
 
3.6%
기 255
 
2.8%
수 249
 
2.7%
업 198
 
2.2%
이 152
 
1.7%
Other values (255) 6063
65.9%
Decimal Number
ValueCountFrequency (%)
1 205
26.6%
0 197
25.5%
2 176
22.8%
6 42
 
5.4%
3 37
 
4.8%
4 32
 
4.1%
9 28
 
3.6%
8 24
 
3.1%
7 17
 
2.2%
5 14
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 92
33.9%
: 66
24.4%
, 49
18.1%
/ 33
 
12.2%
※ 19
 
7.0%
* 11
 
4.1%
; 1
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
U 9
25.0%
C 6
16.7%
T 6
16.7%
N 5
13.9%
L 5
13.9%
F 4
11.1%
V 1
 
2.8%
Close Punctuation
ValueCountFrequency (%)
) 429
97.1%
] 6
 
1.4%
」 4
 
0.9%
』 3
 
0.7%
Lowercase Letter
ValueCountFrequency (%)
m 2
33.3%
l 2
33.3%
o 1
16.7%
t 1
16.7%
Open Punctuation
ValueCountFrequency (%)
( 428
98.4%
「 4
 
0.9%
『 3
 
0.7%
Math Symbol
ValueCountFrequency (%)
= 7
58.3%
~ 4
33.3%
↓ 1
 
8.3%
Space Separator
ValueCountFrequency (%)
1718
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%
Other Symbol
ValueCountFrequency (%)
㎖ 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9198
71.2%
Common 3685
28.5%
Latin 42
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
위 526
 
5.7%
미 413
 
4.5%
생 349
 
3.8%
교 333
 
3.6%
육 333
 
3.6%
필 327
 
3.6%
기 255
 
2.8%
수 249
 
2.7%
업 198
 
2.2%
이 152
 
1.7%
Other values (255) 6063
65.9%
Common
ValueCountFrequency (%)
1718
46.6%
) 429
 
11.6%
( 428
 
11.6%
1 205
 
5.6%
0 197
 
5.3%
2 176
 
4.8%
. 92
 
2.5%
: 66
 
1.8%
, 49
 
1.3%
6 42
 
1.1%
Other values (20) 283
 
7.7%
Latin
ValueCountFrequency (%)
U 9
21.4%
C 6
14.3%
T 6
14.3%
N 5
11.9%
L 5
11.9%
F 4
9.5%
m 2
 
4.8%
l 2
 
4.8%
o 1
 
2.4%
t 1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9198
71.2%
ASCII 3677
 
28.4%
Punctuation 19
 
0.1%
CJK Compat 16
 
0.1%
None 14
 
0.1%
Arrows 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1718
46.7%
) 429
 
11.7%
( 428
 
11.6%
1 205
 
5.6%
0 197
 
5.4%
2 176
 
4.8%
. 92
 
2.5%
: 66
 
1.8%
, 49
 
1.3%
6 42
 
1.1%
Other values (24) 275
 
7.5%
Hangul
ValueCountFrequency (%)
위 526
 
5.7%
미 413
 
4.5%
생 349
 
3.8%
교 333
 
3.6%
육 333
 
3.6%
필 327
 
3.6%
기 255
 
2.8%
수 249
 
2.7%
업 198
 
2.2%
이 152
 
1.7%
Other values (255) 6063
65.9%
Punctuation
ValueCountFrequency (%)
※ 19
100.0%
CJK Compat
ValueCountFrequency (%)
㎖ 16
100.0%
None
ValueCountFrequency (%)
」 4
28.6%
「 4
28.6%
『 3
21.4%
』 3
21.4%
Arrows
ValueCountFrequency (%)
↓ 1
100.0%

처분내용
Text

Distinct132
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
2024-05-18T16:47:09.224440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length74
Median length46
Mean length9.1638225
Min length2

Characters and Unicode

Total characters5370
Distinct characters125
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

Unique85 ?
Unique (%)14.5%

Sample

1st row(경고)경고
2nd row과징금부과180만원(영업정지 2월 갈음)
3rd row영업정지3월(2002.4.12-7.11)
4th row영업정지2월을 과징금180만원으로 갈음(1일3만원*60일)=180만원
5th row영업정지 2월(2007.07.12~2007.09.11)
ValueCountFrequency (%)
경고 178
19.3%
개선명령 90
 
9.8%
영업정지 45
 
4.9%
과태료부과 44
 
4.8%
과태료 44
 
4.8%
및 35
 
3.8%
경고)경고 29
 
3.1%
과징금부과 24
 
2.6%
20만원 23
 
2.5%
16만원 20
 
2.2%
Other values (169) 389
42.2%
2024-05-18T16:47:10.433679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 363
 
6.8%
) 363
 
6.8%
338
 
6.3%
과 319
 
5.9%
경 286
 
5.3%
고 254
 
4.7%
0 181
 
3.4%
령 157
 
2.9%
명 157
 
2.9%
선 154
 
2.9%
Other values (115) 2798
52.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3556
66.2%
Decimal Number 624
 
11.6%
Open Punctuation 365
 
6.8%
Close Punctuation 365
 
6.8%
Space Separator 338
 
6.3%
Other Punctuation 101
 
1.9%
Math Symbol 12
 
0.2%
Dash Punctuation 9
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 319
 
9.0%
경 286
 
8.0%
고 254
 
7.1%
령 157
 
4.4%
명 157
 
4.4%
선 154
 
4.3%
개 154
 
4.3%
부 143
 
4.0%
원 133
 
3.7%
업 131
 
3.7%
Other values (93) 1668
46.9%
Decimal Number
ValueCountFrequency (%)
0 181
29.0%
2 139
22.3%
1 114
18.3%
6 45
 
7.2%
5 37
 
5.9%
3 31
 
5.0%
8 27
 
4.3%
7 20
 
3.2%
4 20
 
3.2%
9 10
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 62
61.4%
: 20
 
19.8%
, 14
 
13.9%
* 5
 
5.0%
Open Punctuation
ValueCountFrequency (%)
( 363
99.5%
[ 2
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 363
99.5%
] 2
 
0.5%
Math Symbol
ValueCountFrequency (%)
~ 8
66.7%
= 4
33.3%
Space Separator
ValueCountFrequency (%)
338
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3556
66.2%
Common 1814
33.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 319
 
9.0%
경 286
 
8.0%
고 254
 
7.1%
령 157
 
4.4%
명 157
 
4.4%
선 154
 
4.3%
개 154
 
4.3%
부 143
 
4.0%
원 133
 
3.7%
업 131
 
3.7%
Other values (93) 1668
46.9%
Common
ValueCountFrequency (%)
( 363
20.0%
) 363
20.0%
338
18.6%
0 181
10.0%
2 139
 
7.7%
1 114
 
6.3%
. 62
 
3.4%
6 45
 
2.5%
5 37
 
2.0%
3 31
 
1.7%
Other values (12) 141
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3556
66.2%
ASCII 1814
33.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 363
20.0%
) 363
20.0%
338
18.6%
0 181
10.0%
2 139
 
7.7%
1 114
 
6.3%
. 62
 
3.4%
6 45
 
2.5%
5 37
 
2.0%
3 31
 
1.7%
Other values (12) 141
 
7.8%
Hangul
ValueCountFrequency (%)
과 319
 
9.0%
경 286
 
8.0%
고 254
 
7.1%
령 157
 
4.4%
명 157
 
4.4%
선 154
 
4.3%
개 154
 
4.3%
부 143
 
4.0%
원 133
 
3.7%
업 131
 
3.7%
Other values (93) 1668
46.9%

처분기간
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)5.3%
Missing472
Missing (%)80.5%
Infinite0
Infinite (%)0.0%
Mean2.5263158
Minimum0
Maximum60
Zeros95
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-05-18T16:47:10.824002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10
Maximum60
Range60
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.5647184
Coefficient of variation (CV)2.9943677
Kurtosis31.499509
Mean2.5263158
Median Absolute Deviation (MAD)0
Skewness4.9673919
Sum288
Variance57.224965
MonotonicityNot monotonic
2024-05-18T16:47:11.262221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 95
 
16.2%
10 14
 
2.4%
30 2
 
0.3%
60 1
 
0.2%
13 1
 
0.2%
15 1
 
0.2%
(Missing) 472
80.5%
ValueCountFrequency (%)
0 95
16.2%
10 14
 
2.4%
13 1
 
0.2%
15 1
 
0.2%
30 2
 
0.3%
60 1
 
0.2%
ValueCountFrequency (%)
60 1
 
0.2%
30 2
 
0.3%
15 1
 
0.2%
13 1
 
0.2%
10 14
 
2.4%
0 95
16.2%

영업장면적(㎡)
Real number (ℝ)

MISSING  ZEROS 

Distinct283
Distinct (%)49.4%
Missing13
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean331.45087
Minimum0
Maximum4995.11
Zeros12
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-05-18T16:47:11.735157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.272
Q124.75
median72
Q3227.12
95-th percentile1800
Maximum4995.11
Range4995.11
Interquartile range (IQR)202.37

Descriptive statistics

Standard deviation738.26393
Coefficient of variation (CV)2.2273706
Kurtosis14.604585
Mean331.45087
Median Absolute Deviation (MAD)54.69
Skewness3.7005089
Sum189921.35
Variance545033.63
MonotonicityNot monotonic
2024-05-18T16:47:12.298715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.0 14
 
2.4%
0.0 12
 
2.0%
4010.0 10
 
1.7%
26.4 10
 
1.7%
1800.0 9
 
1.5%
132.0 8
 
1.4%
279.1 7
 
1.2%
544.95 7
 
1.2%
1567.0 7
 
1.2%
302.61 6
 
1.0%
Other values (273) 483
82.4%
(Missing) 13
 
2.2%
ValueCountFrequency (%)
0.0 12
2.0%
6.6 1
 
0.2%
7.32 2
 
0.3%
8.0 2
 
0.3%
8.23 1
 
0.2%
9.0 2
 
0.3%
9.36 1
 
0.2%
10.0 1
 
0.2%
10.23 1
 
0.2%
10.56 3
 
0.5%
ValueCountFrequency (%)
4995.11 1
 
0.2%
4010.0 10
1.7%
3971.26 1
 
0.2%
3694.0 2
 
0.3%
2686.29 1
 
0.2%
2280.0 2
 
0.3%
2145.0 3
 
0.5%
2093.0 5
0.9%
1920.4 1
 
0.2%
1800.0 9
1.5%

Interactions

2024-05-18T16:46:46.336993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:39.715858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:41.089188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:42.619735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:44.626475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:46.696509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:39.989639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:41.419509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:43.177057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:44.952599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:46.987319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:40.288382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:41.679324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:43.470964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:45.257880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:47.288184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:40.530565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:41.961905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:43.810456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:45.533652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:47.546317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:40.803079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:42.272731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:44.164353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T16:46:45.750717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T16:47:12.807360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분일자업종명업태명소재지도로명지도점검일자위반일자처분기간영업장면적(㎡)
처분일자1.0000.7240.7600.9541.0000.9990.8650.361
업종명0.7241.0000.9731.0000.7230.7220.3500.511
업태명0.7600.9731.0001.0000.7630.7680.6000.767
소재지도로명0.9541.0001.0001.0000.9660.9541.0001.000
지도점검일자1.0000.7230.7630.9661.0001.0000.8580.354
위반일자0.9990.7220.7680.9541.0001.0000.8580.358
처분기간0.8650.3500.6001.0000.8580.8581.0000.808
영업장면적(㎡)0.3610.5110.7671.0000.3540.3580.8081.000
2024-05-18T16:47:13.200501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명업종명
업태명1.0000.790
업종명0.7901.000
2024-05-18T16:47:13.450358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분일자지도점검일자위반일자처분기간영업장면적(㎡)업종명업태명
처분일자1.0001.0001.0000.6510.2820.3810.340
지도점검일자1.0001.0001.0000.6510.2790.3800.342
위반일자1.0001.0001.0000.6510.2790.3790.347
처분기간0.6510.6510.6511.0000.4940.2190.372
영업장면적(㎡)0.2820.2790.2790.4941.0000.2020.430
업종명0.3810.3800.3790.2190.2021.0000.790
업태명0.3400.3420.3470.3720.4300.7901.000

Missing values

2024-05-18T16:46:47.909514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T16:46:48.772111image/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.
2024-05-18T16:46:49.473318image/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(㎡)
030900001999052723200410600029숙박업(일반)여관업도원장<NA>서울특별시 도봉구 도봉동 산 574번지 21호19990527처분확정(경고)경고공중위생법19990527(위생교육 미필)정기 위생교육 미필 1차(경고)경고076.32
13090000200906251숙박업(일반)여관업경남<NA>서울특별시 도봉구 쌍문동 23번지 2호20090520처분확정과징금부과180만원(영업정지 2월 갈음)공중위생관리법 제11조20090520청소년 이성혼숙과징금부과180만원(영업정지 2월 갈음)<NA>116.67
230900002002033023200410700009숙박업(일반)여인숙업삼산<NA>서울특별시 도봉구 도봉동 산 95번지 35호20020103처분확정영업정지3월(2002.4.12-7.11)<NA>20020330청소년 남녀혼숙영업정지3월(2002.4.12-7.11)<NA>103.57
330900002005092343숙박업(일반)여인숙업삼산<NA>서울특별시 도봉구 도봉동 95번지 35호20050906처분확정영업정지2월을 과징금180만원으로 갈음(1일3만원*60일)=180만원공중위생관리법제11조제1항20050731위반내용 : 청소년보호법위반(이성혼숙)-1차 위반법규 : 공중위생관리법제11조제1항 처분내용 : 영업정지2월을 과징금180만원으로 갈음(1일3만원*60일)=180만원영업정지2월을 과징금180만원으로 갈음(1일3만원*60일)=180만원<NA>103.57
430900002007062643숙박업(일반)여인숙업삼산<NA>서울특별시 도봉구 도봉동 95번지 35호20070601처분확정영업정지 2월(2007.07.12~2007.09.11)공중위생관리법 제11조 1항, 동법시행규칙 19조20070512청소년이성혼숙장소제공영업정지 2월(2007.07.12~2007.09.11)<NA>103.57
53090000200911099숙박업(일반)여관업유성<NA>서울특별시 도봉구 쌍문동 716번지 24호20090727처분확정과징금부과(180만원)공중위생관리법 제11조 제1항20090727청소년 이성혼숙과징금부과(180만원)<NA>60.74
63090000202312089숙박업(일반)여관업스토리서울특별시 도봉구 도봉로135길 18, (쌍문동)서울특별시 도봉구 쌍문동 716번지 24호20231010처분확정과태료부과법 제82조제2항20231005재난배상책임보험 미가입 1일과태료부과<NA>60.74
73090000200705187숙박업(일반)여인숙업현대<NA>서울특별시 도봉구 쌍문동 713번지 45호20070423처분확정영업정지 2월을 과징금으로 부과공중위생관리법 제11조 및 동법시행규칙 제19조20070417위반내용 : 청소년 이성혼숙 장소제공 - 1차 위반법규 : 공중위생관리법 제11조 및 동법시행규칙 제19조 처분내용 : 영업정지 2월을 과징금 180만원으로 갈음(1일3만원*60일=180만원)영업정지 2월을 과징금으로 부과<NA>56.9
830900001996112723200410600011숙박업(일반)여관업태화<NA>서울특별시 도봉구 쌍문동 산 716번지 25호19961127처분확정()영업정지2월공중위생법19961127(미성년 남,녀 혼숙및 묵인)미성년자혼숙()영업정지2월<NA>60.72
930900001999052723200410700006숙박업(일반)여인숙업대복<NA>서울특별시 도봉구 창동 산 552번지 43호19990527처분확정(경고)경고공중위생법19990527(위생교육 미필)정기 위생교육 미필 1차(경고)경고047.41
시군구코드처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자행정처분상태처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)
5763090000202007091249일반미용업, 피부미용업일반미용업이찌마헤어휠서울특별시 도봉구 노해로69길 97, 상가동 지층08호 (창동, 동아청솔아파트)서울특별시 도봉구 창동 808번지 동아청솔 상가동 지층08호20200406처분확정과태료부과법 제22조제2항제6호202001012019년 영업자 위생교육 미필과태료부과<NA>37.26
5773090000202108021249일반미용업, 피부미용업일반미용업이찌마헤어휠서울특별시 도봉구 노해로69길 97, 상가동 지층08호 (창동, 동아청솔아파트)서울특별시 도봉구 창동 808번지 동아청솔 상가동 지층08호20210617처분확정영업소폐쇄법 제11조제3항제1호20210617공중위생영업자가 정당한 사유 없이 6개월 이상 계속 휴업하는 경우영업소폐쇄<NA>37.26
5783090000201702061083피부미용업, 네일미용업피부미용업나(N.A)서울특별시 도봉구 방학로3길 116, 지상1층 (쌍문동)서울특별시 도봉구 쌍문동 41번지 1호 지상1층20170119처분확정과태료 20만원 부과(기한내 자진납부시 감경액 16만원)공중위생관리법 제17조201612312016년 기존영업자 위생교육 미필과태료 20만원 부과(기한내 자진납부시 감경액 16만원)<NA>30.0
5793090000202007081643화장ㆍ분장 미용업메이크업업CHO롱beauty서울특별시 도봉구 도봉로112길 57-3, 201동 102호 (창동, 북한산팰리스)서울특별시 도봉구 창동 667번지 106호 201 북한산팰리스-10220200406처분확정과태료부과법 제22조제2항제6호202001012019년 영업자 위생교육 미필과태료부과<NA>16.32
5803090000202007081643화장ㆍ분장 미용업메이크업업CHO롱beauty서울특별시 도봉구 도봉로112길 57-3, 201동 102호 (창동, 북한산팰리스)서울특별시 도봉구 창동 667번지 106호 201 북한산팰리스-10220200406처분확정과태료부과법 제22조제2항제6호202001012019년 영업자 위생교육 미필과태료부과<NA>16.32
5813090000202302242022-00002화장ㆍ분장 미용업메이크업업뷰티룸서울특별시 도봉구 도봉로180길 53, 극동아파트 상가동 203호 (도봉동)서울특별시 도봉구 도봉동 81번지 1호 상가 극동아파트-20320230105처분확정과징금부과법 제11조제1항제4호20230105눈썹 문신 유사한 의료행위를 한 경우과징금부과<NA>14.2
5823090000202302242022-00002화장ㆍ분장 미용업메이크업업뷰티룸서울특별시 도봉구 도봉로180길 53, 극동아파트 상가동 203호 (도봉동)서울특별시 도봉구 도봉동 81번지 1호 상가 극동아파트-20320230105처분확정과징금부과법 제11조제1항제4호20230105눈썹 문신 유사한 의료행위를 한 경우과징금부과<NA>14.2
5833090000202301252022-00002화장ㆍ분장 미용업메이크업업뷰티룸서울특별시 도봉구 도봉로180길 53, 극동아파트 상가동 203호 (도봉동)서울특별시 도봉구 도봉동 81번지 1호 상가 극동아파트-20320230105처분확정과태료부과법 제11조제1항제4호20230105눈썹 문신 유사한 의료행위를 한 경우과태료부과<NA>14.2
5843090000202301252022-00002화장ㆍ분장 미용업메이크업업뷰티룸서울특별시 도봉구 도봉로180길 53, 극동아파트 상가동 203호 (도봉동)서울특별시 도봉구 도봉동 81번지 1호 상가 극동아파트-20320230105처분확정과태료부과법 제11조제1항제4호20230105눈썹 문신 유사한 의료행위를 한 경우과태료부과<NA>14.2
5853090000202007081538피부미용업, 화장ㆍ분장 미용업메이크업업피샵뷰티토탈(p샵beauty total)서울특별시 도봉구 도당로13다길 15-27, 지상1층 101호 (방학동, 현대맨션)서울특별시 도봉구 방학동 632번지 84호 현대맨션 101호 지상1층20200619처분확정과태료부과법 제22조제2항제6호202001012019년 영업자 위생교육 미필과태료부과<NA>26.0

Duplicate rows

Most frequently occurring

시군구코드처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자행정처분상태처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)# duplicates
2309000020090722131이용업일반이용업아영<NA>서울특별시 도봉구 창동 662번지 67호20090519처분확정면허정지공중위생관리법 제11조20090519성매매알선등행위의처벌에관한법률 위반면허정지<NA>72.04
3309000020090722131이용업일반이용업아영<NA>서울특별시 도봉구 창동 662번지 67호20090519처분확정영업정지공중위생관리법 제11조20090519성매매알선등행위의처벌에관한법률 위반영업정지<NA>72.04
12309000020120213피부2011-016피부미용업피부미용업에스테덤 뷰티프라자(창동점)<NA>서울특별시 도봉구 창동 338번지 신원리베르텔-30820120112처분확정경고공중위생관리법 제10조,제17조,제22조20120112위생교육 미필경고<NA>73.04
0309000020070313349미용업일반미용업가위손<NA>서울특별시 도봉구 창동 804번지 0호 대우(아)상가10220070109처분확정과태료부과공중위생관리법 제22조 제2항200701092006년도 기존영업자 위생교육 미필과태료부과<NA><NA>2
130900002007100931숙박업(일반)여관업뉴스타<NA>서울특별시 도봉구 도봉동 620번지 30호20070814처분확정과징금부과공중위생관리법 제11조20070814청소년이성혼숙 장소제공 - 1차과징금부과<NA>248.362
4309000020100203856미용업일반미용업이신영 머리창조<NA>서울특별시 도봉구 창동 560번지 3호20100203처분확정과태료부과(16만원)공중위생관리법 제17조 제1항201002032009 기존영업주 위생교육 미필과태료부과(16만원)<NA>24.752
5309000020100304168일반미용업일반미용업롯데미용실<NA>서울특별시 도봉구 도봉동 600번지 12호20100203처분확정경고공중위생관리법 제17조 제1항201002032009 기존영업자 위생교육 미필경고<NA>22.02
6309000020100304168일반미용업일반미용업롯데미용실<NA>서울특별시 도봉구 도봉동 600번지 12호20100203처분확정과태료부과(20만원)공중위생관리법 제17조 제1항201002032009 기존영업자 위생교육 미필과태료부과(20만원)<NA>22.02
7309000020100408856미용업일반미용업이신영 머리창조<NA>서울특별시 도봉구 창동 560번지 3호20100203처분확정경고공중위생관리법 제17조 제1항201002032009 기존영업주 위생교육 미필경고<NA>24.752
830900002011012516목욕장업공동탕업정한탕<NA>서울특별시 도봉구 창동 731번지 111호20101227처분확정과징금부과공중위생관리법제4조2항20101230욕조수 수질기준 부적합(2차)과징금부과10302.612