Overview

Dataset statistics

Number of variables17
Number of observations578
Missing cells120
Missing cells (%)1.2%
Duplicate rows14
Duplicate rows (%)2.4%
Total size in memory80.3 KiB
Average record size in memory142.2 B

Variable types

Categorical5
Numeric4
Text8

Dataset

Description시군구코드,처분일자,교부번호,업종명,업태명,업소명,소재지도로명,소재지지번,지도점검일자,행정처분상태,처분명,법적근거,위반일자,위반내용,처분내용,처분기간,영업장면적(㎡)
Author서대문구
URLhttps://data.seoul.go.kr/dataList/OA-2306/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
행정처분상태 has constant value ""Constant
Dataset has 14 (2.4%) 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
영업장면적(㎡) is highly overall correlated with 처분기간High correlation
업종명 is highly overall correlated with 업태명 and 1 other fieldsHigh correlation
업태명 is highly overall correlated with 업종명 and 1 other fieldsHigh correlation
처분기간 is highly overall correlated with 처분일자 and 5 other fieldsHigh correlation
처분기간 is highly imbalanced (87.4%)Imbalance
소재지도로명 has 53 (9.2%) missing valuesMissing
위반내용 has 28 (4.8%) missing valuesMissing
영업장면적(㎡) has 39 (6.7%) missing valuesMissing
영업장면적(㎡) has 37 (6.4%) zerosZeros

Reproduction

Analysis started2024-05-04 06:30:07.504134
Analysis finished2024-05-04 06:30:17.655796
Duration10.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
3120000
578 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3120000
2nd row3120000
3rd row3120000
4th row3120000
5th row3120000

Common Values

ValueCountFrequency (%)
3120000 578
100.0%

Length

2024-05-04T06:30:17.841371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T06:30:18.105771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3120000 578
100.0%

처분일자
Real number (ℝ)

HIGH CORRELATION 

Distinct300
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20126486
Minimum20010917
Maximum20240308
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2024-05-04T06:30:18.495269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010917
5-th percentile20050402
Q120090709
median20130218
Q320167889
95-th percentile20210732
Maximum20240308
Range229391
Interquartile range (IQR)77180

Descriptive statistics

Standard deviation50589.936
Coefficient of variation (CV)0.0025136001
Kurtosis-0.76275284
Mean20126486
Median Absolute Deviation (MAD)39652.5
Skewness0.13876151
Sum1.1633109 × 1010
Variance2.5593416 × 109
MonotonicityNot monotonic
2024-05-04T06:30:18.993935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20130515 15
 
2.6%
20180412 13
 
2.2%
20140613 10
 
1.7%
20120619 10
 
1.7%
20111125 9
 
1.6%
20140703 8
 
1.4%
20180314 7
 
1.2%
20130412 7
 
1.2%
20160309 7
 
1.2%
20051012 7
 
1.2%
Other values (290) 485
83.9%
ValueCountFrequency (%)
20010917 1
 
0.2%
20011213 1
 
0.2%
20040419 4
0.7%
20040517 1
 
0.2%
20040601 1
 
0.2%
20040618 2
0.3%
20040713 2
0.3%
20040720 3
0.5%
20040727 1
 
0.2%
20040826 1
 
0.2%
ValueCountFrequency (%)
20240308 2
0.3%
20240305 1
 
0.2%
20240229 1
 
0.2%
20240122 1
 
0.2%
20231201 1
 
0.2%
20231018 1
 
0.2%
20230901 1
 
0.2%
20230426 1
 
0.2%
20230424 1
 
0.2%
20230420 3
0.5%
Distinct277
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2024-05-04T06:30:19.792179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length3.216263
Min length1

Characters and Unicode

Total characters1859
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

Unique159 ?
Unique (%)27.5%

Sample

1st row18
2nd row16
3rd row51
4th row51
5th row24
ValueCountFrequency (%)
33 11
 
1.9%
57 9
 
1.6%
101 9
 
1.6%
82 9
 
1.6%
73 8
 
1.4%
96 7
 
1.2%
187 7
 
1.2%
138 6
 
1.0%
55 6
 
1.0%
70 6
 
1.0%
Other values (267) 500
86.5%
2024-05-04T06:30:20.835692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 338
18.2%
1 324
17.4%
2 252
13.6%
3 164
8.8%
7 146
7.9%
4 142
7.6%
5 129
 
6.9%
8 111
 
6.0%
9 109
 
5.9%
6 97
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1812
97.5%
Dash Punctuation 47
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 338
18.7%
1 324
17.9%
2 252
13.9%
3 164
9.1%
7 146
8.1%
4 142
7.8%
5 129
 
7.1%
8 111
 
6.1%
9 109
 
6.0%
6 97
 
5.4%
Dash Punctuation
ValueCountFrequency (%)
- 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1859
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 338
18.2%
1 324
17.4%
2 252
13.6%
3 164
8.8%
7 146
7.9%
4 142
7.6%
5 129
 
6.9%
8 111
 
6.0%
9 109
 
5.9%
6 97
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1859
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 338
18.2%
1 324
17.4%
2 252
13.6%
3 164
8.8%
7 146
7.9%
4 142
7.6%
5 129
 
6.9%
8 111
 
6.0%
9 109
 
5.9%
6 97
 
5.2%

업종명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
숙박업(일반)
152 
목욕장업
102 
이용업
76 
일반미용업
53 
피부미용업
47 
Other values (13)
148 

Length

Max length23
Median length16
Mean length5.2474048
Min length3

Unique

Unique4 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
숙박업(일반) 152
26.3%
목욕장업 102
17.6%
이용업 76
13.1%
일반미용업 53
 
9.2%
피부미용업 47
 
8.1%
위생관리용역업 45
 
7.8%
미용업 30
 
5.2%
종합미용업 28
 
4.8%
세탁업 22
 
3.8%
네일미용업 5
 
0.9%
Other values (8) 18
 
3.1%

Length

2024-05-04T06:30:21.454507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
숙박업(일반 152
25.7%
목욕장업 102
17.2%
이용업 76
12.8%
일반미용업 59
 
10.0%
피부미용업 49
 
8.3%
위생관리용역업 45
 
7.6%
미용업 34
 
5.7%
종합미용업 28
 
4.7%
세탁업 22
 
3.7%
네일미용업 11
 
1.9%
Other values (4) 14
 
2.4%

업태명
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
일반미용업
107 
여관업
86 
일반이용업
76 
여인숙업
56 
공동탕업
55 
Other values (15)
198 

Length

Max length14
Median length9
Mean length5.3650519
Min length3

Unique

Unique4 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 107
18.5%
여관업 86
14.9%
일반이용업 76
13.1%
여인숙업 56
9.7%
공동탕업 55
9.5%
피부미용업 52
9.0%
위생관리용역업 45
7.8%
공동탕업+찜질시설서비스영업 42
 
7.3%
일반세탁업 21
 
3.6%
네일아트업 11
 
1.9%
Other values (10) 27
 
4.7%

Length

2024-05-04T06:30:22.000759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 107
18.4%
여관업 86
14.8%
일반이용업 76
13.0%
여인숙업 56
9.6%
공동탕업 55
9.4%
피부미용업 52
8.9%
위생관리용역업 45
7.7%
공동탕업+찜질시설서비스영업 42
 
7.2%
일반세탁업 21
 
3.6%
네일아트업 11
 
1.9%
Other values (11) 32
 
5.5%
Distinct379
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2024-05-04T06:30:22.684698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length18
Mean length4.8823529
Min length1

Characters and Unicode

Total characters2822
Distinct characters387
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique267 ?
Unique (%)46.2%

Sample

1st row원앙
2nd row성덕
3rd row폼모텔
4th row폼모텔
5th row해담여관
ValueCountFrequency (%)
오성24시사우나 9
 
1.4%
헤어 8
 
1.2%
한양 7
 
1.1%
통일장 6
 
0.9%
성도 6
 
0.9%
탐라랜드사우나 6
 
0.9%
호텔 6
 
0.9%
얼짱몸짱 5
 
0.8%
동원 5
 
0.8%
홍제점 5
 
0.8%
Other values (415) 596
90.4%
2024-05-04T06:30:23.605044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
81
 
2.9%
69
 
2.4%
65
 
2.3%
63
 
2.2%
61
 
2.2%
51
 
1.8%
45
 
1.6%
41
 
1.5%
40
 
1.4%
39
 
1.4%
Other values (377) 2267
80.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2552
90.4%
Space Separator 81
 
2.9%
Decimal Number 48
 
1.7%
Lowercase Letter 38
 
1.3%
Open Punctuation 33
 
1.2%
Close Punctuation 33
 
1.2%
Uppercase Letter 32
 
1.1%
Other Punctuation 3
 
0.1%
Dash Punctuation 1
 
< 0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
69
 
2.7%
65
 
2.5%
63
 
2.5%
61
 
2.4%
51
 
2.0%
45
 
1.8%
41
 
1.6%
40
 
1.6%
39
 
1.5%
37
 
1.4%
Other values (332) 2041
80.0%
Lowercase Letter
ValueCountFrequency (%)
o 6
15.8%
a 5
13.2%
l 4
10.5%
m 3
7.9%
e 3
7.9%
n 3
7.9%
d 2
 
5.3%
i 2
 
5.3%
b 2
 
5.3%
t 2
 
5.3%
Other values (6) 6
15.8%
Uppercase Letter
ValueCountFrequency (%)
A 6
18.8%
I 3
9.4%
N 3
9.4%
H 3
9.4%
B 3
9.4%
S 3
9.4%
R 2
 
6.2%
L 1
 
3.1%
U 1
 
3.1%
K 1
 
3.1%
Other values (6) 6
18.8%
Decimal Number
ValueCountFrequency (%)
2 18
37.5%
4 15
31.2%
1 5
 
10.4%
3 5
 
10.4%
5 4
 
8.3%
6 1
 
2.1%
Other Punctuation
ValueCountFrequency (%)
2
66.7%
& 1
33.3%
Space Separator
ValueCountFrequency (%)
81
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2552
90.4%
Common 199
 
7.1%
Latin 71
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
69
 
2.7%
65
 
2.5%
63
 
2.5%
61
 
2.4%
51
 
2.0%
45
 
1.8%
41
 
1.6%
40
 
1.6%
39
 
1.5%
37
 
1.4%
Other values (332) 2041
80.0%
Latin
ValueCountFrequency (%)
o 6
 
8.5%
A 6
 
8.5%
a 5
 
7.0%
l 4
 
5.6%
I 3
 
4.2%
N 3
 
4.2%
H 3
 
4.2%
B 3
 
4.2%
m 3
 
4.2%
S 3
 
4.2%
Other values (23) 32
45.1%
Common
ValueCountFrequency (%)
81
40.7%
( 33
16.6%
) 33
16.6%
2 18
 
9.0%
4 15
 
7.5%
1 5
 
2.5%
3 5
 
2.5%
5 4
 
2.0%
2
 
1.0%
& 1
 
0.5%
Other values (2) 2
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2552
90.4%
ASCII 267
 
9.5%
None 2
 
0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
81
30.3%
( 33
12.4%
) 33
12.4%
2 18
 
6.7%
4 15
 
5.6%
o 6
 
2.2%
A 6
 
2.2%
1 5
 
1.9%
3 5
 
1.9%
a 5
 
1.9%
Other values (33) 60
22.5%
Hangul
ValueCountFrequency (%)
69
 
2.7%
65
 
2.5%
63
 
2.5%
61
 
2.4%
51
 
2.0%
45
 
1.8%
41
 
1.6%
40
 
1.6%
39
 
1.5%
37
 
1.4%
Other values (332) 2041
80.0%
None
ValueCountFrequency (%)
2
100.0%
Number Forms
ValueCountFrequency (%)
1
100.0%

소재지도로명
Text

MISSING 

Distinct329
Distinct (%)62.7%
Missing53
Missing (%)9.2%
Memory size4.6 KiB
2024-05-04T06:30:24.293466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length49
Mean length29.434286
Min length23

Characters and Unicode

Total characters15453
Distinct characters175
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

Unique223 ?
Unique (%)42.5%

Sample

1st row서울특별시 서대문구 통일로 173-54, (천연동)
2nd row서울특별시 서대문구 신촌로37길 11, (북아현동)
3rd row서울특별시 서대문구 간호대로1길 36, (홍제동)
4th row서울특별시 서대문구 간호대로1길 36, (홍제동)
5th row서울특별시 서대문구 통일로11길 7, (천연동)
ValueCountFrequency (%)
서울특별시 525
 
18.7%
서대문구 525
 
18.7%
창천동 88
 
3.1%
홍제동 61
 
2.2%
통일로 51
 
1.8%
남가좌동 49
 
1.7%
북가좌동 47
 
1.7%
연희동 43
 
1.5%
홍은동 39
 
1.4%
대현동 38
 
1.4%
Other values (471) 1337
47.7%
2024-05-04T06:30:25.504728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2279
 
14.7%
1071
 
6.9%
, 711
 
4.6%
641
 
4.1%
( 595
 
3.9%
) 593
 
3.8%
538
 
3.5%
533
 
3.4%
527
 
3.4%
526
 
3.4%
Other values (165) 7439
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9101
58.9%
Space Separator 2279
 
14.7%
Decimal Number 1992
 
12.9%
Other Punctuation 718
 
4.6%
Open Punctuation 595
 
3.9%
Close Punctuation 593
 
3.8%
Dash Punctuation 144
 
0.9%
Uppercase Letter 27
 
0.2%
Math Symbol 2
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1071
 
11.8%
641
 
7.0%
538
 
5.9%
533
 
5.9%
527
 
5.8%
526
 
5.8%
525
 
5.8%
525
 
5.8%
517
 
5.7%
461
 
5.1%
Other values (137) 3237
35.6%
Decimal Number
ValueCountFrequency (%)
1 479
24.0%
2 359
18.0%
3 248
12.4%
4 205
10.3%
5 148
 
7.4%
6 134
 
6.7%
7 131
 
6.6%
0 127
 
6.4%
8 87
 
4.4%
9 74
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
B 13
48.1%
S 3
 
11.1%
K 3
 
11.1%
A 3
 
11.1%
D 3
 
11.1%
M 1
 
3.7%
C 1
 
3.7%
Other Punctuation
ValueCountFrequency (%)
, 711
99.0%
? 3
 
0.4%
/ 2
 
0.3%
. 2
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
k 1
50.0%
s 1
50.0%
Space Separator
ValueCountFrequency (%)
2279
100.0%
Open Punctuation
ValueCountFrequency (%)
( 595
100.0%
Close Punctuation
ValueCountFrequency (%)
) 593
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 144
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9101
58.9%
Common 6323
40.9%
Latin 29
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1071
 
11.8%
641
 
7.0%
538
 
5.9%
533
 
5.9%
527
 
5.8%
526
 
5.8%
525
 
5.8%
525
 
5.8%
517
 
5.7%
461
 
5.1%
Other values (137) 3237
35.6%
Common
ValueCountFrequency (%)
2279
36.0%
, 711
 
11.2%
( 595
 
9.4%
) 593
 
9.4%
1 479
 
7.6%
2 359
 
5.7%
3 248
 
3.9%
4 205
 
3.2%
5 148
 
2.3%
- 144
 
2.3%
Other values (9) 562
 
8.9%
Latin
ValueCountFrequency (%)
B 13
44.8%
S 3
 
10.3%
K 3
 
10.3%
A 3
 
10.3%
D 3
 
10.3%
k 1
 
3.4%
s 1
 
3.4%
M 1
 
3.4%
C 1
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9101
58.9%
ASCII 6352
41.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2279
35.9%
, 711
 
11.2%
( 595
 
9.4%
) 593
 
9.3%
1 479
 
7.5%
2 359
 
5.7%
3 248
 
3.9%
4 205
 
3.2%
5 148
 
2.3%
- 144
 
2.3%
Other values (18) 591
 
9.3%
Hangul
ValueCountFrequency (%)
1071
 
11.8%
641
 
7.0%
538
 
5.9%
533
 
5.9%
527
 
5.8%
526
 
5.8%
525
 
5.8%
525
 
5.8%
517
 
5.7%
461
 
5.1%
Other values (137) 3237
35.6%
Distinct371
Distinct (%)64.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2024-05-04T06:30:26.361116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length62
Median length50
Mean length28.982699
Min length22

Characters and Unicode

Total characters16752
Distinct characters153
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

Unique253 ?
Unique (%)43.8%

Sample

1st row서울특별시 서대문구 천연동 13번지 13호
2nd row서울특별시 서대문구 북아현동 125번지 20호
3rd row서울특별시 서대문구 홍제동 278번지 31호
4th row서울특별시 서대문구 홍제동 278번지 31호
5th row서울특별시 서대문구 천연동 10번지 0호
ValueCountFrequency (%)
서울특별시 578
18.5%
서대문구 578
18.5%
창천동 106
 
3.4%
홍제동 86
 
2.7%
남가좌동 72
 
2.3%
대현동 62
 
2.0%
북가좌동 57
 
1.8%
홍은동 56
 
1.8%
연희동 55
 
1.8%
1층 43
 
1.4%
Other values (432) 1439
45.9%
2024-05-04T06:30:27.885991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4089
24.4%
1156
 
6.9%
642
 
3.8%
1 632
 
3.8%
613
 
3.7%
580
 
3.5%
580
 
3.5%
579
 
3.5%
579
 
3.5%
579
 
3.5%
Other values (143) 6723
40.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9430
56.3%
Space Separator 4089
24.4%
Decimal Number 2902
 
17.3%
Open Punctuation 108
 
0.6%
Close Punctuation 107
 
0.6%
Other Punctuation 57
 
0.3%
Uppercase Letter 28
 
0.2%
Dash Punctuation 27
 
0.2%
Math Symbol 2
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1156
 
12.3%
642
 
6.8%
613
 
6.5%
580
 
6.2%
580
 
6.2%
579
 
6.1%
579
 
6.1%
579
 
6.1%
578
 
6.1%
578
 
6.1%
Other values (114) 2966
31.5%
Decimal Number
ValueCountFrequency (%)
1 632
21.8%
2 458
15.8%
3 350
12.1%
4 278
9.6%
0 257
8.9%
5 228
 
7.9%
9 208
 
7.2%
7 185
 
6.4%
6 176
 
6.1%
8 130
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
B 14
50.0%
A 3
 
10.7%
D 3
 
10.7%
S 3
 
10.7%
K 3
 
10.7%
C 1
 
3.6%
M 1
 
3.6%
Other Punctuation
ValueCountFrequency (%)
, 49
86.0%
? 3
 
5.3%
/ 2
 
3.5%
. 2
 
3.5%
: 1
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
s 1
50.0%
k 1
50.0%
Space Separator
ValueCountFrequency (%)
4089
100.0%
Open Punctuation
ValueCountFrequency (%)
( 108
100.0%
Close Punctuation
ValueCountFrequency (%)
) 107
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9430
56.3%
Common 7292
43.5%
Latin 30
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1156
 
12.3%
642
 
6.8%
613
 
6.5%
580
 
6.2%
580
 
6.2%
579
 
6.1%
579
 
6.1%
579
 
6.1%
578
 
6.1%
578
 
6.1%
Other values (114) 2966
31.5%
Common
ValueCountFrequency (%)
4089
56.1%
1 632
 
8.7%
2 458
 
6.3%
3 350
 
4.8%
4 278
 
3.8%
0 257
 
3.5%
5 228
 
3.1%
9 208
 
2.9%
7 185
 
2.5%
6 176
 
2.4%
Other values (10) 431
 
5.9%
Latin
ValueCountFrequency (%)
B 14
46.7%
A 3
 
10.0%
D 3
 
10.0%
S 3
 
10.0%
K 3
 
10.0%
s 1
 
3.3%
k 1
 
3.3%
C 1
 
3.3%
M 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9430
56.3%
ASCII 7322
43.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4089
55.8%
1 632
 
8.6%
2 458
 
6.3%
3 350
 
4.8%
4 278
 
3.8%
0 257
 
3.5%
5 228
 
3.1%
9 208
 
2.8%
7 185
 
2.5%
6 176
 
2.4%
Other values (19) 461
 
6.3%
Hangul
ValueCountFrequency (%)
1156
 
12.3%
642
 
6.8%
613
 
6.5%
580
 
6.2%
580
 
6.2%
579
 
6.1%
579
 
6.1%
579
 
6.1%
578
 
6.1%
578
 
6.1%
Other values (114) 2966
31.5%

지도점검일자
Real number (ℝ)

HIGH CORRELATION 

Distinct293
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20125334
Minimum20010817
Maximum20231228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2024-05-04T06:30:28.362954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010817
5-th percentile20050421
Q120090546
median20125626
Q320161130
95-th percentile20210604
Maximum20231228
Range220411
Interquartile range (IQR)70584

Descriptive statistics

Standard deviation50145.061
Coefficient of variation (CV)0.0024916387
Kurtosis-0.82142344
Mean20125334
Median Absolute Deviation (MAD)35312.5
Skewness0.11767835
Sum1.1632443 × 1010
Variance2.5145271 × 109
MonotonicityNot monotonic
2024-05-04T06:30:28.921380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20180323 15
 
2.6%
20140428 13
 
2.2%
20120518 13
 
2.2%
20150512 10
 
1.7%
20130411 9
 
1.6%
20090828 9
 
1.6%
20111013 9
 
1.6%
20150414 8
 
1.4%
20160218 8
 
1.4%
20120101 8
 
1.4%
Other values (283) 476
82.4%
ValueCountFrequency (%)
20010817 1
 
0.2%
20011130 1
 
0.2%
20040313 4
0.7%
20040414 1
 
0.2%
20040416 1
 
0.2%
20040430 2
0.3%
20040607 1
 
0.2%
20040609 1
 
0.2%
20040610 1
 
0.2%
20040618 2
0.3%
ValueCountFrequency (%)
20231228 3
0.5%
20231110 1
 
0.2%
20231108 1
 
0.2%
20231104 1
 
0.2%
20230817 1
 
0.2%
20230804 1
 
0.2%
20230327 3
0.5%
20230323 1
 
0.2%
20230206 1
 
0.2%
20230131 1
 
0.2%

행정처분상태
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
처분확정
578 

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 578
100.0%

Length

2024-05-04T06:30:29.343988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T06:30:29.673560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
처분확정 578
100.0%
Distinct140
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2024-05-04T06:30:30.232523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length36
Mean length9.0726644
Min length2

Characters and Unicode

Total characters5244
Distinct characters121
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

Unique86 ?
Unique (%)14.9%

Sample

1st row영업정지2월(과징금180만원부과)
2nd row영업정지에 갈음한 과징금부과
3rd row개선명령
4th row개선명령
5th row영업정지
ValueCountFrequency (%)
개선명령 135
 
13.7%
과태료부과 91
 
9.2%
경고 79
 
8.0%
영업소폐쇄 79
 
8.0%
64
 
6.5%
과징금부과 52
 
5.3%
영업정지 45
 
4.6%
부과 42
 
4.3%
갈음 36
 
3.6%
과징금 20
 
2.0%
Other values (147) 344
34.9%
2024-05-04T06:30:31.325614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
551
 
10.5%
410
 
7.8%
286
 
5.5%
232
 
4.4%
230
 
4.4%
176
 
3.4%
154
 
2.9%
154
 
2.9%
151
 
2.9%
151
 
2.9%
Other values (111) 2749
52.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4087
77.9%
Decimal Number 532
 
10.1%
Space Separator 410
 
7.8%
Open Punctuation 82
 
1.6%
Close Punctuation 82
 
1.6%
Other Punctuation 40
 
0.8%
Lowercase Letter 8
 
0.2%
Math Symbol 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
551
 
13.5%
286
 
7.0%
232
 
5.7%
230
 
5.6%
176
 
4.3%
154
 
3.8%
154
 
3.8%
151
 
3.7%
151
 
3.7%
151
 
3.7%
Other values (91) 1851
45.3%
Decimal Number
ValueCountFrequency (%)
0 144
27.1%
2 136
25.6%
1 125
23.5%
6 34
 
6.4%
5 33
 
6.2%
4 20
 
3.8%
3 15
 
2.8%
8 14
 
2.6%
9 6
 
1.1%
7 5
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 22
55.0%
, 17
42.5%
/ 1
 
2.5%
Lowercase Letter
ValueCountFrequency (%)
c 4
50.0%
v 2
25.0%
t 2
25.0%
Space Separator
ValueCountFrequency (%)
410
100.0%
Open Punctuation
ValueCountFrequency (%)
( 82
100.0%
Close Punctuation
ValueCountFrequency (%)
) 82
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4087
77.9%
Common 1149
 
21.9%
Latin 8
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
551
 
13.5%
286
 
7.0%
232
 
5.7%
230
 
5.6%
176
 
4.3%
154
 
3.8%
154
 
3.8%
151
 
3.7%
151
 
3.7%
151
 
3.7%
Other values (91) 1851
45.3%
Common
ValueCountFrequency (%)
410
35.7%
0 144
 
12.5%
2 136
 
11.8%
1 125
 
10.9%
( 82
 
7.1%
) 82
 
7.1%
6 34
 
3.0%
5 33
 
2.9%
. 22
 
1.9%
4 20
 
1.7%
Other values (7) 61
 
5.3%
Latin
ValueCountFrequency (%)
c 4
50.0%
v 2
25.0%
t 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4087
77.9%
ASCII 1157
 
22.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
551
 
13.5%
286
 
7.0%
232
 
5.7%
230
 
5.6%
176
 
4.3%
154
 
3.8%
154
 
3.8%
151
 
3.7%
151
 
3.7%
151
 
3.7%
Other values (91) 1851
45.3%
ASCII
ValueCountFrequency (%)
410
35.4%
0 144
 
12.4%
2 136
 
11.8%
1 125
 
10.8%
( 82
 
7.1%
) 82
 
7.1%
6 34
 
2.9%
5 33
 
2.9%
. 22
 
1.9%
4 20
 
1.7%
Other values (10) 69
 
6.0%
Distinct154
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2024-05-04T06:30:31.914521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length33
Mean length15.913495
Min length6

Characters and Unicode

Total characters9198
Distinct characters44
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)13.8%

Sample

1st row공중위생관리법제11조
2nd row공중위생관리법 제11조제1항, 같은법 시행규칙 제19조
3rd row공중위생관리법 제3조 및 시행규칙 제3조의2 제1항 3호
4th row공중위생관리법시행규칙 제3조의2 제1항 제3호
5th row공중위생관리법 시행규칙 제19조
ValueCountFrequency (%)
공중위생관리법 262
17.4%
168
 
11.1%
시행규칙 122
 
8.1%
제19조 116
 
7.7%
113
 
7.5%
제17조 64
 
4.2%
공중위생관리법제11조 38
 
2.5%
제11조 34
 
2.3%
제11조제1항 33
 
2.2%
공중위생관리법제3조 28
 
1.9%
Other values (127) 530
35.1%
2024-05-04T06:30:33.290795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1029
 
11.2%
933
 
10.1%
1 913
 
9.9%
832
 
9.0%
625
 
6.8%
415
 
4.5%
414
 
4.5%
414
 
4.5%
414
 
4.5%
409
 
4.4%
Other values (34) 2800
30.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6329
68.8%
Decimal Number 1780
 
19.4%
Space Separator 933
 
10.1%
Other Punctuation 154
 
1.7%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1029
16.3%
832
13.1%
625
9.9%
415
 
6.6%
414
 
6.5%
414
 
6.5%
414
 
6.5%
409
 
6.5%
408
 
6.4%
256
 
4.0%
Other values (20) 1113
17.6%
Decimal Number
ValueCountFrequency (%)
1 913
51.3%
2 214
 
12.0%
9 170
 
9.6%
3 162
 
9.1%
7 159
 
8.9%
4 126
 
7.1%
8 16
 
0.9%
6 10
 
0.6%
0 7
 
0.4%
5 3
 
0.2%
Space Separator
ValueCountFrequency (%)
933
100.0%
Other Punctuation
ValueCountFrequency (%)
, 154
100.0%
Open Punctuation
ValueCountFrequency (%)
1
100.0%
Close Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6329
68.8%
Common 2869
31.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1029
16.3%
832
13.1%
625
9.9%
415
 
6.6%
414
 
6.5%
414
 
6.5%
414
 
6.5%
409
 
6.5%
408
 
6.4%
256
 
4.0%
Other values (20) 1113
17.6%
Common
ValueCountFrequency (%)
933
32.5%
1 913
31.8%
2 214
 
7.5%
9 170
 
5.9%
3 162
 
5.6%
7 159
 
5.5%
, 154
 
5.4%
4 126
 
4.4%
8 16
 
0.6%
6 10
 
0.3%
Other values (4) 12
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6328
68.8%
ASCII 2867
31.2%
None 2
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1029
16.3%
832
13.1%
625
9.9%
415
 
6.6%
414
 
6.5%
414
 
6.5%
414
 
6.5%
409
 
6.5%
408
 
6.4%
256
 
4.0%
Other values (19) 1112
17.6%
ASCII
ValueCountFrequency (%)
933
32.5%
1 913
31.8%
2 214
 
7.5%
9 170
 
5.9%
3 162
 
5.7%
7 159
 
5.5%
, 154
 
5.4%
4 126
 
4.4%
8 16
 
0.6%
6 10
 
0.3%
Other values (2) 10
 
0.3%
Compat Jamo
ValueCountFrequency (%)
1
100.0%
None
ValueCountFrequency (%)
1
50.0%
1
50.0%

위반일자
Real number (ℝ)

HIGH CORRELATION 

Distinct292
Distinct (%)50.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20124879
Minimum20010817
Maximum20240131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2024-05-04T06:30:33.767227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010817
5-th percentile20050127
Q120090370
median20121224
Q320161130
95-th percentile20210605
Maximum20240131
Range229314
Interquartile range (IQR)70759.5

Descriptive statistics

Standard deviation50410.981
Coefficient of variation (CV)0.0025049085
Kurtosis-0.81608243
Mean20124879
Median Absolute Deviation (MAD)38994
Skewness0.1313748
Sum1.163218 × 1010
Variance2.541267 × 109
MonotonicityNot monotonic
2024-05-04T06:30:34.369714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20120101 21
 
3.6%
20180323 15
 
2.6%
20130410 15
 
2.6%
20101227 10
 
1.7%
20090828 10
 
1.7%
20140428 10
 
1.7%
20111103 9
 
1.6%
20140513 8
 
1.4%
20160218 8
 
1.4%
20150512 8
 
1.4%
Other values (282) 464
80.3%
ValueCountFrequency (%)
20010817 1
 
0.2%
20011113 1
 
0.2%
20040313 2
0.3%
20040414 1
 
0.2%
20040419 3
0.5%
20040430 2
0.3%
20040607 1
 
0.2%
20040609 1
 
0.2%
20040610 1
 
0.2%
20040618 2
0.3%
ValueCountFrequency (%)
20240131 1
 
0.2%
20240123 1
 
0.2%
20231228 2
0.3%
20231110 1
 
0.2%
20231108 1
 
0.2%
20230804 1
 
0.2%
20230712 1
 
0.2%
20230404 3
0.5%
20230214 1
 
0.2%
20230206 1
 
0.2%

위반내용
Text

MISSING 

Distinct283
Distinct (%)51.5%
Missing28
Missing (%)4.8%
Memory size4.6 KiB
2024-05-04T06:30:34.969680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length104
Median length72
Mean length20.425455
Min length1

Characters and Unicode

Total characters11234
Distinct characters323
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

Unique211 ?
Unique (%)38.4%

Sample

1st row청소년 이성혼숙
2nd row영업장 면적의 3분의 1이상을 변경숙박영업 행위
3rd row영업장면적의 3분의1이상 확장 사용
4th row윤락행위 알선 1차
5th row성매매알선및장소제공
ValueCountFrequency (%)
위생교육 88
 
3.9%
멸실 49
 
2.2%
시설물 48
 
2.1%
미필 45
 
2.0%
욕조수 43
 
1.9%
영업주 41
 
1.8%
청소년이성혼숙 39
 
1.7%
수질기준 38
 
1.7%
청소년 36
 
1.6%
34
 
1.5%
Other values (638) 1795
79.6%
2024-05-04T06:30:35.984043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1765
 
15.7%
0 348
 
3.1%
1 301
 
2.7%
295
 
2.6%
2 270
 
2.4%
247
 
2.2%
242
 
2.2%
222
 
2.0%
216
 
1.9%
201
 
1.8%
Other values (313) 7127
63.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7519
66.9%
Space Separator 1765
 
15.7%
Decimal Number 1200
 
10.7%
Other Punctuation 240
 
2.1%
Close Punctuation 145
 
1.3%
Open Punctuation 142
 
1.3%
Uppercase Letter 137
 
1.2%
Dash Punctuation 41
 
0.4%
Lowercase Letter 32
 
0.3%
Other Symbol 11
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
295
 
3.9%
247
 
3.3%
242
 
3.2%
222
 
3.0%
216
 
2.9%
201
 
2.7%
201
 
2.7%
181
 
2.4%
141
 
1.9%
140
 
1.9%
Other values (272) 5433
72.3%
Decimal Number
ValueCountFrequency (%)
0 348
29.0%
1 301
25.1%
2 270
22.5%
3 53
 
4.4%
8 45
 
3.8%
7 43
 
3.6%
4 37
 
3.1%
6 36
 
3.0%
5 34
 
2.8%
9 33
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
U 34
24.8%
C 30
21.9%
L 29
21.2%
F 28
20.4%
T 7
 
5.1%
N 6
 
4.4%
V 2
 
1.5%
M 1
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 86
35.8%
: 56
23.3%
/ 49
20.4%
, 46
19.2%
* 2
 
0.8%
1
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
l 8
25.0%
c 8
25.0%
m 8
25.0%
t 4
12.5%
v 4
12.5%
Close Punctuation
ValueCountFrequency (%)
) 139
95.9%
4
 
2.8%
] 2
 
1.4%
Open Punctuation
ValueCountFrequency (%)
( 136
95.8%
4
 
2.8%
[ 2
 
1.4%
Other Symbol
ValueCountFrequency (%)
10
90.9%
1
 
9.1%
Math Symbol
ValueCountFrequency (%)
+ 1
50.0%
~ 1
50.0%
Space Separator
ValueCountFrequency (%)
1765
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7519
66.9%
Common 3546
31.6%
Latin 169
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
295
 
3.9%
247
 
3.3%
242
 
3.2%
222
 
3.0%
216
 
2.9%
201
 
2.7%
201
 
2.7%
181
 
2.4%
141
 
1.9%
140
 
1.9%
Other values (272) 5433
72.3%
Common
ValueCountFrequency (%)
1765
49.8%
0 348
 
9.8%
1 301
 
8.5%
2 270
 
7.6%
) 139
 
3.9%
( 136
 
3.8%
. 86
 
2.4%
: 56
 
1.6%
3 53
 
1.5%
/ 49
 
1.4%
Other values (18) 343
 
9.7%
Latin
ValueCountFrequency (%)
U 34
20.1%
C 30
17.8%
L 29
17.2%
F 28
16.6%
l 8
 
4.7%
c 8
 
4.7%
m 8
 
4.7%
T 7
 
4.1%
N 6
 
3.6%
t 4
 
2.4%
Other values (3) 7
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7518
66.9%
ASCII 3695
32.9%
CJK Compat 11
 
0.1%
None 8
 
0.1%
Compat Jamo 1
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1765
47.8%
0 348
 
9.4%
1 301
 
8.1%
2 270
 
7.3%
) 139
 
3.8%
( 136
 
3.7%
. 86
 
2.3%
: 56
 
1.5%
3 53
 
1.4%
/ 49
 
1.3%
Other values (26) 492
 
13.3%
Hangul
ValueCountFrequency (%)
295
 
3.9%
247
 
3.3%
242
 
3.2%
222
 
3.0%
216
 
2.9%
201
 
2.7%
201
 
2.7%
181
 
2.4%
141
 
1.9%
140
 
1.9%
Other values (271) 5432
72.3%
CJK Compat
ValueCountFrequency (%)
10
90.9%
1
 
9.1%
None
ValueCountFrequency (%)
4
50.0%
4
50.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%
Punctuation
ValueCountFrequency (%)
1
100.0%
Distinct140
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2024-05-04T06:30:36.440214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length36
Mean length9.0726644
Min length2

Characters and Unicode

Total characters5244
Distinct characters121
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

Unique86 ?
Unique (%)14.9%

Sample

1st row영업정지2월(과징금180만원부과)
2nd row영업정지에 갈음한 과징금부과
3rd row개선명령
4th row개선명령
5th row영업정지
ValueCountFrequency (%)
개선명령 135
 
13.7%
과태료부과 91
 
9.2%
경고 79
 
8.0%
영업소폐쇄 79
 
8.0%
64
 
6.5%
과징금부과 52
 
5.3%
영업정지 45
 
4.6%
부과 42
 
4.3%
갈음 36
 
3.6%
과징금 20
 
2.0%
Other values (147) 344
34.9%
2024-05-04T06:30:37.612815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
551
 
10.5%
410
 
7.8%
286
 
5.5%
232
 
4.4%
230
 
4.4%
176
 
3.4%
154
 
2.9%
154
 
2.9%
151
 
2.9%
151
 
2.9%
Other values (111) 2749
52.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4087
77.9%
Decimal Number 532
 
10.1%
Space Separator 410
 
7.8%
Open Punctuation 82
 
1.6%
Close Punctuation 82
 
1.6%
Other Punctuation 40
 
0.8%
Lowercase Letter 8
 
0.2%
Math Symbol 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
551
 
13.5%
286
 
7.0%
232
 
5.7%
230
 
5.6%
176
 
4.3%
154
 
3.8%
154
 
3.8%
151
 
3.7%
151
 
3.7%
151
 
3.7%
Other values (91) 1851
45.3%
Decimal Number
ValueCountFrequency (%)
0 144
27.1%
2 136
25.6%
1 125
23.5%
6 34
 
6.4%
5 33
 
6.2%
4 20
 
3.8%
3 15
 
2.8%
8 14
 
2.6%
9 6
 
1.1%
7 5
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 22
55.0%
, 17
42.5%
/ 1
 
2.5%
Lowercase Letter
ValueCountFrequency (%)
c 4
50.0%
v 2
25.0%
t 2
25.0%
Space Separator
ValueCountFrequency (%)
410
100.0%
Open Punctuation
ValueCountFrequency (%)
( 82
100.0%
Close Punctuation
ValueCountFrequency (%)
) 82
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4087
77.9%
Common 1149
 
21.9%
Latin 8
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
551
 
13.5%
286
 
7.0%
232
 
5.7%
230
 
5.6%
176
 
4.3%
154
 
3.8%
154
 
3.8%
151
 
3.7%
151
 
3.7%
151
 
3.7%
Other values (91) 1851
45.3%
Common
ValueCountFrequency (%)
410
35.7%
0 144
 
12.5%
2 136
 
11.8%
1 125
 
10.9%
( 82
 
7.1%
) 82
 
7.1%
6 34
 
3.0%
5 33
 
2.9%
. 22
 
1.9%
4 20
 
1.7%
Other values (7) 61
 
5.3%
Latin
ValueCountFrequency (%)
c 4
50.0%
v 2
25.0%
t 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4087
77.9%
ASCII 1157
 
22.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
551
 
13.5%
286
 
7.0%
232
 
5.7%
230
 
5.6%
176
 
4.3%
154
 
3.8%
154
 
3.8%
151
 
3.7%
151
 
3.7%
151
 
3.7%
Other values (91) 1851
45.3%
ASCII
ValueCountFrequency (%)
410
35.4%
0 144
 
12.4%
2 136
 
11.8%
1 125
 
10.8%
( 82
 
7.1%
) 82
 
7.1%
6 34
 
2.9%
5 33
 
2.9%
. 22
 
1.9%
4 20
 
1.7%
Other values (10) 69
 
6.0%

처분기간
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
<NA>
556 
15
 
13
10
 
5
5
 
3
0
 
1

Length

Max length4
Median length4
Mean length3.916955
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 556
96.2%
15 13
 
2.2%
10 5
 
0.9%
5 3
 
0.5%
0 1
 
0.2%

Length

2024-05-04T06:30:38.065914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T06:30:38.407303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 556
96.2%
15 13
 
2.2%
10 5
 
0.9%
5 3
 
0.5%
0 1
 
0.2%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct290
Distinct (%)53.8%
Missing39
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean297.97935
Minimum0
Maximum3606.89
Zeros37
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2024-05-04T06:30:38.787107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131.925
median75.8
Q3256.15
95-th percentile1448.13
Maximum3606.89
Range3606.89
Interquartile range (IQR)224.225

Descriptive statistics

Standard deviation566.0726
Coefficient of variation (CV)1.8997041
Kurtosis14.471544
Mean297.97935
Median Absolute Deviation (MAD)57.8
Skewness3.5208027
Sum160610.87
Variance320438.18
MonotonicityNot monotonic
2024-05-04T06:30:39.318358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 37
 
6.4%
33.0 11
 
1.9%
697.11 9
 
1.6%
82.5 8
 
1.4%
198.0 6
 
1.0%
3606.89 6
 
1.0%
43.86 6
 
1.0%
2457.59 5
 
0.9%
760.22 5
 
0.9%
39.6 5
 
0.9%
Other values (280) 441
76.3%
(Missing) 39
 
6.7%
ValueCountFrequency (%)
0.0 37
6.4%
4.95 1
 
0.2%
6.6 1
 
0.2%
7.6 1
 
0.2%
8.0 1
 
0.2%
9.0 1
 
0.2%
9.9 1
 
0.2%
9.91 1
 
0.2%
10.0 2
 
0.3%
10.51 1
 
0.2%
ValueCountFrequency (%)
3606.89 6
1.0%
2457.59 5
0.9%
2412.3 2
 
0.3%
2114.32 3
0.5%
1991.0 1
 
0.2%
1952.87 1
 
0.2%
1810.75 1
 
0.2%
1650.0 1
 
0.2%
1620.0 1
 
0.2%
1593.0 1
 
0.2%

Interactions

2024-05-04T06:30:14.229637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:30:10.056276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:30:11.081340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:30:12.310097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:30:14.714096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:30:10.293769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:30:11.330821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:30:12.675755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:30:14.984455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:30:10.546347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:30:11.603983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:30:13.111452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:30:15.294548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:30:10.809787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:30:11.928534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:30:13.832779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T06:30:39.781537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분일자업종명업태명지도점검일자위반일자처분기간영업장면적(㎡)
처분일자1.0000.5560.6090.9960.9990.7880.303
업종명0.5561.0000.9630.5660.5620.6770.424
업태명0.6090.9631.0000.6200.6060.9220.669
지도점검일자0.9960.5660.6201.0000.9930.8440.236
위반일자0.9990.5620.6060.9931.0000.8080.298
처분기간0.7880.6770.9220.8440.8081.0000.554
영업장면적(㎡)0.3030.4240.6690.2360.2980.5541.000
2024-05-04T06:30:40.411983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분기간업태명업종명
처분기간1.0000.5550.588
업태명0.5551.0000.733
업종명0.5880.7331.000
2024-05-04T06:30:40.747512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분일자지도점검일자위반일자영업장면적(㎡)업종명업태명처분기간
처분일자1.0000.9990.9980.0570.2490.2370.537
지도점검일자0.9991.0000.9990.0520.2560.2430.611
위반일자0.9980.9991.0000.0530.2530.2350.562
영업장면적(㎡)0.0570.0520.0531.0000.1910.3530.538
업종명0.2490.2560.2530.1911.0000.7330.588
업태명0.2370.2430.2350.3530.7331.0000.555
처분기간0.5370.6110.5620.5380.5880.5551.000

Missing values

2024-05-04T06:30:15.908459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T06:30:16.768831image/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.
2024-05-04T06:30:17.381410image/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(㎡)
031200002004082618숙박업(일반)여인숙업원앙서울특별시 서대문구 통일로 173-54, (천연동)서울특별시 서대문구 천연동 13번지 13호20040607처분확정영업정지2월(과징금180만원부과)공중위생관리법제11조20040607<NA>영업정지2월(과징금180만원부과)<NA>0.0
131200002014090516숙박업(일반)여관업성덕서울특별시 서대문구 신촌로37길 11, (북아현동)서울특별시 서대문구 북아현동 125번지 20호20140614처분확정영업정지에 갈음한 과징금부과공중위생관리법 제11조제1항, 같은법 시행규칙 제19조20140614청소년 이성혼숙영업정지에 갈음한 과징금부과<NA>107.53
231200002010050351숙박업(일반)여관업폼모텔서울특별시 서대문구 간호대로1길 36, (홍제동)서울특별시 서대문구 홍제동 278번지 31호20100414처분확정개선명령공중위생관리법 제3조 및 시행규칙 제3조의2 제1항 3호20100414영업장 면적의 3분의 1이상을 변경숙박영업 행위개선명령<NA>348.49
331200002010050351숙박업(일반)여관업폼모텔서울특별시 서대문구 간호대로1길 36, (홍제동)서울특별시 서대문구 홍제동 278번지 31호20100414처분확정개선명령공중위생관리법시행규칙 제3조의2 제1항 제3호20100414영업장면적의 3분의1이상 확장 사용개선명령<NA>348.49
431200002005122224숙박업(일반)여관업해담여관서울특별시 서대문구 통일로11길 7, (천연동)서울특별시 서대문구 천연동 10번지 0호20051101처분확정영업정지공중위생관리법 시행규칙 제19조20051222윤락행위 알선 1차영업정지<NA>188.79
531200002008081124숙박업(일반)여관업해담여관서울특별시 서대문구 통일로11길 7, (천연동)서울특별시 서대문구 천연동 10번지 0호20080523처분확정영업정지2월공중위생관리법제11조20080523성매매알선및장소제공영업정지2월<NA>188.79
631200002004051750숙박업(일반)여인숙업화성장서울특별시 서대문구 세무서7길 6-3, (홍제동)서울특별시 서대문구 홍제동 266번지 22호20040416처분확정영업정지(과징금180만원부과)공중위생관리법11조20040419<NA>영업정지(과징금180만원부과)<NA>114.41
7312000020100630169숙박업(일반)여인숙업언덕위의하얀집서울특별시 서대문구 연세로2길 34, (창천동)서울특별시 서대문구 창천동 20번지 49호20100609처분확정개선명령공중위생관리법 시행규칙 제3조의2, 법 제4조 제7항20100609상호변경 미신고 및 영업신고증 미게시개선명령<NA>0.0
8312000020110617169숙박업(일반)여인숙업플러스모텔서울특별시 서대문구 연세로2길 34, (창천동)서울특별시 서대문구 창천동 20번지 49호20110513처분확정영업정지2월갈음 과징금부과공중위생관리법 제11조,시행규칙 제19조20110513청소년이성혼숙 장소제공영업정지2월갈음 과징금부과<NA>0.0
9312000020120423169숙박업(일반)여인숙업플러스모텔서울특별시 서대문구 연세로2길 34, (창천동)서울특별시 서대문구 창천동 20번지 49호20120101처분확정과태료부과 및 경고공중위생관리법 제22조201201012011년도 위생교육미필과태료부과 및 경고<NA>0.0
시군구코드처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자행정처분상태처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)
5683120000202403052019-00030네일미용업네일아트업네일가게서울특별시 서대문구 증가로 150, DMC아이파크2차 상가 100동 B322호 (남가좌동)서울특별시 서대문구 남가좌동 369번지 10호 상가 100 DMC아이파크2차-B32220231228처분확정과태료 60만원 부과「공중위생관리법」제22조제2항제6호202401312023년도 공중위생영업자 위생교육을 받지 아니한 자과태료 60만원 부과<NA>24.91
5693120000202106282019-00057네일미용업네일아트업네일이룸서울특별시 서대문구 연희맛로 29, 2층 왼쪽일부호 (연희동)서울특별시 서대문구 연희동 132번지 41호 2층 왼쪽일부20210604처분확정직권말소법 제11조제3항제2호202106042020년8월31일 사업자 등록폐업직권말소<NA>56.06
5703120000201603092015-00045일반미용업, 네일미용업일반미용업케이뷰티토탈(K-Beauty Total)<NA>서울특별시 서대문구 대현동 40번지 41호 28,29,41(2층)20160218처분확정과태료부과(20만원)법 제17조201602182015년도 공중위생영업자 위생교육 미수료과태료부과(20만원)<NA>65.6
5713120000201712182017-00030일반미용업, 네일미용업일반미용업수아라헤어 2<NA>서울특별시 서대문구 북가좌동 484번지 성공타워2 B동101호,201호20171116처분확정개선명령법 제4조제4항 및 제7항20171116공중위생영업자가 준수하여야 하는 위생관리기준 중 면적이 66제곱미터 이상인 영업소의 경우 영업소 외부에도 최종지불요금표를 게시 또는 부착하여야 하는데 이를 위반개선명령<NA>88.96
5723120000202003272017-00030일반미용업, 네일미용업일반미용업수아라헤어 & 네일 가좌2호점서울특별시 서대문구 수색로6길 17-14, (북가좌동, 성공타워2 B동101호,201호)서울특별시 서대문구 북가좌동 484번지 성공타워2 B동101호,201호20200224처분확정과태료부과 20만원법 제22조제2항제6호202002242019년도 공중위생영업주 위생교육 미필(1차)과태료부과 20만원<NA>88.96
5733120000201304112441피부미용업, 네일미용업피부미용업페어레이디<NA>서울특별시 서대문구 대현동 40번지 17호 (1층)20130318처분확정경고 및 과태료부과공중위생관리법제17조1항,22조201212312012년도 위생교육 미필경고 및 과태료부과<NA>23.0
5743120000201603092015-00042일반미용업, 화장ㆍ분장 미용업일반미용업비비메이크업룸서울특별시 서대문구 신촌로 195, (대현동, 3층 302호)서울특별시 서대문구 대현동 67번지 19호 3층 302호20160218처분확정과태료부과(20만원)법 제17조201602182015년도 공중위생영업자 위생교육 미수료과태료부과(20만원)<NA>66.0
5753120000201812072016-00078피부미용업, 화장ㆍ분장 미용업피부미용업캐치업 브로우바 이대점서울특별시 서대문구 이화여대길 56, 지하1층 (대현동)서울특별시 서대문구 대현동 33번지 13호 지하1층20181126처분확정영업정지법 제8조제2항20181123영업장 외 영업영업정지<NA>47.73
5763120000201804122016-00069일반미용업, 네일미용업, 화장ㆍ분장 미용업일반미용업원 헤어 필름(won hair film)서울특별시 서대문구 신촌로 241, 1층 (북아현동)서울특별시 서대문구 북아현동 220번지 36호20180323처분확정과태료부과법 제17조201803232017년 공중위생업 영업주 위생교육 미이수 관련 처분과태료부과<NA>29.92
5773120000202203112020-00043일반미용업, 네일미용업, 화장ㆍ분장 미용업네일아트업다나한네일뷰티샵서울특별시 서대문구 신촌로 13, 아륭빌딩 2층 (창천동)서울특별시 서대문구 창천동 506번지 20호 아륭빌딩20220218처분확정직권말소법 제3조3항202202212021.8.31. 사업자등록 폐업직권말소<NA>23.4

Duplicate rows

Most frequently occurring

시군구코드처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자행정처분상태처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)# duplicates
0312000020050713157숙박업(일반)여인숙업황금여관서울특별시 서대문구 연세로2다길 57, (창천동)서울특별시 서대문구 창천동 4번지 50호20050515처분확정영업정지 2월(과징금 일백팔십만원 대체)공중위생관리법20050515청소년이성혼숙영업정지 2월(과징금 일백팔십만원 대체)<NA>81.02
1312000020060907145숙박업(일반)여관업리베로모텔서울특별시 서대문구 응암로 130, (북가좌동)서울특별시 서대문구 북가좌동 273번지 9호20060818처분확정경고및과태료50만원부과공중위생관리법 제4조제7항, 제11조 및 제22조20060818정기적소독 미실시(1차)경고및과태료50만원부과<NA><NA>2
231200002006102012숙박업(일반)여인숙업테마서울특별시 서대문구 신촌로9길 4, (창천동)서울특별시 서대문구 창천동 112번지 15호20060829처분확정영업정지2월 갈음 과징금246만원부과공중위생관리법 제11조20060826청소년이성혼숙(1차)영업정지2월 갈음 과징금246만원부과<NA>0.02
3312000020070105248이용업일반이용업동원서울특별시 서대문구 통일로 426, (홍제동)서울특별시 서대문구 홍제동 137번지 12호20061220처분확정개선명령(즉시) 및 과태료50만원부과공중위생관리법 시행령 제11조제3항 및 동법 시행규칙 제19조20061214이용사 면허증 미게시(1차)개선명령(즉시) 및 과태료50만원부과<NA>115.02
431200002012061980피부미용업피부미용업얼짱몸짱 홍제점서울특별시 서대문구 통일로 432-1, (홍제동,(2층))서울특별시 서대문구 홍제동 174번지 15호 (2층)20120518처분확정과태료부과공중위생관리법 제17조1항201201012011년도 공중위생교육 미필과태료부과<NA><NA>2
5312000020130312122종합미용업일반미용업유키미쇼서울특별시 서대문구 이화여대7길 21, (대현동,(지상2층))서울특별시 서대문구 대현동 37번지 6호 (지상2층)20130125처분확정영업소폐쇄공중위생관리법 제3조,11조 및 시행규칙 19조20130122시설물 멸실영업소폐쇄<NA>73.722
631200002013041280피부미용업피부미용업얼짱몸짱 홍제점서울특별시 서대문구 통일로 432-1, (홍제동,(2층))서울특별시 서대문구 홍제동 174번지 15호 (2층)20120518처분확정경고 및 과태료부과공중위생관리법 제17조1항201201012011년도 공중위생교육 미필경고 및 과태료부과<NA><NA>2
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