Overview

Dataset statistics

Number of variables17
Number of observations968
Missing cells232
Missing cells (%)1.4%
Duplicate rows65
Duplicate rows (%)6.7%
Total size in memory134.4 KiB
Average record size in memory142.1 B

Variable types

Categorical5
Numeric4
Text8

Dataset

Description시군구코드,처분일자,교부번호,업종명,업태명,업소명,소재지도로명,소재지지번,지도점검일자,행정처분상태,처분명,법적근거,위반일자,위반내용,처분내용,처분기간,영업장면적(㎡)
Author송파구
URLhttps://data.seoul.go.kr/dataList/OA-11452/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
행정처분상태 has constant value ""Constant
Dataset has 65 (6.7%) 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 (88.1%)Imbalance
소재지도로명 has 60 (6.2%) missing valuesMissing
영업장면적(㎡) has 172 (17.8%) missing valuesMissing

Reproduction

Analysis started2024-05-04 03:30:08.955720
Analysis finished2024-05-04 03:30:16.985470
Duration8.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
3230000
968 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3230000
2nd row3230000
3rd row3230000
4th row3230000
5th row3230000

Common Values

ValueCountFrequency (%)
3230000 968
100.0%

Length

2024-05-04T03:30:17.156848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T03:30:17.360126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3230000 968
100.0%

처분일자
Real number (ℝ)

HIGH CORRELATION 

Distinct255
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20139804
Minimum20031117
Maximum20240429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2024-05-04T03:30:17.633693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20031117
5-th percentile20040708
Q120120412
median20140767
Q320170228
95-th percentile20230519
Maximum20240429
Range209312
Interquartile range (IQR)49816

Descriptive statistics

Standard deviation57609.878
Coefficient of variation (CV)0.0028604984
Kurtosis-0.59278094
Mean20139804
Median Absolute Deviation (MAD)29461
Skewness-0.12589862
Sum1.949533 × 1010
Variance3.318898 × 109
MonotonicityDecreasing
2024-05-04T03:30:17.969178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20170228 70
 
7.2%
20170321 37
 
3.8%
20150406 33
 
3.4%
20230410 22
 
2.3%
20120906 21
 
2.2%
20120816 20
 
2.1%
20230412 18
 
1.9%
20120810 17
 
1.8%
20120418 17
 
1.8%
20150909 16
 
1.7%
Other values (245) 697
72.0%
ValueCountFrequency (%)
20031117 3
0.3%
20031119 2
0.2%
20031127 3
0.3%
20031128 3
0.3%
20031202 2
0.2%
20031217 3
0.3%
20040112 1
 
0.1%
20040210 1
 
0.1%
20040226 1
 
0.1%
20040311 2
0.2%
ValueCountFrequency (%)
20240429 1
 
0.1%
20240416 1
 
0.1%
20240312 1
 
0.1%
20240116 2
0.2%
20231218 2
0.2%
20231116 1
 
0.1%
20231113 4
0.4%
20231108 1
 
0.1%
20231004 1
 
0.1%
20230918 1
 
0.1%
Distinct596
Distinct (%)61.6%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
2024-05-04T03:30:18.493030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.47624
Min length1

Characters and Unicode

Total characters10141
Distinct characters14
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

Unique444 ?
Unique (%)45.9%

Sample

1st row2022-00001
2nd row271
3rd row2003-01-004
4th row2014-00120
5th row00076
ValueCountFrequency (%)
2004-02-014 15
 
1.5%
2003-04-370 10
 
1.0%
2010-03-13 9
 
0.9%
2003-01-102 9
 
0.9%
2003-01-094 9
 
0.9%
2003-03-122 8
 
0.8%
2003-04-278 8
 
0.8%
2003-01-105 8
 
0.8%
2003-02-136 8
 
0.8%
2003-01-074 8
 
0.8%
Other values (586) 876
90.5%
2024-05-04T03:30:19.367424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3408
33.6%
- 1644
16.2%
2 1485
14.6%
1 1195
 
11.8%
3 790
 
7.8%
4 574
 
5.7%
6 251
 
2.5%
8 205
 
2.0%
5 205
 
2.0%
9 191
 
1.9%
Other values (4) 193
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8494
83.8%
Dash Punctuation 1644
 
16.2%
Other Letter 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3408
40.1%
2 1485
17.5%
1 1195
 
14.1%
3 790
 
9.3%
4 574
 
6.8%
6 251
 
3.0%
8 205
 
2.4%
5 205
 
2.4%
9 191
 
2.2%
7 190
 
2.2%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 1644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10138
> 99.9%
Hangul 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3408
33.6%
- 1644
16.2%
2 1485
14.6%
1 1195
 
11.8%
3 790
 
7.8%
4 574
 
5.7%
6 251
 
2.5%
8 205
 
2.0%
5 205
 
2.0%
9 191
 
1.9%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10138
> 99.9%
Hangul 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3408
33.6%
- 1644
16.2%
2 1485
14.6%
1 1195
 
11.8%
3 790
 
7.8%
4 574
 
5.7%
6 251
 
2.5%
8 205
 
2.0%
5 205
 
2.0%
9 191
 
1.9%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

업종명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
숙박업(일반)
173 
이용업
165 
피부미용업
139 
목욕장업
137 
위생관리용역업
127 
Other values (11)
227 

Length

Max length19
Median length16
Mean length5.1270661
Min length3

Unique

Unique3 ?
Unique (%)0.3%

Sample

1st row세탁업
2nd row세탁업
3rd row숙박업(일반)
4th row피부미용업
5th row일반미용업, 네일미용업

Common Values

ValueCountFrequency (%)
숙박업(일반) 173
17.9%
이용업 165
17.0%
피부미용업 139
14.4%
목욕장업 137
14.2%
위생관리용역업 127
13.1%
일반미용업 127
13.1%
미용업 46
 
4.8%
종합미용업 18
 
1.9%
네일미용업 13
 
1.3%
세탁업 10
 
1.0%
Other values (6) 13
 
1.3%

Length

2024-05-04T03:30:19.814518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
숙박업(일반 173
17.6%
이용업 165
16.8%
피부미용업 147
15.0%
목욕장업 137
13.9%
일반미용업 133
13.5%
위생관리용역업 127
12.9%
미용업 47
 
4.8%
네일미용업 24
 
2.4%
종합미용업 18
 
1.8%
세탁업 10
 
1.0%
Other values (2) 2
 
0.2%

업태명
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
일반미용업
181 
일반이용업
165 
피부미용업
153 
여관업
136 
위생관리용역업
127 
Other values (14)
206 

Length

Max length14
Median length5
Mean length4.963843
Min length2

Unique

Unique4 ?
Unique (%)0.4%

Sample

1st row일반세탁업
2nd row일반세탁업
3rd row여인숙업
4th row피부미용업
5th row일반미용업

Common Values

ValueCountFrequency (%)
일반미용업 181
18.7%
일반이용업 165
17.0%
피부미용업 153
15.8%
여관업 136
14.0%
위생관리용역업 127
13.1%
공동탕업 117
12.1%
네일아트업 19
 
2.0%
일반호텔 15
 
1.5%
공동탕업+찜질시설서비스영업 14
 
1.4%
관광호텔 12
 
1.2%
Other values (9) 29
 
3.0%

Length

2024-05-04T03:30:20.232872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 181
18.6%
일반이용업 165
17.0%
피부미용업 153
15.7%
여관업 136
14.0%
위생관리용역업 127
13.1%
공동탕업 117
12.0%
네일아트업 19
 
2.0%
일반호텔 15
 
1.5%
공동탕업+찜질시설서비스영업 14
 
1.4%
관광호텔 12
 
1.2%
Other values (9) 34
 
3.5%
Distinct625
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
2024-05-04T03:30:20.826385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length14
Mean length5.5795455
Min length1

Characters and Unicode

Total characters5401
Distinct characters469
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

Unique489 ?
Unique (%)50.5%

Sample

1st row우성명품세탁
2nd row아이파크 세탁
3rd row대영
4th row뷰티갤러리
5th rowOWN#(오운샵)
ValueCountFrequency (%)
모텔 20
 
1.7%
백제불한증막인삼사우나 15
 
1.3%
호텔 12
 
1.0%
에스테틱 11
 
0.9%
테크노 10
 
0.9%
대성 10
 
0.9%
미용실 9
 
0.8%
리젠트 9
 
0.8%
티파니 9
 
0.8%
모텔로즈 8
 
0.7%
Other values (697) 1046
90.3%
2024-05-04T03:30:22.150720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
205
 
3.8%
191
 
3.5%
116
 
2.1%
110
 
2.0%
105
 
1.9%
( 104
 
1.9%
) 104
 
1.9%
95
 
1.8%
91
 
1.7%
86
 
1.6%
Other values (459) 4194
77.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4764
88.2%
Space Separator 191
 
3.5%
Open Punctuation 104
 
1.9%
Close Punctuation 104
 
1.9%
Uppercase Letter 90
 
1.7%
Lowercase Letter 83
 
1.5%
Decimal Number 33
 
0.6%
Other Punctuation 26
 
0.5%
Dash Punctuation 4
 
0.1%
Modifier Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
205
 
4.3%
116
 
2.4%
110
 
2.3%
105
 
2.2%
95
 
2.0%
91
 
1.9%
86
 
1.8%
84
 
1.8%
81
 
1.7%
78
 
1.6%
Other values (396) 3713
77.9%
Uppercase Letter
ValueCountFrequency (%)
B 12
 
13.3%
S 9
 
10.0%
O 8
 
8.9%
E 5
 
5.6%
W 5
 
5.6%
Y 4
 
4.4%
A 4
 
4.4%
C 4
 
4.4%
M 4
 
4.4%
I 4
 
4.4%
Other values (13) 31
34.4%
Lowercase Letter
ValueCountFrequency (%)
i 13
15.7%
e 10
12.0%
t 9
10.8%
o 8
9.6%
a 7
8.4%
l 6
7.2%
s 5
 
6.0%
u 4
 
4.8%
q 4
 
4.8%
m 3
 
3.6%
Other values (10) 14
16.9%
Other Punctuation
ValueCountFrequency (%)
& 10
38.5%
. 7
26.9%
# 2
 
7.7%
' 2
 
7.7%
2
 
7.7%
! 1
 
3.8%
; 1
 
3.8%
, 1
 
3.8%
Decimal Number
ValueCountFrequency (%)
2 12
36.4%
1 8
24.2%
9 5
15.2%
0 3
 
9.1%
5 2
 
6.1%
4 2
 
6.1%
3 1
 
3.0%
Space Separator
ValueCountFrequency (%)
191
100.0%
Open Punctuation
ValueCountFrequency (%)
( 104
100.0%
Close Punctuation
ValueCountFrequency (%)
) 104
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4764
88.2%
Common 464
 
8.6%
Latin 173
 
3.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
205
 
4.3%
116
 
2.4%
110
 
2.3%
105
 
2.2%
95
 
2.0%
91
 
1.9%
86
 
1.8%
84
 
1.8%
81
 
1.7%
78
 
1.6%
Other values (396) 3713
77.9%
Latin
ValueCountFrequency (%)
i 13
 
7.5%
B 12
 
6.9%
e 10
 
5.8%
S 9
 
5.2%
t 9
 
5.2%
o 8
 
4.6%
O 8
 
4.6%
a 7
 
4.0%
l 6
 
3.5%
E 5
 
2.9%
Other values (33) 86
49.7%
Common
ValueCountFrequency (%)
191
41.2%
( 104
22.4%
) 104
22.4%
2 12
 
2.6%
& 10
 
2.2%
1 8
 
1.7%
. 7
 
1.5%
9 5
 
1.1%
- 4
 
0.9%
0 3
 
0.6%
Other values (10) 16
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4764
88.2%
ASCII 635
 
11.8%
None 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
205
 
4.3%
116
 
2.4%
110
 
2.3%
105
 
2.2%
95
 
2.0%
91
 
1.9%
86
 
1.8%
84
 
1.8%
81
 
1.7%
78
 
1.6%
Other values (396) 3713
77.9%
ASCII
ValueCountFrequency (%)
191
30.1%
( 104
16.4%
) 104
16.4%
i 13
 
2.0%
B 12
 
1.9%
2 12
 
1.9%
& 10
 
1.6%
e 10
 
1.6%
S 9
 
1.4%
t 9
 
1.4%
Other values (52) 161
25.4%
None
ValueCountFrequency (%)
2
100.0%

소재지도로명
Text

MISSING 

Distinct584
Distinct (%)64.3%
Missing60
Missing (%)6.2%
Memory size7.7 KiB
2024-05-04T03:30:22.744384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length69
Median length51
Mean length30.904185
Min length22

Characters and Unicode

Total characters28061
Distinct characters239
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

Unique458 ?
Unique (%)50.4%

Sample

1st row서울특별시 송파구 송파대로32길 8, 1층 6호 (가락동, 가락우성아파트)
2nd row서울특별시 송파구 법원로 55, 1층 E존202호 (문정동)
3rd row서울특별시 송파구 마천로 260, (거여동)
4th row서울특별시 송파구 오금로46길 18-1, 지상1층 (가락동)
5th row서울특별시 송파구 위례광장로 188, 아이온스퀘어 1층 132호 (장지동)
ValueCountFrequency (%)
서울특별시 908
 
17.6%
송파구 908
 
17.6%
방이동 151
 
2.9%
잠실동 125
 
2.4%
가락동 93
 
1.8%
올림픽로 91
 
1.8%
오금로11길 64
 
1.2%
백제고분로 57
 
1.1%
문정동 55
 
1.1%
삼전동 52
 
1.0%
Other values (819) 2652
51.4%
2024-05-04T03:30:23.916947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4252
 
15.2%
, 1243
 
4.4%
1093
 
3.9%
1 1084
 
3.9%
1065
 
3.8%
992
 
3.5%
) 928
 
3.3%
( 928
 
3.3%
917
 
3.3%
914
 
3.3%
Other values (229) 14645
52.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16271
58.0%
Space Separator 4252
 
15.2%
Decimal Number 4188
 
14.9%
Other Punctuation 1246
 
4.4%
Close Punctuation 928
 
3.3%
Open Punctuation 928
 
3.3%
Dash Punctuation 176
 
0.6%
Uppercase Letter 69
 
0.2%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1093
 
6.7%
1065
 
6.5%
992
 
6.1%
917
 
5.6%
914
 
5.6%
910
 
5.6%
908
 
5.6%
908
 
5.6%
908
 
5.6%
905
 
5.6%
Other values (199) 6751
41.5%
Uppercase Letter
ValueCountFrequency (%)
B 34
49.3%
A 18
26.1%
K 3
 
4.3%
J 3
 
4.3%
F 2
 
2.9%
C 2
 
2.9%
T 2
 
2.9%
L 1
 
1.4%
E 1
 
1.4%
Y 1
 
1.4%
Other values (2) 2
 
2.9%
Decimal Number
ValueCountFrequency (%)
1 1084
25.9%
2 772
18.4%
3 532
12.7%
4 391
 
9.3%
0 323
 
7.7%
5 278
 
6.6%
8 237
 
5.7%
6 233
 
5.6%
7 175
 
4.2%
9 163
 
3.9%
Other Punctuation
ValueCountFrequency (%)
, 1243
99.8%
@ 2
 
0.2%
/ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
4252
100.0%
Close Punctuation
ValueCountFrequency (%)
) 928
100.0%
Open Punctuation
ValueCountFrequency (%)
( 928
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 176
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16271
58.0%
Common 11721
41.8%
Latin 69
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1093
 
6.7%
1065
 
6.5%
992
 
6.1%
917
 
5.6%
914
 
5.6%
910
 
5.6%
908
 
5.6%
908
 
5.6%
908
 
5.6%
905
 
5.6%
Other values (199) 6751
41.5%
Common
ValueCountFrequency (%)
4252
36.3%
, 1243
 
10.6%
1 1084
 
9.2%
) 928
 
7.9%
( 928
 
7.9%
2 772
 
6.6%
3 532
 
4.5%
4 391
 
3.3%
0 323
 
2.8%
5 278
 
2.4%
Other values (8) 990
 
8.4%
Latin
ValueCountFrequency (%)
B 34
49.3%
A 18
26.1%
K 3
 
4.3%
J 3
 
4.3%
F 2
 
2.9%
C 2
 
2.9%
T 2
 
2.9%
L 1
 
1.4%
E 1
 
1.4%
Y 1
 
1.4%
Other values (2) 2
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16271
58.0%
ASCII 11790
42.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4252
36.1%
, 1243
 
10.5%
1 1084
 
9.2%
) 928
 
7.9%
( 928
 
7.9%
2 772
 
6.5%
3 532
 
4.5%
4 391
 
3.3%
0 323
 
2.7%
5 278
 
2.4%
Other values (20) 1059
 
9.0%
Hangul
ValueCountFrequency (%)
1093
 
6.7%
1065
 
6.5%
992
 
6.1%
917
 
5.6%
914
 
5.6%
910
 
5.6%
908
 
5.6%
908
 
5.6%
908
 
5.6%
905
 
5.6%
Other values (199) 6751
41.5%
Distinct615
Distinct (%)63.5%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
2024-05-04T03:30:24.528811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length45
Mean length28.107438
Min length21

Characters and Unicode

Total characters27208
Distinct characters231
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

Unique474 ?
Unique (%)49.0%

Sample

1st row서울특별시 송파구 가락동 96번지 1호 가락우성아파트
2nd row서울특별시 송파구 문정동 624번지
3rd row서울특별시 송파구 거여동 129번지 84호
4th row서울특별시 송파구 가락동 174번지 25호 지상1층
5th row서울특별시 송파구 장지동 881번지 아이온스퀘어
ValueCountFrequency (%)
서울특별시 968
 
18.2%
송파구 968
 
18.2%
방이동 190
 
3.6%
잠실동 166
 
3.1%
가락동 133
 
2.5%
1호 93
 
1.7%
석촌동 73
 
1.4%
4호 71
 
1.3%
문정동 70
 
1.3%
2호 64
 
1.2%
Other values (613) 2522
47.4%
2024-05-04T03:30:25.612478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6800
25.0%
1146
 
4.2%
1 1097
 
4.0%
1094
 
4.0%
1037
 
3.8%
1005
 
3.7%
983
 
3.6%
976
 
3.6%
970
 
3.6%
968
 
3.6%
Other values (221) 11132
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15707
57.7%
Space Separator 6800
25.0%
Decimal Number 4411
 
16.2%
Dash Punctuation 129
 
0.5%
Uppercase Letter 69
 
0.3%
Close Punctuation 30
 
0.1%
Open Punctuation 30
 
0.1%
Other Punctuation 29
 
0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1146
 
7.3%
1094
 
7.0%
1037
 
6.6%
1005
 
6.4%
983
 
6.3%
976
 
6.2%
970
 
6.2%
968
 
6.2%
968
 
6.2%
968
 
6.2%
Other values (193) 5592
35.6%
Decimal Number
ValueCountFrequency (%)
1 1097
24.9%
2 673
15.3%
3 424
 
9.6%
4 424
 
9.6%
0 388
 
8.8%
8 324
 
7.3%
5 279
 
6.3%
7 274
 
6.2%
9 273
 
6.2%
6 255
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
B 29
42.0%
A 19
27.5%
C 6
 
8.7%
S 5
 
7.2%
K 3
 
4.3%
J 3
 
4.3%
L 1
 
1.4%
T 1
 
1.4%
Y 1
 
1.4%
F 1
 
1.4%
Other Punctuation
ValueCountFrequency (%)
, 25
86.2%
@ 2
 
6.9%
/ 2
 
6.9%
Space Separator
ValueCountFrequency (%)
6800
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 129
100.0%
Close Punctuation
ValueCountFrequency (%)
) 30
100.0%
Open Punctuation
ValueCountFrequency (%)
( 30
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15707
57.7%
Common 11432
42.0%
Latin 69
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1146
 
7.3%
1094
 
7.0%
1037
 
6.6%
1005
 
6.4%
983
 
6.3%
976
 
6.2%
970
 
6.2%
968
 
6.2%
968
 
6.2%
968
 
6.2%
Other values (193) 5592
35.6%
Common
ValueCountFrequency (%)
6800
59.5%
1 1097
 
9.6%
2 673
 
5.9%
3 424
 
3.7%
4 424
 
3.7%
0 388
 
3.4%
8 324
 
2.8%
5 279
 
2.4%
7 274
 
2.4%
9 273
 
2.4%
Other values (8) 476
 
4.2%
Latin
ValueCountFrequency (%)
B 29
42.0%
A 19
27.5%
C 6
 
8.7%
S 5
 
7.2%
K 3
 
4.3%
J 3
 
4.3%
L 1
 
1.4%
T 1
 
1.4%
Y 1
 
1.4%
F 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15707
57.7%
ASCII 11501
42.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6800
59.1%
1 1097
 
9.5%
2 673
 
5.9%
3 424
 
3.7%
4 424
 
3.7%
0 388
 
3.4%
8 324
 
2.8%
5 279
 
2.4%
7 274
 
2.4%
9 273
 
2.4%
Other values (18) 545
 
4.7%
Hangul
ValueCountFrequency (%)
1146
 
7.3%
1094
 
7.0%
1037
 
6.6%
1005
 
6.4%
983
 
6.3%
976
 
6.2%
970
 
6.2%
968
 
6.2%
968
 
6.2%
968
 
6.2%
Other values (193) 5592
35.6%

지도점검일자
Real number (ℝ)

HIGH CORRELATION 

Distinct289
Distinct (%)29.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20138621
Minimum20030417
Maximum20240315
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2024-05-04T03:30:26.043523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20030417
5-th percentile20040526
Q120120217
median20140108
Q320170110
95-th percentile20230417
Maximum20240315
Range209898
Interquartile range (IQR)49893

Descriptive statistics

Standard deviation57711.627
Coefficient of variation (CV)0.0028657188
Kurtosis-0.59813005
Mean20138621
Median Absolute Deviation (MAD)30002
Skewness-0.11412447
Sum1.9494186 × 1010
Variance3.3306318 × 109
MonotonicityNot monotonic
2024-05-04T03:30:26.506875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20170110 108
 
11.2%
20150302 52
 
5.4%
20230307 35
 
3.6%
20120217 32
 
3.3%
20150116 32
 
3.3%
20140108 22
 
2.3%
20150303 20
 
2.1%
20230328 18
 
1.9%
20120329 12
 
1.2%
20230223 11
 
1.1%
Other values (279) 626
64.7%
ValueCountFrequency (%)
20030417 4
0.4%
20030512 1
 
0.1%
20030630 1
 
0.1%
20030822 1
 
0.1%
20030913 1
 
0.1%
20030923 1
 
0.1%
20030926 1
 
0.1%
20031003 1
 
0.1%
20031010 2
0.2%
20031011 1
 
0.1%
ValueCountFrequency (%)
20240315 2
0.2%
20240223 1
 
0.1%
20231222 2
0.2%
20231129 1
 
0.1%
20231030 1
 
0.1%
20231025 4
0.4%
20231019 1
 
0.1%
20231006 1
 
0.1%
20230915 1
 
0.1%
20230829 1
 
0.1%

행정처분상태
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
처분확정
968 

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 968
100.0%

Length

2024-05-04T03:30:27.225531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T03:30:27.684409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
처분확정 968
100.0%
Distinct83
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
2024-05-04T03:30:28.195731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length33
Mean length6.625
Min length2

Characters and Unicode

Total characters6413
Distinct characters76
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)5.8%

Sample

1st row직권말소
2nd row직권말소
3rd row과징금부과
4th row직권말소
5th row직권말소
ValueCountFrequency (%)
개선명령 167
13.0%
영업소폐쇄 163
12.7%
경고 156
12.2%
과태료부과 135
10.5%
영업소폐쇄(직권말소 109
8.5%
직권말소 69
 
5.4%
67
 
5.2%
과태료 65
 
5.1%
부과 47
 
3.7%
영업정지 46
 
3.6%
Other values (91) 257
20.1%
2024-05-04T03:30:29.573823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
564
 
8.8%
463
 
7.2%
367
 
5.7%
358
 
5.6%
315
 
4.9%
284
 
4.4%
283
 
4.4%
258
 
4.0%
211
 
3.3%
211
 
3.3%
Other values (66) 3099
48.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5370
83.7%
Decimal Number 383
 
6.0%
Space Separator 315
 
4.9%
Open Punctuation 144
 
2.2%
Close Punctuation 144
 
2.2%
Other Punctuation 53
 
0.8%
Math Symbol 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
564
 
10.5%
463
 
8.6%
367
 
6.8%
358
 
6.7%
284
 
5.3%
283
 
5.3%
258
 
4.8%
211
 
3.9%
211
 
3.9%
179
 
3.3%
Other values (50) 2192
40.8%
Decimal Number
ValueCountFrequency (%)
0 153
39.9%
1 61
 
15.9%
2 57
 
14.9%
5 29
 
7.6%
3 24
 
6.3%
6 17
 
4.4%
4 13
 
3.4%
8 11
 
2.9%
9 10
 
2.6%
7 8
 
2.1%
Other Punctuation
ValueCountFrequency (%)
, 38
71.7%
. 15
 
28.3%
Space Separator
ValueCountFrequency (%)
315
100.0%
Open Punctuation
ValueCountFrequency (%)
( 144
100.0%
Close Punctuation
ValueCountFrequency (%)
) 144
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5370
83.7%
Common 1043
 
16.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
564
 
10.5%
463
 
8.6%
367
 
6.8%
358
 
6.7%
284
 
5.3%
283
 
5.3%
258
 
4.8%
211
 
3.9%
211
 
3.9%
179
 
3.3%
Other values (50) 2192
40.8%
Common
ValueCountFrequency (%)
315
30.2%
0 153
14.7%
( 144
13.8%
) 144
13.8%
1 61
 
5.8%
2 57
 
5.5%
, 38
 
3.6%
5 29
 
2.8%
3 24
 
2.3%
6 17
 
1.6%
Other values (6) 61
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5370
83.7%
ASCII 1043
 
16.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
564
 
10.5%
463
 
8.6%
367
 
6.8%
358
 
6.7%
284
 
5.3%
283
 
5.3%
258
 
4.8%
211
 
3.9%
211
 
3.9%
179
 
3.3%
Other values (50) 2192
40.8%
ASCII
ValueCountFrequency (%)
315
30.2%
0 153
14.7%
( 144
13.8%
) 144
13.8%
1 61
 
5.8%
2 57
 
5.5%
, 38
 
3.6%
5 29
 
2.8%
3 24
 
2.3%
6 17
 
1.6%
Other values (6) 61
 
5.8%
Distinct168
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
2024-05-04T03:30:30.356916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length28
Mean length12.991736
Min length4

Characters and Unicode

Total characters12576
Distinct characters59
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

Unique86 ?
Unique (%)8.9%

Sample

1st row법 제11조제3항제2호
2nd row법 제11조제3항제2호
3rd row법 제11조제1항제8호
4th row법 제11조제3항제2호
5th row법 제11조제3항제2호
ValueCountFrequency (%)
421
19.9%
공중위생관리법 307
14.5%
제11조제3항 106
 
5.0%
공중위생관리법제4조제7항 91
 
4.3%
제3조제1항 87
 
4.1%
제17조 87
 
4.1%
제11조제3항제2호 81
 
3.8%
72
 
3.4%
제11조 54
 
2.6%
제3조3항 42
 
2.0%
Other values (129) 767
36.3%
2024-05-04T03:30:31.745871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1879
14.9%
1287
10.2%
1 1094
 
8.7%
1066
 
8.5%
1032
 
8.2%
841
 
6.7%
565
 
4.5%
542
 
4.3%
542
 
4.3%
541
 
4.3%
Other values (49) 3187
25.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8653
68.8%
Decimal Number 2550
 
20.3%
Space Separator 1287
 
10.2%
Other Punctuation 62
 
0.5%
Close Punctuation 12
 
0.1%
Open Punctuation 12
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1879
21.7%
1066
12.3%
1032
11.9%
841
9.7%
565
 
6.5%
542
 
6.3%
542
 
6.3%
541
 
6.3%
532
 
6.1%
532
 
6.1%
Other values (35) 581
 
6.7%
Decimal Number
ValueCountFrequency (%)
1 1094
42.9%
3 506
19.8%
7 309
 
12.1%
4 303
 
11.9%
2 277
 
10.9%
6 23
 
0.9%
0 18
 
0.7%
9 10
 
0.4%
8 9
 
0.4%
5 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1287
100.0%
Other Punctuation
ValueCountFrequency (%)
, 62
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8653
68.8%
Common 3923
31.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1879
21.7%
1066
12.3%
1032
11.9%
841
9.7%
565
 
6.5%
542
 
6.3%
542
 
6.3%
541
 
6.3%
532
 
6.1%
532
 
6.1%
Other values (35) 581
 
6.7%
Common
ValueCountFrequency (%)
1287
32.8%
1 1094
27.9%
3 506
 
12.9%
7 309
 
7.9%
4 303
 
7.7%
2 277
 
7.1%
, 62
 
1.6%
6 23
 
0.6%
0 18
 
0.5%
) 12
 
0.3%
Other values (4) 32
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8653
68.8%
ASCII 3923
31.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1879
21.7%
1066
12.3%
1032
11.9%
841
9.7%
565
 
6.5%
542
 
6.3%
542
 
6.3%
541
 
6.3%
532
 
6.1%
532
 
6.1%
Other values (35) 581
 
6.7%
ASCII
ValueCountFrequency (%)
1287
32.8%
1 1094
27.9%
3 506
 
12.9%
7 309
 
7.9%
4 303
 
7.7%
2 277
 
7.1%
, 62
 
1.6%
6 23
 
0.6%
0 18
 
0.5%
) 12
 
0.3%
Other values (4) 32
 
0.8%

위반일자
Real number (ℝ)

HIGH CORRELATION 

Distinct384
Distinct (%)39.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20127615
Minimum19940501
Maximum20231213
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2024-05-04T03:30:32.295362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19940501
5-th percentile20040515
Q120101225
median20131118
Q320170110
95-th percentile20211231
Maximum20231213
Range290712
Interquartile range (IQR)68885.25

Descriptive statistics

Standard deviation50935.162
Coefficient of variation (CV)0.0025306108
Kurtosis-0.35831109
Mean20127615
Median Absolute Deviation (MAD)30717
Skewness-0.37389318
Sum1.9483532 × 1010
Variance2.5943907 × 109
MonotonicityNot monotonic
2024-05-04T03:30:32.916013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20170110 111
 
11.5%
20150302 50
 
5.2%
20150116 32
 
3.3%
20120217 32
 
3.3%
20140108 22
 
2.3%
20150303 22
 
2.3%
20211231 15
 
1.5%
20160617 11
 
1.1%
20130321 10
 
1.0%
20060428 10
 
1.0%
Other values (374) 653
67.5%
ValueCountFrequency (%)
19940501 1
 
0.1%
19960930 1
 
0.1%
20030417 4
0.4%
20030512 1
 
0.1%
20030822 1
 
0.1%
20030913 1
 
0.1%
20030923 1
 
0.1%
20030926 1
 
0.1%
20031003 1
 
0.1%
20031010 2
0.2%
ValueCountFrequency (%)
20231213 1
0.1%
20231019 1
0.1%
20230811 1
0.1%
20230731 1
0.1%
20230727 1
0.1%
20230713 1
0.1%
20230614 1
0.1%
20230609 1
0.1%
20230418 1
0.1%
20230321 1
0.1%
Distinct459
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
2024-05-04T03:30:33.565373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length138
Median length79
Mean length22.048554
Min length4

Characters and Unicode

Total characters21343
Distinct characters328
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

Unique335 ?
Unique (%)34.6%

Sample

1st row폐업 미신고
2nd row폐업 미신고
3rd row청소년 이성혼숙
4th row사업자 폐업신고 후 영업신고 폐업 미이행
5th row사업자 등록 폐업신고 후 영업신고 폐업 미이행
ValueCountFrequency (%)
사업자등록 272
 
6.4%
169
 
3.9%
폐업 143
 
3.3%
폐업신고없이 121
 
2.8%
수질기준 121
 
2.8%
폐업일자 114
 
2.7%
위생교육 109
 
2.5%
미이행 103
 
2.4%
폐업신고 99
 
2.3%
부적합 83
 
1.9%
Other values (829) 2946
68.8%
2024-05-04T03:30:34.938705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3535
 
16.6%
1150
 
5.4%
0 622
 
2.9%
591
 
2.8%
558
 
2.6%
2 514
 
2.4%
. 511
 
2.4%
1 507
 
2.4%
471
 
2.2%
427
 
2.0%
Other values (318) 12457
58.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14000
65.6%
Space Separator 3535
 
16.6%
Decimal Number 2232
 
10.5%
Other Punctuation 849
 
4.0%
Open Punctuation 219
 
1.0%
Close Punctuation 219
 
1.0%
Dash Punctuation 170
 
0.8%
Lowercase Letter 54
 
0.3%
Uppercase Letter 47
 
0.2%
Math Symbol 18
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1150
 
8.2%
591
 
4.2%
558
 
4.0%
471
 
3.4%
427
 
3.0%
409
 
2.9%
353
 
2.5%
339
 
2.4%
326
 
2.3%
324
 
2.3%
Other values (284) 9052
64.7%
Decimal Number
ValueCountFrequency (%)
0 622
27.9%
2 514
23.0%
1 507
22.7%
3 151
 
6.8%
4 127
 
5.7%
5 79
 
3.5%
6 79
 
3.5%
7 63
 
2.8%
8 55
 
2.5%
9 35
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
U 11
23.4%
C 8
17.0%
F 8
17.0%
L 8
17.0%
T 5
10.6%
N 4
 
8.5%
H 1
 
2.1%
O 1
 
2.1%
E 1
 
2.1%
Other Punctuation
ValueCountFrequency (%)
. 511
60.2%
: 192
 
22.6%
, 117
 
13.8%
/ 28
 
3.3%
* 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
c 26
48.1%
t 13
24.1%
v 13
24.1%
m 1
 
1.9%
l 1
 
1.9%
Space Separator
ValueCountFrequency (%)
3535
100.0%
Open Punctuation
ValueCountFrequency (%)
( 219
100.0%
Close Punctuation
ValueCountFrequency (%)
) 219
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 170
100.0%
Math Symbol
ValueCountFrequency (%)
~ 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14000
65.6%
Common 7242
33.9%
Latin 101
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1150
 
8.2%
591
 
4.2%
558
 
4.0%
471
 
3.4%
427
 
3.0%
409
 
2.9%
353
 
2.5%
339
 
2.4%
326
 
2.3%
324
 
2.3%
Other values (284) 9052
64.7%
Common
ValueCountFrequency (%)
3535
48.8%
0 622
 
8.6%
2 514
 
7.1%
. 511
 
7.1%
1 507
 
7.0%
( 219
 
3.0%
) 219
 
3.0%
: 192
 
2.7%
- 170
 
2.3%
3 151
 
2.1%
Other values (10) 602
 
8.3%
Latin
ValueCountFrequency (%)
c 26
25.7%
t 13
12.9%
v 13
12.9%
U 11
10.9%
C 8
 
7.9%
F 8
 
7.9%
L 8
 
7.9%
T 5
 
5.0%
N 4
 
4.0%
m 1
 
1.0%
Other values (4) 4
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13999
65.6%
ASCII 7343
34.4%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3535
48.1%
0 622
 
8.5%
2 514
 
7.0%
. 511
 
7.0%
1 507
 
6.9%
( 219
 
3.0%
) 219
 
3.0%
: 192
 
2.6%
- 170
 
2.3%
3 151
 
2.1%
Other values (24) 703
 
9.6%
Hangul
ValueCountFrequency (%)
1150
 
8.2%
591
 
4.2%
558
 
4.0%
471
 
3.4%
427
 
3.1%
409
 
2.9%
353
 
2.5%
339
 
2.4%
326
 
2.3%
324
 
2.3%
Other values (283) 9051
64.7%
Compat Jamo
ValueCountFrequency (%)
1
100.0%
Distinct83
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
2024-05-04T03:30:35.575172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length33
Mean length6.625
Min length2

Characters and Unicode

Total characters6413
Distinct characters76
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)5.8%

Sample

1st row직권말소
2nd row직권말소
3rd row과징금부과
4th row직권말소
5th row직권말소
ValueCountFrequency (%)
개선명령 167
13.0%
영업소폐쇄 163
12.7%
경고 156
12.2%
과태료부과 135
10.5%
영업소폐쇄(직권말소 109
8.5%
직권말소 69
 
5.4%
67
 
5.2%
과태료 65
 
5.1%
부과 47
 
3.7%
영업정지 46
 
3.6%
Other values (91) 257
20.1%
2024-05-04T03:30:36.893881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
564
 
8.8%
463
 
7.2%
367
 
5.7%
358
 
5.6%
315
 
4.9%
284
 
4.4%
283
 
4.4%
258
 
4.0%
211
 
3.3%
211
 
3.3%
Other values (66) 3099
48.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5370
83.7%
Decimal Number 383
 
6.0%
Space Separator 315
 
4.9%
Open Punctuation 144
 
2.2%
Close Punctuation 144
 
2.2%
Other Punctuation 53
 
0.8%
Math Symbol 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
564
 
10.5%
463
 
8.6%
367
 
6.8%
358
 
6.7%
284
 
5.3%
283
 
5.3%
258
 
4.8%
211
 
3.9%
211
 
3.9%
179
 
3.3%
Other values (50) 2192
40.8%
Decimal Number
ValueCountFrequency (%)
0 153
39.9%
1 61
 
15.9%
2 57
 
14.9%
5 29
 
7.6%
3 24
 
6.3%
6 17
 
4.4%
4 13
 
3.4%
8 11
 
2.9%
9 10
 
2.6%
7 8
 
2.1%
Other Punctuation
ValueCountFrequency (%)
, 38
71.7%
. 15
 
28.3%
Space Separator
ValueCountFrequency (%)
315
100.0%
Open Punctuation
ValueCountFrequency (%)
( 144
100.0%
Close Punctuation
ValueCountFrequency (%)
) 144
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5370
83.7%
Common 1043
 
16.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
564
 
10.5%
463
 
8.6%
367
 
6.8%
358
 
6.7%
284
 
5.3%
283
 
5.3%
258
 
4.8%
211
 
3.9%
211
 
3.9%
179
 
3.3%
Other values (50) 2192
40.8%
Common
ValueCountFrequency (%)
315
30.2%
0 153
14.7%
( 144
13.8%
) 144
13.8%
1 61
 
5.8%
2 57
 
5.5%
, 38
 
3.6%
5 29
 
2.8%
3 24
 
2.3%
6 17
 
1.6%
Other values (6) 61
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5370
83.7%
ASCII 1043
 
16.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
564
 
10.5%
463
 
8.6%
367
 
6.8%
358
 
6.7%
284
 
5.3%
283
 
5.3%
258
 
4.8%
211
 
3.9%
211
 
3.9%
179
 
3.3%
Other values (50) 2192
40.8%
ASCII
ValueCountFrequency (%)
315
30.2%
0 153
14.7%
( 144
13.8%
) 144
13.8%
1 61
 
5.8%
2 57
 
5.5%
, 38
 
3.6%
5 29
 
2.8%
3 24
 
2.3%
6 17
 
1.6%
Other values (6) 61
 
5.8%

처분기간
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
<NA>
931 
15
 
19
29
 
7
10
 
5
5
 
4

Length

Max length4
Median length4
Mean length3.9173554
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 931
96.2%
15 19
 
2.0%
29 7
 
0.7%
10 5
 
0.5%
5 4
 
0.4%
0 2
 
0.2%

Length

2024-05-04T03:30:37.786916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T03:30:38.212061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 931
96.2%
15 19
 
2.0%
29 7
 
0.7%
10 5
 
0.5%
5 4
 
0.4%
0 2
 
0.2%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct395
Distinct (%)49.6%
Missing172
Missing (%)17.8%
Infinite0
Infinite (%)0.0%
Mean552.76608
Minimum0
Maximum29316
Zeros8
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2024-05-04T03:30:38.666382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q133
median90.43
Q3691.2
95-th percentile1920.14
Maximum29316
Range29316
Interquartile range (IQR)658.2

Descriptive statistics

Standard deviation1718.5433
Coefficient of variation (CV)3.1089884
Kurtosis199.10009
Mean552.76608
Median Absolute Deviation (MAD)68.33
Skewness12.558558
Sum440001.8
Variance2953391.2
MonotonicityNot monotonic
2024-05-04T03:30:39.167593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.0 26
 
2.7%
33.0 25
 
2.6%
1879.47 15
 
1.5%
132.0 13
 
1.3%
20.0 11
 
1.1%
886.59 9
 
0.9%
1028.16 9
 
0.9%
0.0 8
 
0.8%
1920.14 8
 
0.8%
937.78 8
 
0.8%
Other values (385) 664
68.6%
(Missing) 172
 
17.8%
ValueCountFrequency (%)
0.0 8
0.8%
3.3 1
 
0.1%
4.95 2
 
0.2%
5.0 1
 
0.1%
6.0 3
 
0.3%
6.6 1
 
0.1%
8.0 1
 
0.1%
8.7 1
 
0.1%
9.9 3
 
0.3%
10.8 1
 
0.1%
ValueCountFrequency (%)
29316.0 2
 
0.2%
10985.1 1
 
0.1%
8780.0 1
 
0.1%
5932.92 4
0.4%
5244.88 1
 
0.1%
4690.8 2
 
0.2%
4234.09 2
 
0.2%
3488.38 6
0.6%
3169.0 1
 
0.1%
2975.0 2
 
0.2%

Interactions

2024-05-04T03:30:14.694215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:30:11.646287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:30:12.694266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:30:13.723592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:30:14.870862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:30:11.905596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:30:12.866779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:30:13.992417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:30:15.047701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:30:12.165671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:30:13.191739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:30:14.253104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:30:15.224547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:30:12.429496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:30:13.456885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:30:14.475319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T03:30:39.514202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분일자업종명업태명지도점검일자처분명위반일자처분내용처분기간영업장면적(㎡)
처분일자1.0000.6870.7020.9990.9070.8800.9070.7830.178
업종명0.6871.0000.9530.6970.8110.6800.8110.9360.130
업태명0.7020.9531.0000.7140.6820.6000.6820.8980.619
지도점검일자0.9990.6970.7141.0000.9090.8960.9090.9540.000
처분명0.9070.8110.6820.9091.0000.7951.0000.7660.449
위반일자0.8800.6800.6000.8960.7951.0000.7950.8750.143
처분내용0.9070.8110.6820.9091.0000.7951.0000.7660.449
처분기간0.7830.9360.8980.9540.7660.8750.7661.0000.665
영업장면적(㎡)0.1780.1300.6190.0000.4490.1430.4490.6651.000
2024-05-04T03:30:39.868928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업태명처분기간
업종명1.0000.7150.646
업태명0.7151.0000.815
처분기간0.6460.8151.000
2024-05-04T03:30:40.144036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분일자지도점검일자위반일자영업장면적(㎡)업종명업태명처분기간
처분일자1.0000.9950.844-0.3750.3520.3510.654
지도점검일자0.9951.0000.849-0.3770.3590.3570.643
위반일자0.8440.8491.000-0.3160.3070.3020.522
영업장면적(㎡)-0.375-0.377-0.3161.0000.0660.3620.603
업종명0.3520.3590.3070.0661.0000.7150.646
업태명0.3510.3570.3020.3620.7151.0000.815
처분기간0.6540.6430.5220.6030.6460.8151.000

Missing values

2024-05-04T03:30:15.623434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T03:30:16.326768image/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.
2024-05-04T03:30:16.823169image/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(㎡)
03230000202404292022-00001세탁업일반세탁업우성명품세탁서울특별시 송파구 송파대로32길 8, 1층 6호 (가락동, 가락우성아파트)서울특별시 송파구 가락동 96번지 1호 가락우성아파트20240315처분확정직권말소법 제11조제3항제2호20230614폐업 미신고직권말소<NA>15.2
1323000020240416271세탁업일반세탁업아이파크 세탁서울특별시 송파구 법원로 55, 1층 E존202호 (문정동)서울특별시 송파구 문정동 624번지20240315처분확정직권말소법 제11조제3항제2호20230713폐업 미신고직권말소<NA>0.0
23230000202403122003-01-004숙박업(일반)여인숙업대영서울특별시 송파구 마천로 260, (거여동)서울특별시 송파구 거여동 129번지 84호20240223처분확정과징금부과법 제11조제1항제8호20230727청소년 이성혼숙과징금부과<NA>30.87
33230000202401162014-00120피부미용업피부미용업뷰티갤러리서울특별시 송파구 오금로46길 18-1, 지상1층 (가락동)서울특별시 송파구 가락동 174번지 25호 지상1층20231222처분확정직권말소법 제11조제3항제2호20231213사업자 폐업신고 후 영업신고 폐업 미이행직권말소<NA>26.4
432300002024011600076일반미용업, 네일미용업일반미용업OWN#(오운샵)서울특별시 송파구 위례광장로 188, 아이온스퀘어 1층 132호 (장지동)서울특별시 송파구 장지동 881번지 아이온스퀘어20231222처분확정직권말소법 제11조제3항제2호20230731사업자 등록 폐업신고 후 영업신고 폐업 미이행직권말소<NA>43.65
53230000202312182018-00010위생관리용역업위생관리용역업케이비 페더럴 매인텐언스서울특별시 송파구 삼전로 70, 203호 (삼전동)서울특별시 송파구 삼전동 7번지 5호20231129처분확정직권말소법 제11조제3항제2호20201105직권말소직권말소<NA>33.0
63230000202312182014-00002목욕장업공동탕업+찜질시설서비스영업송파여성소금한증막서울특별시 송파구 가락로 120, 지하1층 (석촌동)서울특별시 송파구 석촌동 296번지 4호20231019처분확정경고법 제11조제1항제4호2023101922:00 이후부터 05:00까지 청소년의 영업소 출입을 제한하지 아니함 (2023.10.19. 서울송파경찰서 적발, 2023.11.7. 송파구 보건위생과 통보)경고<NA>665.33
73230000202311162015-00007위생관리용역업위생관리용역업지(G)-크린서울특별시 송파구 송이로31길 52, 108호 (문정동, 문정시영아파트상가)서울특별시 송파구 문정동 145번지 문정시영아파트상가-10820231030처분확정직권말소법 제3조3항20221228사업자등록 폐업후 영업 폐업신고 미이행직권말소<NA>35.65
83230000202311132009-06-04위생관리용역업위생관리용역업(주)유엔에스서울특별시 송파구 가락로 23, (석촌동,운포빌딩 2층)서울특별시 송파구 석촌동 226번지 16호 운포빌딩 2층20231025처분확정직권말소법 제3조3항20110623사업자등록 폐업 후 영업 폐업신고 미이행직권말소<NA>132.0
93230000202311132016-00022위생관리용역업위생관리용역업반짝반짝 크린서울특별시 송파구 오금로11길 55, 2층 B204호 (방이동)서울특별시 송파구 방이동 48번지 5호20231025처분확정직권말소법 제3조3항20210531사업자등록 폐업 후 영업 폐업신고 미이행직권말소<NA>16.0
시군구코드처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자행정처분상태처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)
9583230000200311282003-04-194이용업일반이용업에이스서울특별시 송파구 송이로30길 9, (문정동)서울특별시 송파구 문정동 107번지 4호20031016처분확정영업정지15일에 갈음하는 과징금 450,000원 부과공중위생관리법제3조 제1항20031016칸막이설치영업정지15일에 갈음하는 과징금 450,000원 부과15<NA>
9593230000200311282003-04-129이용업일반이용업세븐서울특별시 송파구 양재대로66길 22, (가락동)서울특별시 송파구 가락동 29번지 14호20031013처분확정과징금 615,000원 부과공중위생관리법 제 3조제1항20031014칸막이 설치과징금 615,000원 부과15<NA>
9603230000200311272003-04-133이용업일반이용업성환서울특별시 송파구 양재대로62길 37-1, (가락동)서울특별시 송파구 가락동 75번지 13호20031015처분확정개선명령공중위생관리법 제3조 제3호20031015시설기준위반(칸막이 설치)개선명령<NA><NA>
9613230000200311272003-04-271이용업일반이용업뉴월드서울특별시 송파구 송파대로 422, (송파동)서울특별시 송파구 송파동 84번지 7호20030923처분확정개선명령공중위생관리법 제3조 제1항20030923시설기준위반(칸막이 설치)개선명령<NA><NA>
9623230000200311272003-04-167이용업일반이용업바다서울특별시 송파구 양산로10길 4, (거여동)서울특별시 송파구 거여동 565번지 2호20031010처분확정개선명령공중위생관리법 제3조 제1항20031010시설기준위반(커텐설치)개선명령<NA><NA>
9633230000200311192003-04-259이용업일반이용업월광서울특별시 송파구 오금로 400, (가락동)서울특별시 송파구 가락동 166번지 1호20030822처분확정영업정지1월에 갈음하는 과징금 1,230,000 원부과공중위생솬리법 제11조제1항20030822무자격안마(의료법위반)영업정지1월에 갈음하는 과징금 1,230,000 원부과<NA><NA>
9643230000200311192003-04-068이용업일반이용업영동서울특별시 송파구 백제고분로 423, (송파동)서울특별시 송파구 송파동 47번지 3호20030913처분확정영업정지 및 업무정지공중위생관리법 제11조 제1항20030913음란행위 알서 및 장소제공영업정지 및 업무정지<NA>31.87
9653230000200311172003-04-089이용업일반이용업청실서울특별시 송파구 송파대로 369, (석촌동)서울특별시 송파구 석촌동 296번지 15호20031010처분확정개선명령공중위생관리법 제3조제1항20031010칸막이설치(시설기준위반)개선명령<NA>52.81
9663230000200311172003-04-186이용업일반이용업바다서울특별시 송파구 마천로 53, (오금동)서울특별시 송파구 오금동 17번지 8호20030926처분확정영업정지1월에 갈음하는 과징금 1,560,000원 부과공중위생관리법 제11조제1항20030926의료법위반(무자격안마)영업정지1월에 갈음하는 과징금 1,560,000원 부과<NA><NA>
9673230000200311172003-04-117이용업일반이용업뉴한일서울특별시 송파구 백제고분로 75, (잠실동)서울특별시 송파구 잠실동 196번지 0호20031016처분확정개선명령공중위생관리법 제 3조제1항20031016칸막이설치(커텐설치)개선명령<NA>52.92

Duplicate rows

Most frequently occurring

시군구코드처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자행정처분상태처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)# duplicates
23230000200512122003-04-139이용업일반이용업거북이서울특별시 송파구 송파대로28길 13, (가락동)서울특별시 송파구 가락동 98번지 7호20050922처분확정영업정지 1월 갈음 123만원공중위생관리법제11조20050922무자격안마사의 안마행위영업정지 1월 갈음 123만원<NA>21.04
543230000201408042003-02-136목욕장업공동탕업가락한양사우나서울특별시 송파구 가락로 183, (송파동,B층)서울특별시 송파구 송파동 121번지 B층20140619처분확정개선명령법 제4조제2항20140619욕수의 수질기준에 적합하게 욕수를 유지한지 않음개선명령<NA><NA>4
553230000201408042003-02-136목욕장업공동탕업가락한양사우나서울특별시 송파구 가락로 183, (송파동,B층)서울특별시 송파구 송파동 121번지 B층20140619처분확정과태료부과법 제4조제2항20140619욕수의 수질기준에 적합하게 욕수를 유지한지 않음과태료부과<NA><NA>4
563230000201504022014-00034일반미용업일반미용업까끌래 뽀끌래서울특별시 송파구 마천로43길 14, 지상1층 (마천동)서울특별시 송파구 마천동 124번지 27호 지상1층20150303처분확정경고 및 과태료 부과법 제17조201503032014. 위생교육 미필경고 및 과태료 부과<NA>15.54
573230000201504022014-00781일반미용업일반미용업내몸사랑서울특별시 송파구 송파대로28길 32, 지상2층 203호 (가락동, 현진오피스텔)서울특별시 송파구 가락동 79번지 7호 현진오피스텔 203호20150407처분확정경고 및 과태료법 제17조201503032014. 위생교육 미이수경고 및 과태료<NA>29.523
03230000200403172003-01-119숙박업(일반)관광호텔잠실관광호텔서울특별시 송파구 삼전로 67, (잠실동)서울특별시 송파구 잠실동 250번지 9호20030417처분확정과징금부과공중위생관리법 제11조 제1항20030417윤락장소 제공과징금부과103488.382
13230000200403172003-01-119숙박업(일반)관광호텔잠실관광호텔서울특별시 송파구 삼전로 67, (잠실동)서울특별시 송파구 잠실동 250번지 9호20030417처분확정영업정지 10일 및 과징금 1,000만원공중위생관리법 제11조 제1항20030417윤락장소 제공영업정지 10일 및 과징금 1,000만원103488.382
33230000200602062003-04-370이용업일반이용업테크노서울특별시 송파구 중대로 188, (가락동)서울특별시 송파구 가락동 155번지 2호20051111처분확정영업정지공중생관리법제11조20051111윤락행위, 무자격안마영업정지15132.02
43230000200602062003-04-370이용업일반이용업테크노서울특별시 송파구 중대로 188, (가락동)서울특별시 송파구 가락동 155번지 2호20051111처분확정영업정지공중위생관리법 제11조20051111윤락행위 및 무자격안마영업정지15132.02
53230000200602062003-04-370이용업일반이용업테크노서울특별시 송파구 중대로 188, (가락동)서울특별시 송파구 가락동 155번지 2호20051111처분확정영업정지공중위생관리법제11조20051111윤락행위영업정지15132.02