Overview

Dataset statistics

Number of variables14
Number of observations352
Missing cells964
Missing cells (%)19.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.3 KiB
Average record size in memory117.4 B

Variable types

Categorical4
Text4
DateTime1
Unsupported2
Numeric3

Dataset

Description휴게음식점(유원지) 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=9H2IBYIJLBX2DSEH099313475934&infSeq=1

Alerts

위생업태명 has constant value ""Constant
위생업종명 is highly overall correlated with 소재지우편번호 and 4 other fieldsHigh correlation
영업상태명 is highly overall correlated with 위생업종명High correlation
시군명 is highly overall correlated with 소재지우편번호 and 3 other fieldsHigh correlation
소재지우편번호 is highly overall correlated with WGS84위도 and 3 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
시군명 is highly imbalanced (75.2%)Imbalance
폐업일자 has 159 (45.2%) missing valuesMissing
다중이용업소여부 has 352 (100.0%) missing valuesMissing
총시설규모(㎡) has 352 (100.0%) missing valuesMissing
소재지도로명주소 has 37 (10.5%) missing valuesMissing
소재지우편번호 has 12 (3.4%) missing valuesMissing
WGS84위도 has 26 (7.4%) missing valuesMissing
WGS84경도 has 26 (7.4%) missing valuesMissing
다중이용업소여부 is an unsupported type, check if it needs cleaning or further analysisUnsupported
총시설규모(㎡) is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-03 19:28:47.226446
Analysis finished2024-05-03 19:28:52.819965
Duration5.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct14
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
용인시
306 
과천시
 
19
광명시
 
4
김포시
 
4
연천군
 
4
Other values (9)
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique5 ?
Unique (%)1.4%

Sample

1st row가평군
2nd row과천시
3rd row과천시
4th row과천시
5th row과천시

Common Values

ValueCountFrequency (%)
용인시 306
86.9%
과천시 19
 
5.4%
광명시 4
 
1.1%
김포시 4
 
1.1%
연천군 4
 
1.1%
안산시 3
 
0.9%
파주시 3
 
0.9%
시흥시 2
 
0.6%
평택시 2
 
0.6%
가평군 1
 
0.3%
Other values (4) 4
 
1.1%

Length

2024-05-03T19:28:53.032948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
용인시 306
86.9%
과천시 19
 
5.4%
광명시 4
 
1.1%
김포시 4
 
1.1%
연천군 4
 
1.1%
안산시 3
 
0.9%
파주시 3
 
0.9%
시흥시 2
 
0.6%
평택시 2
 
0.6%
가평군 1
 
0.3%
Other values (4) 4
 
1.1%

사업장명
Text

Distinct274
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2024-05-03T19:28:53.638509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length20
Mean length8.2727273
Min length2

Characters and Unicode

Total characters2912
Distinct characters298
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

Unique204 ?
Unique (%)58.0%

Sample

1st row달달하:주(ZOO)
2nd row카페베네
3rd row기린나라커피숍
4th row후토스카페
5th row라이언카페
ValueCountFrequency (%)
휴게1점 25
 
5.0%
휴게2점 20
 
4.0%
휴게3점 19
 
3.8%
휴게4점 13
 
2.6%
스노우버스터 12
 
2.4%
콜럼버스 9
 
1.8%
휴게5점 9
 
1.8%
글로벌페어 9
 
1.8%
스낵버스터 7
 
1.4%
그랜드스테이지 7
 
1.4%
Other values (246) 367
73.8%
2024-05-03T19:28:54.591385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 235
 
8.1%
스 169
 
5.8%
휴 164
 
5.6%
게 162
 
5.6%
145
 
5.0%
1 63
 
2.2%
2 58
 
2.0%
버 57
 
2.0%
드 51
 
1.8%
리 50
 
1.7%
Other values (288) 1758
60.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2252
77.3%
Decimal Number 241
 
8.3%
Space Separator 145
 
5.0%
Uppercase Letter 126
 
4.3%
Lowercase Letter 84
 
2.9%
Close Punctuation 25
 
0.9%
Open Punctuation 25
 
0.9%
Dash Punctuation 11
 
0.4%
Other Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 235
 
10.4%
스 169
 
7.5%
휴 164
 
7.3%
게 162
 
7.2%
버 57
 
2.5%
드 51
 
2.3%
리 50
 
2.2%
이 46
 
2.0%
낵 42
 
1.9%
터 36
 
1.6%
Other values (235) 1240
55.1%
Uppercase Letter
ValueCountFrequency (%)
S 15
11.9%
O 13
10.3%
C 12
9.5%
A 11
8.7%
K 10
 
7.9%
I 10
 
7.9%
E 8
 
6.3%
L 8
 
6.3%
F 7
 
5.6%
R 6
 
4.8%
Other values (9) 26
20.6%
Lowercase Letter
ValueCountFrequency (%)
o 12
14.3%
p 11
13.1%
n 9
10.7%
u 7
8.3%
t 7
8.3%
i 7
8.3%
e 6
7.1%
c 5
6.0%
h 5
6.0%
a 4
 
4.8%
Other values (8) 11
13.1%
Decimal Number
ValueCountFrequency (%)
1 63
26.1%
2 58
24.1%
3 43
17.8%
4 25
 
10.4%
5 22
 
9.1%
6 14
 
5.8%
7 9
 
3.7%
8 4
 
1.7%
0 2
 
0.8%
9 1
 
0.4%
Other Punctuation
ValueCountFrequency (%)
, 2
66.7%
: 1
33.3%
Space Separator
ValueCountFrequency (%)
145
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2252
77.3%
Common 450
 
15.5%
Latin 210
 
7.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 235
 
10.4%
스 169
 
7.5%
휴 164
 
7.3%
게 162
 
7.2%
버 57
 
2.5%
드 51
 
2.3%
리 50
 
2.2%
이 46
 
2.0%
낵 42
 
1.9%
터 36
 
1.6%
Other values (235) 1240
55.1%
Latin
ValueCountFrequency (%)
S 15
 
7.1%
O 13
 
6.2%
C 12
 
5.7%
o 12
 
5.7%
p 11
 
5.2%
A 11
 
5.2%
K 10
 
4.8%
I 10
 
4.8%
n 9
 
4.3%
E 8
 
3.8%
Other values (27) 99
47.1%
Common
ValueCountFrequency (%)
145
32.2%
1 63
14.0%
2 58
 
12.9%
3 43
 
9.6%
) 25
 
5.6%
4 25
 
5.6%
( 25
 
5.6%
5 22
 
4.9%
6 14
 
3.1%
- 11
 
2.4%
Other values (6) 19
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2252
77.3%
ASCII 660
 
22.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 235
 
10.4%
스 169
 
7.5%
휴 164
 
7.3%
게 162
 
7.2%
버 57
 
2.5%
드 51
 
2.3%
리 50
 
2.2%
이 46
 
2.0%
낵 42
 
1.9%
터 36
 
1.6%
Other values (235) 1240
55.1%
ASCII
ValueCountFrequency (%)
145
22.0%
1 63
 
9.5%
2 58
 
8.8%
3 43
 
6.5%
) 25
 
3.8%
4 25
 
3.8%
( 25
 
3.8%
5 22
 
3.3%
S 15
 
2.3%
6 14
 
2.1%
Other values (43) 225
34.1%

인허가일자
Text

Distinct171
Distinct (%)48.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2024-05-03T19:28:55.217927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length8.4034091
Min length8

Characters and Unicode

Total characters2958
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

Unique113 ?
Unique (%)32.1%

Sample

1st row2021-01-05
2nd row20120723
3rd row20141023
4th row20120706
5th row20141226
ValueCountFrequency (%)
19990817 33
 
9.4%
19990709 27
 
7.7%
19990806 17
 
4.8%
20040713 9
 
2.6%
19991209 7
 
2.0%
20040517 6
 
1.7%
20140828 6
 
1.7%
1999-08-17 5
 
1.4%
20140430 4
 
1.1%
20110527 4
 
1.1%
Other values (161) 234
66.5%
2024-05-03T19:28:56.110610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 760
25.7%
1 530
17.9%
9 424
14.3%
2 393
13.3%
7 174
 
5.9%
8 147
 
5.0%
- 142
 
4.8%
3 119
 
4.0%
4 100
 
3.4%
6 90
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2816
95.2%
Dash Punctuation 142
 
4.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 760
27.0%
1 530
18.8%
9 424
15.1%
2 393
14.0%
7 174
 
6.2%
8 147
 
5.2%
3 119
 
4.2%
4 100
 
3.6%
6 90
 
3.2%
5 79
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2958
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 760
25.7%
1 530
17.9%
9 424
14.3%
2 393
13.3%
7 174
 
5.9%
8 147
 
5.0%
- 142
 
4.8%
3 119
 
4.0%
4 100
 
3.4%
6 90
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2958
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 760
25.7%
1 530
17.9%
9 424
14.3%
2 393
13.3%
7 174
 
5.9%
8 147
 
5.0%
- 142
 
4.8%
3 119
 
4.0%
4 100
 
3.4%
6 90
 
3.0%

영업상태명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
폐업 등
186 
운영중
86 
영업
73 
폐업
 
7

Length

Max length4
Median length4
Mean length3.3011364
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영업
2nd row영업
3rd row운영중
4th row운영중
5th row운영중

Common Values

ValueCountFrequency (%)
폐업 등 186
52.8%
운영중 86
24.4%
영업 73
 
20.7%
폐업 7
 
2.0%

Length

2024-05-03T19:28:56.406717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T19:28:56.751169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 193
35.9%
등 186
34.6%
운영중 86
16.0%
영업 73
 
13.6%

폐업일자
Date

MISSING 

Distinct83
Distinct (%)43.0%
Missing159
Missing (%)45.2%
Memory size2.9 KiB
Minimum1999-03-30 00:00:00
Maximum2023-08-20 00:00:00
2024-05-03T19:28:57.040849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:28:57.361133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

다중이용업소여부
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing352
Missing (%)100.0%
Memory size3.2 KiB

총시설규모(㎡)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing352
Missing (%)100.0%
Memory size3.2 KiB

위생업종명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
휴게음식점
272 
<NA>
80 

Length

Max length5
Median length5
Mean length4.7727273
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
휴게음식점 272
77.3%
<NA> 80
 
22.7%

Length

2024-05-03T19:28:57.713916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T19:28:58.056043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
휴게음식점 272
77.3%
na 80
 
22.7%

위생업태명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
유원지
352 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
유원지 352
100.0%

Length

2024-05-03T19:28:58.334445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T19:28:58.593571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유원지 352
100.0%

소재지도로명주소
Text

MISSING 

Distinct86
Distinct (%)27.3%
Missing37
Missing (%)10.5%
Memory size2.9 KiB
2024-05-03T19:28:58.888019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length25
Mean length27.866667
Min length15

Characters and Unicode

Total characters8778
Distinct characters170
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

Unique52 ?
Unique (%)16.5%

Sample

1st row경기도 가평군 상면 임초밤안골로 301, 1동 2층
2nd row경기도 과천시 광명로 140 (막계동, (서울랜드 정문앞))
3rd row경기도 과천시 광명로 140, 서울대공원내 2층 (막계동)
4th row경기도 과천시 광명로 181 (막계동, 서울랜드 후문)
5th row경기도 과천시 광명로 181 (막계동, 서울대공원(사자우리))
ValueCountFrequency (%)
경기도 315
15.4%
용인시 280
13.6%
처인구 272
13.3%
포곡읍 268
13.1%
에버랜드로 267
13.0%
199 261
12.7%
가동 19
 
0.9%
과천시 16
 
0.8%
광명로 13
 
0.6%
외1필지 11
 
0.5%
Other values (143) 330
16.1%
2024-05-03T19:28:59.575513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1737
19.8%
인 552
 
6.3%
9 538
 
6.1%
1 354
 
4.0%
기 329
 
3.7%
경 316
 
3.6%
도 316
 
3.6%
로 314
 
3.6%
시 312
 
3.6%
구 286
 
3.3%
Other values (160) 3724
42.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5675
64.7%
Space Separator 1737
 
19.8%
Decimal Number 1050
 
12.0%
Other Punctuation 111
 
1.3%
Close Punctuation 79
 
0.9%
Open Punctuation 79
 
0.9%
Uppercase Letter 36
 
0.4%
Dash Punctuation 11
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 552
 
9.7%
기 329
 
5.8%
경 316
 
5.6%
도 316
 
5.6%
로 314
 
5.5%
시 312
 
5.5%
구 286
 
5.0%
드 280
 
4.9%
용 280
 
4.9%
랜 279
 
4.9%
Other values (134) 2411
42.5%
Uppercase Letter
ValueCountFrequency (%)
B 9
25.0%
A 8
22.2%
O 4
11.1%
C 3
 
8.3%
P 3
 
8.3%
D 2
 
5.6%
E 2
 
5.6%
M 2
 
5.6%
Z 1
 
2.8%
N 1
 
2.8%
Decimal Number
ValueCountFrequency (%)
9 538
51.2%
1 354
33.7%
2 53
 
5.0%
0 27
 
2.6%
8 23
 
2.2%
5 18
 
1.7%
3 12
 
1.1%
4 11
 
1.0%
7 8
 
0.8%
6 6
 
0.6%
Space Separator
ValueCountFrequency (%)
1737
100.0%
Other Punctuation
ValueCountFrequency (%)
, 111
100.0%
Close Punctuation
ValueCountFrequency (%)
) 79
100.0%
Open Punctuation
ValueCountFrequency (%)
( 79
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5675
64.7%
Common 3067
34.9%
Latin 36
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 552
 
9.7%
기 329
 
5.8%
경 316
 
5.6%
도 316
 
5.6%
로 314
 
5.5%
시 312
 
5.5%
구 286
 
5.0%
드 280
 
4.9%
용 280
 
4.9%
랜 279
 
4.9%
Other values (134) 2411
42.5%
Common
ValueCountFrequency (%)
1737
56.6%
9 538
 
17.5%
1 354
 
11.5%
, 111
 
3.6%
) 79
 
2.6%
( 79
 
2.6%
2 53
 
1.7%
0 27
 
0.9%
8 23
 
0.7%
5 18
 
0.6%
Other values (5) 48
 
1.6%
Latin
ValueCountFrequency (%)
B 9
25.0%
A 8
22.2%
O 4
11.1%
C 3
 
8.3%
P 3
 
8.3%
D 2
 
5.6%
E 2
 
5.6%
M 2
 
5.6%
Z 1
 
2.8%
N 1
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5675
64.7%
ASCII 3103
35.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1737
56.0%
9 538
 
17.3%
1 354
 
11.4%
, 111
 
3.6%
) 79
 
2.5%
( 79
 
2.5%
2 53
 
1.7%
0 27
 
0.9%
8 23
 
0.7%
5 18
 
0.6%
Other values (16) 84
 
2.7%
Hangul
ValueCountFrequency (%)
인 552
 
9.7%
기 329
 
5.8%
경 316
 
5.6%
도 316
 
5.6%
로 314
 
5.5%
시 312
 
5.5%
구 286
 
5.0%
드 280
 
4.9%
용 280
 
4.9%
랜 279
 
4.9%
Other values (134) 2411
42.5%

소재지지번주소
Text

Distinct184
Distinct (%)52.3%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2024-05-03T19:28:59.983081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length66
Median length47
Mean length28.21875
Min length16

Characters and Unicode

Total characters9933
Distinct characters186
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique146 ?
Unique (%)41.5%

Sample

1st row경기도 가평군 상면 임초리 622-13 외 5필지(622-2, 622-3, 622-12, 622-14, 622-15)
2nd row경기도 과천시 막계동 433
3rd row경기도 과천시 막계동 산 118-3번지
4th row경기도 과천시 막계동 33번지 서울랜드 후문
5th row경기도 과천시 막계동 662번지 서울대공원(사자우리)
ValueCountFrequency (%)
경기도 352
15.6%
용인시 306
13.5%
처인구 296
13.1%
포곡읍 292
12.9%
전대리 106
 
4.7%
가실리 102
 
4.5%
유운리 83
 
3.7%
551-1번지 36
 
1.6%
104번지 32
 
1.4%
가동 20
 
0.9%
Other values (242) 638
28.2%
2024-05-03T19:29:00.861722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1991
20.0%
인 602
 
6.1%
1 455
 
4.6%
기 367
 
3.7%
도 353
 
3.6%
경 352
 
3.5%
시 350
 
3.5%
지 337
 
3.4%
구 313
 
3.2%
리 312
 
3.1%
Other values (176) 4501
45.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6189
62.3%
Space Separator 1991
 
20.0%
Decimal Number 1469
 
14.8%
Dash Punctuation 215
 
2.2%
Uppercase Letter 36
 
0.4%
Other Punctuation 23
 
0.2%
Letter Number 4
 
< 0.1%
Close Punctuation 3
 
< 0.1%
Open Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 602
 
9.7%
기 367
 
5.9%
도 353
 
5.7%
경 352
 
5.7%
시 350
 
5.7%
지 337
 
5.4%
구 313
 
5.1%
리 312
 
5.0%
용 306
 
4.9%
곡 305
 
4.9%
Other values (148) 2592
41.9%
Uppercase Letter
ValueCountFrequency (%)
B 9
25.0%
A 8
22.2%
O 4
11.1%
P 3
 
8.3%
E 2
 
5.6%
C 2
 
5.6%
M 2
 
5.6%
D 2
 
5.6%
Z 1
 
2.8%
L 1
 
2.8%
Other values (2) 2
 
5.6%
Decimal Number
ValueCountFrequency (%)
1 455
31.0%
5 305
20.8%
4 144
 
9.8%
2 136
 
9.3%
0 93
 
6.3%
3 86
 
5.9%
9 80
 
5.4%
8 77
 
5.2%
6 73
 
5.0%
7 20
 
1.4%
Space Separator
ValueCountFrequency (%)
1991
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 215
100.0%
Other Punctuation
ValueCountFrequency (%)
, 23
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6189
62.3%
Common 3704
37.3%
Latin 40
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 602
 
9.7%
기 367
 
5.9%
도 353
 
5.7%
경 352
 
5.7%
시 350
 
5.7%
지 337
 
5.4%
구 313
 
5.1%
리 312
 
5.0%
용 306
 
4.9%
곡 305
 
4.9%
Other values (148) 2592
41.9%
Common
ValueCountFrequency (%)
1991
53.8%
1 455
 
12.3%
5 305
 
8.2%
- 215
 
5.8%
4 144
 
3.9%
2 136
 
3.7%
0 93
 
2.5%
3 86
 
2.3%
9 80
 
2.2%
8 77
 
2.1%
Other values (5) 122
 
3.3%
Latin
ValueCountFrequency (%)
B 9
22.5%
A 8
20.0%
O 4
10.0%
Ⅰ 4
10.0%
P 3
 
7.5%
E 2
 
5.0%
C 2
 
5.0%
M 2
 
5.0%
D 2
 
5.0%
Z 1
 
2.5%
Other values (3) 3
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6189
62.3%
ASCII 3740
37.7%
Number Forms 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1991
53.2%
1 455
 
12.2%
5 305
 
8.2%
- 215
 
5.7%
4 144
 
3.9%
2 136
 
3.6%
0 93
 
2.5%
3 86
 
2.3%
9 80
 
2.1%
8 77
 
2.1%
Other values (17) 158
 
4.2%
Hangul
ValueCountFrequency (%)
인 602
 
9.7%
기 367
 
5.9%
도 353
 
5.7%
경 352
 
5.7%
시 350
 
5.7%
지 337
 
5.4%
구 313
 
5.1%
리 312
 
5.0%
용 306
 
4.9%
곡 305
 
4.9%
Other values (148) 2592
41.9%
Number Forms
ValueCountFrequency (%)
Ⅰ 4
100.0%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)7.9%
Missing12
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean16538.309
Minimum10090
Maximum17973
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-05-03T19:29:01.245274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10090
5-th percentile13829
Q117023
median17023
Q317023
95-th percentile17023
Maximum17973
Range7883
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1428.142
Coefficient of variation (CV)0.086353571
Kurtosis8.7323276
Mean16538.309
Median Absolute Deviation (MAD)0
Skewness-3.0362398
Sum5623025
Variance2039589.6
MonotonicityNot monotonic
2024-05-03T19:29:01.633215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
17023 277
78.7%
13829 17
 
4.8%
17075 6
 
1.7%
17164 4
 
1.1%
10090 4
 
1.1%
11027 4
 
1.1%
14341 4
 
1.1%
17021 3
 
0.9%
15010 2
 
0.6%
10801 2
 
0.6%
Other values (17) 17
 
4.8%
(Missing) 12
 
3.4%
ValueCountFrequency (%)
10090 4
 
1.1%
10801 2
 
0.6%
10953 1
 
0.3%
11027 4
 
1.1%
11123 1
 
0.3%
12447 1
 
0.3%
12634 1
 
0.3%
13822 1
 
0.3%
13829 17
4.8%
13837 1
 
0.3%
ValueCountFrequency (%)
17973 1
 
0.3%
17714 1
 
0.3%
17558 1
 
0.3%
17164 4
 
1.1%
17087 1
 
0.3%
17075 6
 
1.7%
17028 1
 
0.3%
17023 277
78.7%
17021 3
 
0.9%
16976 1
 
0.3%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct45
Distinct (%)13.8%
Missing26
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean37.314707
Minimum36.975251
Maximum38.015649
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-05-03T19:29:01.917935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.975251
5-th percentile37.290346
Q137.290722
median37.290722
Q337.290722
95-th percentile37.436502
Maximum38.015649
Range1.0403973
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.10968663
Coefficient of variation (CV)0.0029395012
Kurtosis23.241104
Mean37.314707
Median Absolute Deviation (MAD)0
Skewness4.2510731
Sum12164.594
Variance0.012031156
MonotonicityNot monotonic
2024-05-03T19:29:02.210865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
37.2907221232 236
67.0%
37.2972051797 10
 
2.8%
37.4365017609 8
 
2.3%
37.2940619896 8
 
2.3%
37.2905253334 5
 
1.4%
37.6420211573 4
 
1.1%
37.2129073682 4
 
1.1%
38.0120752747 3
 
0.9%
37.2591311904 3
 
0.9%
37.2903463725 3
 
0.9%
Other values (35) 42
 
11.9%
(Missing) 26
 
7.4%
ValueCountFrequency (%)
36.9752514145 1
 
0.3%
36.9920854229 1
 
0.3%
37.0981452395 1
 
0.3%
37.2129073682 4
1.1%
37.2186909629 1
 
0.3%
37.2565731109 2
0.6%
37.2591311904 3
0.9%
37.2804342304 1
 
0.3%
37.2813345358 1
 
0.3%
37.2869899926 1
 
0.3%
ValueCountFrequency (%)
38.015648742 1
 
0.3%
38.0120752747 3
 
0.9%
37.8832804639 1
 
0.3%
37.7727067707 1
 
0.3%
37.7484978512 1
 
0.3%
37.6420211573 4
1.1%
37.5086718515 1
 
0.3%
37.4430912297 1
 
0.3%
37.4365017609 8
2.3%
37.4364431708 2
 
0.6%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct45
Distinct (%)13.8%
Missing26
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean127.17087
Minimum126.67856
Maximum127.66638
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-05-03T19:29:02.488930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.67856
5-th percentile127.01712
Q1127.19673
median127.19673
Q3127.19673
95-th percentile127.20213
Maximum127.66638
Range0.98782269
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.097124957
Coefficient of variation (CV)0.00076373587
Kurtosis13.402095
Mean127.17087
Median Absolute Deviation (MAD)0
Skewness-2.7100178
Sum41457.704
Variance0.0094332572
MonotonicityNot monotonic
2024-05-03T19:29:02.825235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
127.1967269237 236
67.0%
127.2009702513 10
 
2.8%
127.0240724311 8
 
2.3%
127.2021291328 8
 
2.3%
127.2015731702 5
 
1.4%
126.6785604873 4
 
1.1%
127.2948210725 4
 
1.1%
127.0554841722 3
 
0.9%
127.1225361599 3
 
0.9%
127.2006622315 3
 
0.9%
Other values (35) 42
 
11.9%
(Missing) 26
 
7.4%
ValueCountFrequency (%)
126.6785604873 4
1.1%
126.721467027 2
0.6%
126.7437361036 1
 
0.3%
126.8300749703 1
 
0.3%
126.8330759762 1
 
0.3%
126.8532927379 1
 
0.3%
126.902325639 1
 
0.3%
126.9929312719 1
 
0.3%
127.0083731253 1
 
0.3%
127.0087990979 1
 
0.3%
ValueCountFrequency (%)
127.6663831773 1
 
0.3%
127.3610725876 1
 
0.3%
127.3020891949 1
 
0.3%
127.2948210725 4
1.1%
127.2343958244 1
 
0.3%
127.2088469222 1
 
0.3%
127.2051784272 2
 
0.6%
127.2021291328 8
2.3%
127.2015731702 5
1.4%
127.2012688892 1
 
0.3%

Interactions

2024-05-03T19:28:50.640553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:28:48.968666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:28:49.804673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:28:50.899643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:28:49.257696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:28:50.118309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:28:51.217633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:28:49.527658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:28:50.380377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T19:29:03.018830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명영업상태명폐업일자소재지도로명주소소재지우편번호WGS84위도WGS84경도
시군명1.0000.4931.0001.0000.9910.9740.960
영업상태명0.4931.0001.0000.6490.2980.4690.196
폐업일자1.0001.0001.0000.9951.0001.0001.000
소재지도로명주소1.0000.6490.9951.0001.0001.0001.000
소재지우편번호0.9910.2981.0001.0001.0000.9270.964
WGS84위도0.9740.4691.0001.0000.9271.0000.871
WGS84경도0.9600.1961.0001.0000.9640.8711.000
2024-05-03T19:29:03.255933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위생업종명영업상태명시군명
위생업종명1.0001.0001.000
영업상태명1.0001.0000.296
시군명1.0000.2961.000
2024-05-03T19:29:03.564970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지우편번호WGS84위도WGS84경도시군명영업상태명위생업종명
소재지우편번호1.000-0.7910.5050.9780.2031.000
WGS84위도-0.7911.000-0.2280.8900.2961.000
WGS84경도0.505-0.2281.0000.8600.0881.000
시군명0.9780.8900.8601.0000.2961.000
영업상태명0.2030.2960.0880.2961.0001.000
위생업종명1.0001.0001.0001.0001.0001.000

Missing values

2024-05-03T19:28:51.590054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T19:28:52.195413image/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.
2024-05-03T19:28:52.611169image/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

시군명사업장명인허가일자영업상태명폐업일자다중이용업소여부총시설규모(㎡)위생업종명위생업태명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0가평군달달하:주(ZOO)2021-01-05영업<NA><NA><NA><NA>유원지경기도 가평군 상면 임초밤안골로 301, 1동 2층경기도 가평군 상면 임초리 622-13 외 5필지(622-2, 622-3, 622-12, 622-14, 622-15)1244737.748498127.361073
1과천시카페베네20120723영업<NA><NA><NA><NA>유원지경기도 과천시 광명로 140 (막계동, (서울랜드 정문앞))경기도 과천시 막계동 4331382937.43479127.019901
2과천시기린나라커피숍20141023운영중<NA><NA><NA>휴게음식점유원지경기도 과천시 광명로 140, 서울대공원내 2층 (막계동)경기도 과천시 막계동 산 118-3번지1382937.43479127.019901
3과천시후토스카페20120706운영중<NA><NA><NA>휴게음식점유원지경기도 과천시 광명로 181 (막계동, 서울랜드 후문)경기도 과천시 막계동 33번지 서울랜드 후문1382937.436502127.024072
4과천시라이언카페20141226운영중<NA><NA><NA>휴게음식점유원지경기도 과천시 광명로 181 (막계동, 서울대공원(사자우리))경기도 과천시 막계동 662번지 서울대공원(사자우리)1382937.436502127.024072
5과천시카페베네20120723운영중<NA><NA><NA>휴게음식점유원지경기도 과천시 광명로 140 (막계동, (서울랜드 정문앞))경기도 과천시 막계동 433번지1382937.43479127.019901
6과천시대한민국상이군경회 대공원 던킨도너츠20060801운영중<NA><NA><NA>휴게음식점유원지경기도 과천시 광명로 80 (막계동, 서울대공원 대형주차장일부)경기도 과천시 막계동 273-1번지 서울대공원 대형주차장일부1382937.435348127.008373
7과천시서울랜드롯데리아19880509운영중<NA><NA><NA>휴게음식점유원지경기도 과천시 광명로 181 (막계동, 서울랜드내)경기도 과천시 막계동 33번지 서울랜드내1382937.436502127.024072
8과천시기린카페띠아모20120217운영중<NA><NA><NA>휴게음식점유원지경기도 과천시 광명로 80 (막계동, 서울대공원내)경기도 과천시 막계동 692번지 서울대공원내1382937.436443127.014103
9과천시서울랜드롯데리아1988-05-09폐업2023-03-28<NA><NA><NA>유원지경기도 과천시 광명로 181 (막계동, 서울랜드내)경기도 과천시 막계동 33 서울랜드내1382937.436502127.024072
시군명사업장명인허가일자영업상태명폐업일자다중이용업소여부총시설규모(㎡)위생업종명위생업태명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
342용인시지구마을휴게8점20110518폐업 등20170407<NA><NA>휴게음식점유원지경기도 용인시 처인구 포곡읍 에버랜드로 199경기도 용인시 처인구 포곡읍 가실리 104번지1702337.290722127.196727
343용인시아이거스낵20121224폐업 등20141219<NA><NA>휴게음식점유원지경기도 용인시 처인구 포곡읍 에버랜드로 199, P-14동 (외80필지)경기도 용인시 처인구 포곡읍 전대리 506번지 외80필지 P-14동1702337.290722127.196727
344용인시디스코라운드휴게4점20130219폐업 등20160805<NA><NA>휴게음식점유원지경기도 용인시 처인구 포곡읍 에버랜드로 199, A동 (외1필지)경기도 용인시 처인구 포곡읍 전대리 519-22번지 외1필지 A동1702337.290722127.196727
345용인시희원주차장20130416폐업 등20130430<NA><NA>휴게음식점유원지<NA>경기도 용인시 처인구 포곡읍 가실리 241-1번지 외1필지(나동,다동)1702137.293268127.18955
346파주시능마을휴게정19970830폐업 등19990330<NA><NA>휴게음식점유원지경기도 파주시 광탄면 기산로 28경기도 파주시 광탄면 영장리 301-1번지1095337.772707126.902326
347파주시임진강폭포어장(주)19961227폐업 등20120611<NA><NA>휴게음식점유원지<NA>경기도 파주시 파평면 덕천리 289-1번지10801<NA><NA>
348파주시임진강폭포어장(주)19940624폐업 등20120611<NA><NA>휴게음식점유원지<NA>경기도 파주시 파평면 덕천리 254-12번지10801<NA><NA>
349평택시내리매점20210607영업<NA><NA><NA><NA>유원지경기도 평택시 팽성읍 내리길 64-23, 내리캠핑장 1동 1층경기도 평택시 팽성읍 내리 20-31797336.975251127.032555
350평택시진위천시민유원지20100723폐업 등20100825<NA><NA>휴게음식점유원지경기도 평택시 진위면 진위서로 264-15경기도 평택시 진위면 봉남리 432번지 진위천시민유원지내1771437.098145127.086481
351포천시운악산19970327폐업 등20000418<NA><NA>휴게음식점유원지<NA>경기도 포천시 화현면 화현리 1148-6번지1112337.88328127.302089