Overview

Dataset statistics

Number of variables15
Number of observations1138
Missing cells1152
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory136.8 KiB
Average record size in memory123.1 B

Variable types

Categorical5
Text5
Boolean1
Numeric3
DateTime1

Dataset

Description김해시 유흥주점 및 단란주점 업체 현황(업종구분, 사업장명, 지번주소, 도로명주소, 소재지전화, 위생업태명, 영업장주변구분명 등)에 대한 데이터 제공.
Author경상남도 김해시
URLhttps://www.data.go.kr/data/15033421/fileData.do

Alerts

구분 is highly overall correlated with 위생업태명High correlation
위생업태명 is highly overall correlated with 구분High correlation
다중이용업소여부 is highly overall correlated with 영업상태High correlation
영업상태 is highly overall correlated with 다중이용업소여부High correlation
급수시설구분명 is highly imbalanced (52.3%)Imbalance
소재지전화 has 288 (25.3%) missing valuesMissing
소재지면적 has 116 (10.2%) missing valuesMissing
도로명주소 has 34 (3.0%) missing valuesMissing
폐업일자 has 714 (62.7%) missing valuesMissing
시설총규모 has 127 (11.2%) zerosZeros

Reproduction

Analysis started2023-12-12 15:26:42.987387
Analysis finished2023-12-12 15:26:46.158063
Duration3.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
유흥주점영업
868 
단란주점영업
270 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
유흥주점영업 868
76.3%
단란주점영업 270
 
23.7%

Length

2023-12-13T00:26:46.237823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:26:46.339704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유흥주점영업 868
76.3%
단란주점영업 270
 
23.7%

소재지전화
Text

MISSING 

Distinct767
Distinct (%)90.2%
Missing288
Missing (%)25.3%
Memory size9.0 KiB
2023-12-13T00:26:46.585727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters10200
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

Unique691 ?
Unique (%)81.3%

Sample

1st row055-325-6686
2nd row055-326-2237
3rd row055-324-9911
4th row055-343-0028
5th row055-345-6678
ValueCountFrequency (%)
055-313-1006 4
 
0.5%
055-323-8456 3
 
0.4%
055-328-1616 3
 
0.4%
055-325-9091 3
 
0.4%
055-325-0090 3
 
0.4%
055-321-9888 3
 
0.4%
055-326-5700 2
 
0.2%
055-321-5678 2
 
0.2%
055-322-7188 2
 
0.2%
055-335-2808 2
 
0.2%
Other values (757) 823
96.8%
2023-12-13T00:26:47.049544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 2102
20.6%
- 1700
16.7%
3 1613
15.8%
0 1292
12.7%
2 763
 
7.5%
1 586
 
5.7%
4 502
 
4.9%
7 435
 
4.3%
6 415
 
4.1%
8 398
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8500
83.3%
Dash Punctuation 1700
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2102
24.7%
3 1613
19.0%
0 1292
15.2%
2 763
 
9.0%
1 586
 
6.9%
4 502
 
5.9%
7 435
 
5.1%
6 415
 
4.9%
8 398
 
4.7%
9 394
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 1700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 2102
20.6%
- 1700
16.7%
3 1613
15.8%
0 1292
12.7%
2 763
 
7.5%
1 586
 
5.7%
4 502
 
4.9%
7 435
 
4.3%
6 415
 
4.1%
8 398
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 2102
20.6%
- 1700
16.7%
3 1613
15.8%
0 1292
12.7%
2 763
 
7.5%
1 586
 
5.7%
4 502
 
4.9%
7 435
 
4.3%
6 415
 
4.1%
8 398
 
3.9%

소재지면적
Text

MISSING 

Distinct886
Distinct (%)86.7%
Missing116
Missing (%)10.2%
Memory size9.0 KiB
2023-12-13T00:26:47.501102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length5.3043053
Min length1

Characters and Unicode

Total characters5421
Distinct characters12
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

Unique786 ?
Unique (%)76.9%

Sample

1st row124.56
2nd row197.96
3rd row127
4th row193.7
5th row123.52
ValueCountFrequency (%)
0 9
 
0.9%
125.24 5
 
0.5%
120.69 5
 
0.5%
105 4
 
0.4%
112.78 4
 
0.4%
114.81 4
 
0.4%
131.92 4
 
0.4%
69.69 4
 
0.4%
97.44 3
 
0.3%
95.45 3
 
0.3%
Other values (876) 977
95.6%
2023-12-13T00:26:48.135649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 994
18.3%
. 957
17.7%
2 564
10.4%
8 408
7.5%
9 393
 
7.2%
6 378
 
7.0%
4 373
 
6.9%
7 354
 
6.5%
3 353
 
6.5%
5 336
 
6.2%
Other values (2) 311
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4461
82.3%
Other Punctuation 960
 
17.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 994
22.3%
2 564
12.6%
8 408
9.1%
9 393
 
8.8%
6 378
 
8.5%
4 373
 
8.4%
7 354
 
7.9%
3 353
 
7.9%
5 336
 
7.5%
0 308
 
6.9%
Other Punctuation
ValueCountFrequency (%)
. 957
99.7%
, 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 5421
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 994
18.3%
. 957
17.7%
2 564
10.4%
8 408
7.5%
9 393
 
7.2%
6 378
 
7.0%
4 373
 
6.9%
7 354
 
6.5%
3 353
 
6.5%
5 336
 
6.2%
Other values (2) 311
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5421
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 994
18.3%
. 957
17.7%
2 564
10.4%
8 408
7.5%
9 393
 
7.2%
6 378
 
7.0%
4 373
 
6.9%
7 354
 
6.5%
3 353
 
6.5%
5 336
 
6.2%
Other values (2) 311
 
5.7%

지번주소
Text

Distinct930
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2023-12-13T00:26:48.496569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length38
Mean length24.540422
Min length16

Characters and Unicode

Total characters27927
Distinct characters191
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

Unique785 ?
Unique (%)69.0%

Sample

1st row경상남도 김해시 부원동 608-18번지
2nd row경상남도 김해시 서상동 258-1번지
3rd row경상남도 김해시 외동 1255-6번지 502호
4th row경상남도 김해시 진영읍 진영리 1614-6번지
5th row경상남도 김해시 진영읍 진영리 1614-9번지 진영그랜드프라자 503호
ValueCountFrequency (%)
경상남도 1138
20.6%
김해시 1138
20.6%
부원동 191
 
3.5%
대청동 169
 
3.1%
어방동 148
 
2.7%
삼계동 117
 
2.1%
외동 107
 
1.9%
진영읍 100
 
1.8%
내동 82
 
1.5%
삼방동 67
 
1.2%
Other values (859) 2261
41.0%
2023-12-13T00:26:48.960976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4381
 
15.7%
1 1460
 
5.2%
상 1221
 
4.4%
남 1154
 
4.1%
시 1153
 
4.1%
해 1140
 
4.1%
경 1139
 
4.1%
도 1138
 
4.1%
김 1138
 
4.1%
- 1122
 
4.0%
Other values (181) 12881
46.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15900
56.9%
Decimal Number 6378
22.8%
Space Separator 4381
 
15.7%
Dash Punctuation 1122
 
4.0%
Other Punctuation 48
 
0.2%
Uppercase Letter 39
 
0.1%
Open Punctuation 29
 
0.1%
Close Punctuation 29
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 1221
 
7.7%
남 1154
 
7.3%
시 1153
 
7.3%
해 1140
 
7.2%
경 1139
 
7.2%
도 1138
 
7.2%
김 1138
 
7.2%
동 1050
 
6.6%
지 1038
 
6.5%
번 1000
 
6.3%
Other values (158) 4729
29.7%
Decimal Number
ValueCountFrequency (%)
1 1460
22.9%
2 798
12.5%
0 748
11.7%
5 604
9.5%
3 560
 
8.8%
4 548
 
8.6%
6 547
 
8.6%
8 419
 
6.6%
7 398
 
6.2%
9 296
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
B 16
41.0%
N 10
25.6%
C 10
25.6%
L 2
 
5.1%
A 1
 
2.6%
Other Punctuation
ValueCountFrequency (%)
, 38
79.2%
/ 6
 
12.5%
. 4
 
8.3%
Space Separator
ValueCountFrequency (%)
4381
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1122
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15900
56.9%
Common 11988
42.9%
Latin 39
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 1221
 
7.7%
남 1154
 
7.3%
시 1153
 
7.3%
해 1140
 
7.2%
경 1139
 
7.2%
도 1138
 
7.2%
김 1138
 
7.2%
동 1050
 
6.6%
지 1038
 
6.5%
번 1000
 
6.3%
Other values (158) 4729
29.7%
Common
ValueCountFrequency (%)
4381
36.5%
1 1460
 
12.2%
- 1122
 
9.4%
2 798
 
6.7%
0 748
 
6.2%
5 604
 
5.0%
3 560
 
4.7%
4 548
 
4.6%
6 547
 
4.6%
8 419
 
3.5%
Other values (8) 801
 
6.7%
Latin
ValueCountFrequency (%)
B 16
41.0%
N 10
25.6%
C 10
25.6%
L 2
 
5.1%
A 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15899
56.9%
ASCII 12027
43.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4381
36.4%
1 1460
 
12.1%
- 1122
 
9.3%
2 798
 
6.6%
0 748
 
6.2%
5 604
 
5.0%
3 560
 
4.7%
4 548
 
4.6%
6 547
 
4.5%
8 419
 
3.5%
Other values (13) 840
 
7.0%
Hangul
ValueCountFrequency (%)
상 1221
 
7.7%
남 1154
 
7.3%
시 1153
 
7.3%
해 1140
 
7.2%
경 1139
 
7.2%
도 1138
 
7.2%
김 1138
 
7.2%
동 1050
 
6.6%
지 1038
 
6.5%
번 1000
 
6.3%
Other values (157) 4728
29.7%
Compat Jamo
ValueCountFrequency (%)
ㅁ 1
100.0%

도로명주소
Text

MISSING 

Distinct942
Distinct (%)85.3%
Missing34
Missing (%)3.0%
Memory size9.0 KiB
2023-12-13T00:26:49.306301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length39
Mean length29.612319
Min length15

Characters and Unicode

Total characters32692
Distinct characters194
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

Unique822 ?
Unique (%)74.5%

Sample

1st row경상남도 김해시 김해대로2355번길 19 (부원동)
2nd row경상남도 김해시 가락로49번길 14 (서상동)
3rd row경상남도 김해시 내외중앙로 39 (외동, 502호)
4th row경상남도 김해시 진영읍 김해대로365번길 6-4
5th row경상남도 김해시 진영읍 김해대로361번길 2, 503호 (진영그랜드프라자)
ValueCountFrequency (%)
경상남도 1104
 
17.2%
김해시 1104
 
17.2%
부원동 182
 
2.8%
어방동 139
 
2.2%
삼계동 116
 
1.8%
내외중앙로 111
 
1.7%
외동 107
 
1.7%
진영읍 96
 
1.5%
대청동 81
 
1.3%
내동 81
 
1.3%
Other values (626) 3301
51.4%
2023-12-13T00:26:49.792676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5318
 
16.3%
해 1246
 
3.8%
김 1196
 
3.7%
상 1173
 
3.6%
1 1171
 
3.6%
시 1122
 
3.4%
남 1118
 
3.4%
로 1113
 
3.4%
경 1107
 
3.4%
도 1104
 
3.4%
Other values (184) 17024
52.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18423
56.4%
Decimal Number 5949
 
18.2%
Space Separator 5318
 
16.3%
Close Punctuation 1014
 
3.1%
Open Punctuation 1014
 
3.1%
Other Punctuation 713
 
2.2%
Dash Punctuation 220
 
0.7%
Uppercase Letter 41
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
해 1246
 
6.8%
김 1196
 
6.5%
상 1173
 
6.4%
시 1122
 
6.1%
남 1118
 
6.1%
로 1113
 
6.0%
경 1107
 
6.0%
도 1104
 
6.0%
동 898
 
4.9%
번 833
 
4.5%
Other values (164) 7513
40.8%
Decimal Number
ValueCountFrequency (%)
1 1171
19.7%
2 820
13.8%
0 755
12.7%
3 748
12.6%
5 622
10.5%
4 490
8.2%
6 470
7.9%
7 424
 
7.1%
9 226
 
3.8%
8 223
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
B 16
39.0%
C 12
29.3%
N 12
29.3%
A 1
 
2.4%
Other Punctuation
ValueCountFrequency (%)
, 706
99.0%
/ 7
 
1.0%
Space Separator
ValueCountFrequency (%)
5318
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1014
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1014
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 220
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18423
56.4%
Common 14228
43.5%
Latin 41
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
해 1246
 
6.8%
김 1196
 
6.5%
상 1173
 
6.4%
시 1122
 
6.1%
남 1118
 
6.1%
로 1113
 
6.0%
경 1107
 
6.0%
도 1104
 
6.0%
동 898
 
4.9%
번 833
 
4.5%
Other values (164) 7513
40.8%
Common
ValueCountFrequency (%)
5318
37.4%
1 1171
 
8.2%
) 1014
 
7.1%
( 1014
 
7.1%
2 820
 
5.8%
0 755
 
5.3%
3 748
 
5.3%
, 706
 
5.0%
5 622
 
4.4%
4 490
 
3.4%
Other values (6) 1570
 
11.0%
Latin
ValueCountFrequency (%)
B 16
39.0%
C 12
29.3%
N 12
29.3%
A 1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18423
56.4%
ASCII 14269
43.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5318
37.3%
1 1171
 
8.2%
) 1014
 
7.1%
( 1014
 
7.1%
2 820
 
5.7%
0 755
 
5.3%
3 748
 
5.2%
, 706
 
4.9%
5 622
 
4.4%
4 490
 
3.4%
Other values (10) 1611
 
11.3%
Hangul
ValueCountFrequency (%)
해 1246
 
6.8%
김 1196
 
6.5%
상 1173
 
6.4%
시 1122
 
6.1%
남 1118
 
6.1%
로 1113
 
6.0%
경 1107
 
6.0%
도 1104
 
6.0%
동 898
 
4.9%
번 833
 
4.5%
Other values (164) 7513
40.8%

사업장명
Text

Distinct1036
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2023-12-13T00:26:50.047773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length5.8497364
Min length1

Characters and Unicode

Total characters6657
Distinct characters521
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

Unique946 ?
Unique (%)83.1%

Sample

1st row루루노래주점
2nd row양지카페주점
3rd row신세계노래주점
4th row노찾사패밀리노래타운
5th row국빈노래주점
ValueCountFrequency (%)
노래주점 11
 
0.9%
친구노래주점 4
 
0.3%
수노래주점 3
 
0.3%
코끼리 3
 
0.3%
호박꽃 3
 
0.3%
팡팡노래주점 3
 
0.3%
노래를찾는사람들 3
 
0.3%
가고파 3
 
0.3%
노래타운 3
 
0.3%
월드노래주점 3
 
0.3%
Other values (1049) 1141
96.7%
2023-12-13T00:26:50.411537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 629
 
9.4%
점 627
 
9.4%
노 483
 
7.3%
래 479
 
7.2%
란 194
 
2.9%
단 193
 
2.9%
이 116
 
1.7%
스 107
 
1.6%
라 76
 
1.1%
타 70
 
1.1%
Other values (511) 3683
55.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6304
94.7%
Decimal Number 122
 
1.8%
Uppercase Letter 106
 
1.6%
Space Separator 42
 
0.6%
Lowercase Letter 26
 
0.4%
Open Punctuation 25
 
0.4%
Close Punctuation 25
 
0.4%
Other Punctuation 7
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 629
 
10.0%
점 627
 
9.9%
노 483
 
7.7%
래 479
 
7.6%
란 194
 
3.1%
단 193
 
3.1%
이 116
 
1.8%
스 107
 
1.7%
라 76
 
1.2%
타 70
 
1.1%
Other values (457) 3330
52.8%
Uppercase Letter
ValueCountFrequency (%)
B 12
 
11.3%
M 10
 
9.4%
I 10
 
9.4%
A 9
 
8.5%
E 7
 
6.6%
N 6
 
5.7%
P 5
 
4.7%
V 5
 
4.7%
C 5
 
4.7%
S 4
 
3.8%
Other values (13) 33
31.1%
Lowercase Letter
ValueCountFrequency (%)
s 4
15.4%
a 4
15.4%
e 3
11.5%
l 3
11.5%
y 3
11.5%
i 2
7.7%
w 1
 
3.8%
t 1
 
3.8%
r 1
 
3.8%
v 1
 
3.8%
Other values (3) 3
11.5%
Decimal Number
ValueCountFrequency (%)
0 37
30.3%
8 19
15.6%
7 19
15.6%
2 17
13.9%
1 15
12.3%
3 5
 
4.1%
5 4
 
3.3%
9 3
 
2.5%
6 2
 
1.6%
4 1
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 3
42.9%
% 1
 
14.3%
& 1
 
14.3%
? 1
 
14.3%
/ 1
 
14.3%
Space Separator
ValueCountFrequency (%)
42
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6304
94.7%
Common 221
 
3.3%
Latin 132
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 629
 
10.0%
점 627
 
9.9%
노 483
 
7.7%
래 479
 
7.6%
란 194
 
3.1%
단 193
 
3.1%
이 116
 
1.8%
스 107
 
1.7%
라 76
 
1.2%
타 70
 
1.1%
Other values (457) 3330
52.8%
Latin
ValueCountFrequency (%)
B 12
 
9.1%
M 10
 
7.6%
I 10
 
7.6%
A 9
 
6.8%
E 7
 
5.3%
N 6
 
4.5%
P 5
 
3.8%
V 5
 
3.8%
C 5
 
3.8%
s 4
 
3.0%
Other values (26) 59
44.7%
Common
ValueCountFrequency (%)
42
19.0%
0 37
16.7%
( 25
11.3%
) 25
11.3%
8 19
8.6%
7 19
8.6%
2 17
7.7%
1 15
 
6.8%
3 5
 
2.3%
5 4
 
1.8%
Other values (8) 13
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6304
94.7%
ASCII 353
 
5.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 629
 
10.0%
점 627
 
9.9%
노 483
 
7.7%
래 479
 
7.6%
란 194
 
3.1%
단 193
 
3.1%
이 116
 
1.8%
스 107
 
1.7%
라 76
 
1.2%
타 70
 
1.1%
Other values (457) 3330
52.8%
ASCII
ValueCountFrequency (%)
42
 
11.9%
0 37
 
10.5%
( 25
 
7.1%
) 25
 
7.1%
8 19
 
5.4%
7 19
 
5.4%
2 17
 
4.8%
1 15
 
4.2%
B 12
 
3.4%
M 10
 
2.8%
Other values (44) 132
37.4%

위생업태명
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
비어(바)살롱
436 
단란주점
270 
룸살롱
262 
기타
63 
간이주점
 
41
Other values (5)
66 

Length

Max length12
Median length9
Mean length4.8172232
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row비어(바)살롱
2nd row스텐드바
3rd row비어(바)살롱
4th row비어(바)살롱
5th row비어(바)살롱

Common Values

ValueCountFrequency (%)
비어(바)살롱 436
38.3%
단란주점 270
23.7%
룸살롱 262
23.0%
기타 63
 
5.5%
간이주점 41
 
3.6%
노래클럽 23
 
2.0%
스텐드바 20
 
1.8%
카바레 18
 
1.6%
고고(디스코)클럽 4
 
0.4%
관광호텔나이트(디스코) 1
 
0.1%

Length

2023-12-13T00:26:50.556199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:26:50.704355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비어(바)살롱 436
38.3%
단란주점 270
23.7%
룸살롱 262
23.0%
기타 63
 
5.5%
간이주점 41
 
3.6%
노래클럽 23
 
2.0%
스텐드바 20
 
1.8%
카바레 18
 
1.6%
고고(디스코)클럽 4
 
0.4%
관광호텔나이트(디스코 1
 
0.1%

영업장주변구분명
Categorical

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
유흥업소밀집지역
559 
<NA>
390 
기타
121 
학교정화(상대)
 
44
주택가주변
 
20
Other values (2)
 
4

Length

Max length8
Median length8
Mean length5.9358524
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row유흥업소밀집지역
2nd row기타
3rd row유흥업소밀집지역
4th row유흥업소밀집지역
5th row유흥업소밀집지역

Common Values

ValueCountFrequency (%)
유흥업소밀집지역 559
49.1%
<NA> 390
34.3%
기타 121
 
10.6%
학교정화(상대) 44
 
3.9%
주택가주변 20
 
1.8%
학교정화(절대) 3
 
0.3%
아파트지역 1
 
0.1%

Length

2023-12-13T00:26:50.852723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:26:51.002888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유흥업소밀집지역 559
49.1%
na 390
34.3%
기타 121
 
10.6%
학교정화(상대 44
 
3.9%
주택가주변 20
 
1.8%
학교정화(절대 3
 
0.3%
아파트지역 1
 
0.1%

급수시설구분명
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
상수도전용
646 
<NA>
444 
지하수전용
 
36
간이상수도
 
7
상수도(음용)지하수(주방용)겸용
 
4

Length

Max length19
Median length5
Mean length4.6643234
Min length4

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 646
56.8%
<NA> 444
39.0%
지하수전용 36
 
3.2%
간이상수도 7
 
0.6%
상수도(음용)지하수(주방용)겸용 4
 
0.4%
전용상수도(특정시설의 자가용 수도) 1
 
0.1%

Length

2023-12-13T00:26:51.159815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:26:51.286429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 646
56.7%
na 444
38.9%
지하수전용 36
 
3.2%
간이상수도 7
 
0.6%
상수도(음용)지하수(주방용)겸용 4
 
0.4%
전용상수도(특정시설의 1
 
0.1%
자가용 1
 
0.1%
수도 1
 
0.1%

다중이용업소여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
True
756 
False
382 
ValueCountFrequency (%)
True 756
66.4%
False 382
33.6%
2023-12-13T00:26:51.400762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시설총규모
Real number (ℝ)

ZEROS 

Distinct884
Distinct (%)77.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.78736
Minimum0
Maximum2963.91
Zeros127
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-13T00:26:51.521138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q177.785
median108.95
Q3129.5475
95-th percentile229.6905
Maximum2963.91
Range2963.91
Interquartile range (IQR)51.7625

Descriptive statistics

Standard deviation124.61244
Coefficient of variation (CV)1.0951344
Kurtosis253.83851
Mean113.78736
Median Absolute Deviation (MAD)22.815
Skewness12.551886
Sum129490.01
Variance15528.261
MonotonicityNot monotonic
2023-12-13T00:26:51.680275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 127
 
11.2%
120.69 5
 
0.4%
125.24 5
 
0.4%
105.0 4
 
0.4%
131.92 4
 
0.4%
112.78 4
 
0.4%
69.69 4
 
0.4%
114.81 4
 
0.4%
124.56 3
 
0.3%
115.92 3
 
0.3%
Other values (874) 975
85.7%
ValueCountFrequency (%)
0.0 127
11.2%
12.16 1
 
0.1%
13.2 1
 
0.1%
13.53 1
 
0.1%
14.52 2
 
0.2%
17.16 1
 
0.1%
17.34 1
 
0.1%
19.5 1
 
0.1%
19.8 1
 
0.1%
21.84 1
 
0.1%
ValueCountFrequency (%)
2963.91 1
0.1%
1214.28 1
0.1%
1209.34 1
0.1%
900.55 1
0.1%
849.0 1
0.1%
740.86 1
0.1%
632.0 1
0.1%
560.37 1
0.1%
527.88 1
0.1%
522.5 1
0.1%

영업상태
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
영업
714 
폐업
424 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영업 714
62.7%
폐업 424
37.3%

Length

2023-12-13T00:26:51.816008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:26:51.934231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업 714
62.7%
폐업 424
37.3%

폐업일자
Date

MISSING 

Distinct348
Distinct (%)82.1%
Missing714
Missing (%)62.7%
Memory size9.0 KiB
Minimum1999-04-07 00:00:00
Maximum2021-04-01 00:00:00
2023-12-13T00:26:52.069583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:26:52.229681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

위도
Real number (ℝ)

Distinct483
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.237308
Minimum35.172416
Maximum35.323335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-13T00:26:52.396607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.172416
5-th percentile35.19295
Q135.228592
median35.234656
Q335.24542
95-th percentile35.307018
Maximum35.323335
Range0.15091887
Interquartile range (IQR)0.016828035

Descriptive statistics

Standard deviation0.030373052
Coefficient of variation (CV)0.00086195722
Kurtosis0.58548911
Mean35.237308
Median Absolute Deviation (MAD)0.009750815
Skewness0.65980693
Sum40100.056
Variance0.00092252229
MonotonicityNot monotonic
2023-12-13T00:26:52.549944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.30701759 25
 
2.2%
35.30792782 19
 
1.7%
35.19415905 17
 
1.5%
35.19437522 17
 
1.5%
35.19252059 15
 
1.3%
35.19471086 14
 
1.2%
35.19295015 12
 
1.1%
35.23400647 12
 
1.1%
35.19355858 12
 
1.1%
35.20000684 11
 
1.0%
Other values (473) 984
86.5%
ValueCountFrequency (%)
35.17241625 1
 
0.1%
35.17255447 1
 
0.1%
35.17725352 1
 
0.1%
35.1780502 1
 
0.1%
35.19114996 1
 
0.1%
35.19198114 11
1.0%
35.19221035 3
 
0.3%
35.19241274 3
 
0.3%
35.19252059 15
1.3%
35.19263098 4
 
0.4%
ValueCountFrequency (%)
35.32333512 1
0.1%
35.32173425 1
0.1%
35.32079934 2
0.2%
35.32014199 1
0.1%
35.3199362 1
0.1%
35.31949613 1
0.1%
35.318151 1
0.1%
35.31625242 1
0.1%
35.31440536 1
0.1%
35.31325504 1
0.1%

경도
Real number (ℝ)

Distinct483
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.85147
Minimum128.72072
Maximum128.9696
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-13T00:26:52.710878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.72072
5-th percentile128.73219
Q1128.8032
median128.87049
Q3128.88335
95-th percentile128.90668
Maximum128.9696
Range0.2488829
Interquartile range (IQR)0.0801463

Descriptive statistics

Standard deviation0.052563013
Coefficient of variation (CV)0.00040793492
Kurtosis-0.0041684811
Mean128.85147
Median Absolute Deviation (MAD)0.0317805
Skewness-1.0100457
Sum146632.97
Variance0.0027628703
MonotonicityNot monotonic
2023-12-13T00:26:52.844282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.7298577 25
 
2.2%
128.7306289 19
 
1.7%
128.801517 17
 
1.5%
128.8014123 17
 
1.5%
128.8019115 15
 
1.3%
128.8012485 14
 
1.2%
128.8033153 12
 
1.1%
128.8657973 12
 
1.1%
128.7996645 12
 
1.1%
128.8150519 11
 
1.0%
Other values (473) 984
86.5%
ValueCountFrequency (%)
128.7207191 1
 
0.1%
128.7245205 1
 
0.1%
128.7274983 1
 
0.1%
128.7278453 1
 
0.1%
128.7283121 1
 
0.1%
128.7298456 1
 
0.1%
128.7298577 25
2.2%
128.7303063 1
 
0.1%
128.7303157 5
 
0.4%
128.7306289 19
1.7%
ValueCountFrequency (%)
128.969602 1
 
0.1%
128.9690391 1
 
0.1%
128.944948 1
 
0.1%
128.9093994 4
0.4%
128.9093078 2
0.2%
128.9093051 2
0.2%
128.9092848 1
 
0.1%
128.9090242 2
0.2%
128.9089883 1
 
0.1%
128.9089589 2
0.2%

Interactions

2023-12-13T00:26:44.893219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:26:44.109102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:26:44.508083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:26:45.019387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:26:44.230078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:26:44.629366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:26:45.142228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:26:44.359915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:26:44.775023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:26:52.930015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분위생업태명영업장주변구분명급수시설구분명다중이용업소여부시설총규모영업상태위도경도
구분1.0001.0000.5140.0000.5490.0450.5270.3560.241
위생업태명1.0001.0000.4260.3310.5290.4110.5090.4370.294
영업장주변구분명0.5140.4261.0000.0000.1950.0550.2020.3320.319
급수시설구분명0.0000.3310.0001.0000.0620.0000.0640.5010.292
다중이용업소여부0.5490.5290.1950.0621.0000.0000.9920.3110.173
시설총규모0.0450.4110.0550.0000.0001.0000.0000.0000.000
영업상태0.5270.5090.2020.0640.9920.0001.0000.2970.156
위도0.3560.4370.3320.5010.3110.0000.2971.0000.892
경도0.2410.2940.3190.2920.1730.0000.1560.8921.000
2023-12-13T00:26:53.045570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
급수시설구분명구분위생업태명영업장주변구분명다중이용업소여부영업상태
급수시설구분명1.0000.0000.1430.0000.0750.078
구분0.0001.0000.9960.3710.3700.353
위생업태명0.1430.9961.0000.2400.4060.390
영업장주변구분명0.0000.3710.2401.0000.1400.145
다중이용업소여부0.0750.3700.4060.1401.0000.920
영업상태0.0780.3530.3900.1450.9201.000
2023-12-13T00:26:53.141427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설총규모위도경도구분위생업태명영업장주변구분명급수시설구분명다중이용업소여부영업상태
시설총규모1.000-0.108-0.1010.0320.2300.0350.0000.0000.000
위도-0.1081.0000.1230.2720.1470.1810.2300.2380.227
경도-0.1010.1231.0000.2390.1380.1640.1830.1700.152
구분0.0320.2720.2391.0000.9960.3710.0000.3700.353
위생업태명0.2300.1470.1380.9961.0000.2400.1430.4060.390
영업장주변구분명0.0350.1810.1640.3710.2401.0000.0000.1400.145
급수시설구분명0.0000.2300.1830.0000.1430.0001.0000.0750.078
다중이용업소여부0.0000.2380.1700.3700.4060.1400.0751.0000.920
영업상태0.0000.2270.1520.3530.3900.1450.0780.9201.000

Missing values

2023-12-13T00:26:45.294131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:26:45.550909image/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.
2023-12-13T00:26:46.069870image/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

구분소재지전화소재지면적지번주소도로명주소사업장명위생업태명영업장주변구분명급수시설구분명다중이용업소여부시설총규모영업상태폐업일자위도경도
0유흥주점영업055-325-6686124.56경상남도 김해시 부원동 608-18번지경상남도 김해시 김해대로2355번길 19 (부원동)루루노래주점비어(바)살롱유흥업소밀집지역상수도전용Y124.56영업<NA>35.22828128.883645
1유흥주점영업055-326-2237197.96경상남도 김해시 서상동 258-1번지경상남도 김해시 가락로49번길 14 (서상동)양지카페주점스텐드바기타상수도전용Y197.96영업<NA>35.230959128.880537
2유흥주점영업055-324-9911127경상남도 김해시 외동 1255-6번지 502호경상남도 김해시 내외중앙로 39 (외동, 502호)신세계노래주점비어(바)살롱유흥업소밀집지역상수도전용Y127.0영업<NA>35.233508128.866137
3유흥주점영업055-343-0028193.7경상남도 김해시 진영읍 진영리 1614-6번지경상남도 김해시 진영읍 김해대로365번길 6-4노찾사패밀리노래타운비어(바)살롱유흥업소밀집지역<NA>Y193.7영업<NA>35.307024128.730306
4유흥주점영업<NA>123.52경상남도 김해시 진영읍 진영리 1614-9번지 진영그랜드프라자 503호경상남도 김해시 진영읍 김해대로361번길 2, 503호 (진영그랜드프라자)국빈노래주점비어(바)살롱유흥업소밀집지역상수도전용Y123.52영업<NA>35.307018128.729858
5유흥주점영업055-345-6678130.82경상남도 김해시 진영읍 진영리 1614-9번지 진영그랜드프라자 310호경상남도 김해시 진영읍 김해대로361번길 2, 310호 (진영그랜드프라자)채팅노래주점비어(바)살롱유흥업소밀집지역상수도전용Y130.82영업<NA>35.307018128.729858
6유흥주점영업<NA>95.76경상남도 김해시 어방동 1093-2번지경상남도 김해시 분성로517번길 6-10 (어방동)해원노래주점비어(바)살롱유흥업소밀집지역<NA>Y95.76영업<NA>35.235959128.902988
7유흥주점영업<NA>560.37경상남도 김해시 어방동 523번지경상남도 김해시 인제로188번길 6, 701호 (어방동, 힐튼상가)힐튼노래주점비어(바)살롱유흥업소밀집지역상수도전용Y560.37영업<NA>35.24508128.904598
8유흥주점영업055-333-226490.88경상남도 김해시 어방동 1095-14번지 2층경상남도 김해시 분성로529번길 6, 2층 (어방동)은성비지니스룸비어(바)살롱<NA><NA>Y90.88영업<NA>35.236139128.904067
9유흥주점영업055-321-5436132.03경상남도 김해시 삼방동 176-6번지경상남도 김해시 삼안로195번길 38, 2층 (삼방동)로또노래주점비어(바)살롱유흥업소밀집지역상수도전용Y132.03영업<NA>35.245094128.907898
구분소재지전화소재지면적지번주소도로명주소사업장명위생업태명영업장주변구분명급수시설구분명다중이용업소여부시설총규모영업상태폐업일자위도경도
1128단란주점영업<NA>190.76경상남도 김해시 동상동 896번지경상남도 김해시 분성로335번길 20 (동상동)랄린푸드단란주점기타<NA>Y190.76영업<NA>35.235358128.882492
1129단란주점영업<NA>147경상남도 김해시 부원동 833-9번지경상남도 김해시 가락로 55, 지1층 (부원동)따완뎅단란주점유흥업소밀집지역상수도전용Y147.0영업<NA>35.231478128.881646
1130단란주점영업055-323-190086.39경상남도 김해시 삼계동 1487-4번지경상남도 김해시 해반천로144번길 36, 802호 (삼계동, 다이너스티빌딩)락휴노래타운단란주점유흥업소밀집지역상수도전용Y86.39영업<NA>35.26122128.871384
1131단란주점영업055-332-2095104.61경상남도 김해시 내동 1122-4번지 동화선프라자 803호경상남도 김해시 내외중앙로 81 (내동, 동화선프라자 803호)더큐브노래주점단란주점유흥업소밀집지역상수도전용Y104.61영업<NA>35.237255128.866854
1132단란주점영업055-333-6776126.15경상남도 김해시 삼방동 172-11번지 에이스빌딩 301호경상남도 김해시 활천로255번길 36 (삼방동, 에이스빌딩 301호)뮤즈노래타운2단란주점유흥업소밀집지역상수도전용Y126.15영업<NA>35.245494128.906299
1133단란주점영업055-333-2264142.39경상남도 김해시 어방동 1095-14번지경상남도 김해시 분성로529번길 6, 2층 (어방동)은성가라오케단란주점유흥업소밀집지역상수도전용Y142.39영업<NA>35.236139128.904067
1134단란주점영업055-331-5660148.36경상남도 김해시 삼계동 1488-5번지 2층경상남도 김해시 해반천로144번길 28 (삼계동, 2층)별이빛나는밤에단란주점유흥업소밀집지역상수도전용Y148.36영업<NA>35.261122128.870338
1135단란주점영업<NA>60.58경상남도 김해시 삼계동 1478-7번지 2층경상남도 김해시 해반천로144번길 35-15 (삼계동, 2층)힐링단란주점단란주점유흥업소밀집지역상수도전용Y60.58영업<NA>35.261973128.870897
1136단란주점영업055-313-0449125.24경상남도 김해시 삼계동 1461-5번지 302호경상남도 김해시 해반천로144번길 35-30, 302호 (삼계동)스고이단란주점단란주점유흥업소밀집지역상수도전용Y125.24영업<NA>35.262667128.871219
1137단란주점영업055-313-7474143.29경상남도 김해시 삼계동 1479-1번지 6층호경상남도 김해시 해반천로144번길 35-22, 6층호 (삼계동)필단란주점단란주점유흥업소밀집지역상수도전용Y143.29영업<NA>35.262268128.871258