Overview

Dataset statistics

Number of variables5
Number of observations218
Missing cells1
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.6 KiB
Average record size in memory40.6 B

Variable types

Categorical2
Text3

Dataset

Description인천광역시 서구에 있는 업종, 상호, 지번주소, 도로명주소를 포함한 당구장업 현황입니다. 인천광역시 서구 당구장 위치를 찾는데 유용한 정보입니다.
Author인천광역시 서구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15081309&srcSe=7661IVAWM27C61E190

Alerts

업종 has constant value ""Constant
데이터기준일자 has constant value ""Constant

Reproduction

Analysis started2024-01-28 16:59:24.786143
Analysis finished2024-01-28 16:59:25.188530
Duration0.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
당구장업
218 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
당구장업 218
100.0%

Length

2024-01-29T01:59:25.237575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:59:25.316284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
당구장업 218
100.0%

상호
Text

Distinct200
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-01-29T01:59:25.537079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length13
Mean length6.3440367
Min length3

Characters and Unicode

Total characters1383
Distinct characters229
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique189 ?
Unique (%)86.7%

Sample

1st row유진당구장
2nd rowo.k당구장
3rd row경인당구장
4th row신명당구장
5th row보성당구장
ValueCountFrequency (%)
당구클럽 42
 
13.5%
당구장 38
 
12.2%
sbs당구장 6
 
1.9%
매니아 3
 
1.0%
sbs당구클럽 3
 
1.0%
한게임 3
 
1.0%
원당구장 3
 
1.0%
제이제이 2
 
0.6%
샤방 2
 
0.6%
sbs 2
 
0.6%
Other values (200) 208
66.7%
2024-01-29T01:59:25.943699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 207
15.0%
당 206
14.9%
장 120
 
8.7%
94
 
6.8%
클 84
 
6.1%
럽 84
 
6.1%
S 31
 
2.2%
B 21
 
1.5%
스 19
 
1.4%
리 17
 
1.2%
Other values (219) 500
36.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1141
82.5%
Uppercase Letter 117
 
8.5%
Space Separator 94
 
6.8%
Lowercase Letter 17
 
1.2%
Decimal Number 6
 
0.4%
Other Punctuation 5
 
0.4%
Math Symbol 1
 
0.1%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 207
18.1%
당 206
18.1%
장 120
 
10.5%
클 84
 
7.4%
럽 84
 
7.4%
스 19
 
1.7%
리 17
 
1.5%
아 16
 
1.4%
이 15
 
1.3%
제 10
 
0.9%
Other values (177) 363
31.8%
Uppercase Letter
ValueCountFrequency (%)
S 31
26.5%
B 21
17.9%
K 9
 
7.7%
C 8
 
6.8%
O 7
 
6.0%
J 6
 
5.1%
T 6
 
5.1%
N 5
 
4.3%
I 5
 
4.3%
P 3
 
2.6%
Other values (9) 16
13.7%
Lowercase Letter
ValueCountFrequency (%)
c 2
11.8%
l 2
11.8%
a 2
11.8%
e 2
11.8%
i 1
 
5.9%
h 1
 
5.9%
f 1
 
5.9%
s 1
 
5.9%
d 1
 
5.9%
r 1
 
5.9%
Other values (3) 3
17.6%
Decimal Number
ValueCountFrequency (%)
1 2
33.3%
6 1
16.7%
8 1
16.7%
7 1
16.7%
2 1
16.7%
Space Separator
ValueCountFrequency (%)
94
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%
Math Symbol
ValueCountFrequency (%)
＋ 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1140
82.4%
Latin 134
 
9.7%
Common 108
 
7.8%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 207
18.2%
당 206
18.1%
장 120
 
10.5%
클 84
 
7.4%
럽 84
 
7.4%
스 19
 
1.7%
리 17
 
1.5%
아 16
 
1.4%
이 15
 
1.3%
제 10
 
0.9%
Other values (176) 362
31.8%
Latin
ValueCountFrequency (%)
S 31
23.1%
B 21
15.7%
K 9
 
6.7%
C 8
 
6.0%
O 7
 
5.2%
J 6
 
4.5%
T 6
 
4.5%
N 5
 
3.7%
I 5
 
3.7%
P 3
 
2.2%
Other values (22) 33
24.6%
Common
ValueCountFrequency (%)
94
87.0%
. 5
 
4.6%
1 2
 
1.9%
＋ 1
 
0.9%
6 1
 
0.9%
8 1
 
0.9%
7 1
 
0.9%
( 1
 
0.9%
) 1
 
0.9%
2 1
 
0.9%
Han
ValueCountFrequency (%)
秀 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1140
82.4%
ASCII 241
 
17.4%
None 1
 
0.1%
CJK 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 207
18.2%
당 206
18.1%
장 120
 
10.5%
클 84
 
7.4%
럽 84
 
7.4%
스 19
 
1.7%
리 17
 
1.5%
아 16
 
1.4%
이 15
 
1.3%
제 10
 
0.9%
Other values (176) 362
31.8%
ASCII
ValueCountFrequency (%)
94
39.0%
S 31
 
12.9%
B 21
 
8.7%
K 9
 
3.7%
C 8
 
3.3%
O 7
 
2.9%
J 6
 
2.5%
T 6
 
2.5%
. 5
 
2.1%
N 5
 
2.1%
Other values (31) 49
20.3%
None
ValueCountFrequency (%)
＋ 1
100.0%
CJK
ValueCountFrequency (%)
秀 1
100.0%

지번주소
Text

Distinct215
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-01-29T01:59:26.243223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length41
Mean length23.912844
Min length17

Characters and Unicode

Total characters5213
Distinct characters152
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

Unique212 ?
Unique (%)97.2%

Sample

1st row인천광역시 서구 가좌동 157-18
2nd row인천광역시 서구 석남동 576-49
3rd row인천광역시 서구 석남동 179-109
4th row인천광역시 서구 가좌동 139-10
5th row인천광역시 서구 가정동 511
ValueCountFrequency (%)
인천광역시 218
20.2%
서구 218
20.2%
석남동 45
 
4.2%
가좌동 42
 
3.9%
2층 30
 
2.8%
심곡동 24
 
2.2%
3층 23
 
2.1%
가정동 17
 
1.6%
마전동 16
 
1.5%
청라동 15
 
1.4%
Other values (338) 429
39.8%
2024-01-29T01:59:26.659701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1058
20.3%
1 229
 
4.4%
구 225
 
4.3%
동 221
 
4.2%
서 220
 
4.2%
인 220
 
4.2%
시 219
 
4.2%
천 219
 
4.2%
역 218
 
4.2%
광 218
 
4.2%
Other values (142) 2166
41.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2660
51.0%
Decimal Number 1246
23.9%
Space Separator 1058
 
20.3%
Dash Punctuation 199
 
3.8%
Other Punctuation 21
 
0.4%
Open Punctuation 12
 
0.2%
Close Punctuation 12
 
0.2%
Math Symbol 3
 
0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 225
 
8.5%
동 221
 
8.3%
서 220
 
8.3%
인 220
 
8.3%
시 219
 
8.2%
천 219
 
8.2%
역 218
 
8.2%
광 218
 
8.2%
층 75
 
2.8%
가 60
 
2.3%
Other values (123) 765
28.8%
Decimal Number
ValueCountFrequency (%)
1 229
18.4%
2 192
15.4%
3 155
12.4%
0 132
10.6%
5 116
9.3%
4 109
8.7%
6 96
7.7%
7 78
 
6.3%
8 75
 
6.0%
9 64
 
5.1%
Other Punctuation
ValueCountFrequency (%)
, 20
95.2%
. 1
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
K 1
50.0%
M 1
50.0%
Space Separator
ValueCountFrequency (%)
1058
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 199
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2660
51.0%
Common 2551
48.9%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 225
 
8.5%
동 221
 
8.3%
서 220
 
8.3%
인 220
 
8.3%
시 219
 
8.2%
천 219
 
8.2%
역 218
 
8.2%
광 218
 
8.2%
층 75
 
2.8%
가 60
 
2.3%
Other values (123) 765
28.8%
Common
ValueCountFrequency (%)
1058
41.5%
1 229
 
9.0%
- 199
 
7.8%
2 192
 
7.5%
3 155
 
6.1%
0 132
 
5.2%
5 116
 
4.5%
4 109
 
4.3%
6 96
 
3.8%
7 78
 
3.1%
Other values (7) 187
 
7.3%
Latin
ValueCountFrequency (%)
K 1
50.0%
M 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2660
51.0%
ASCII 2553
49.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1058
41.4%
1 229
 
9.0%
- 199
 
7.8%
2 192
 
7.5%
3 155
 
6.1%
0 132
 
5.2%
5 116
 
4.5%
4 109
 
4.3%
6 96
 
3.8%
7 78
 
3.1%
Other values (9) 189
 
7.4%
Hangul
ValueCountFrequency (%)
구 225
 
8.5%
동 221
 
8.3%
서 220
 
8.3%
인 220
 
8.3%
시 219
 
8.2%
천 219
 
8.2%
역 218
 
8.2%
광 218
 
8.2%
층 75
 
2.8%
가 60
 
2.3%
Other values (123) 765
28.8%

도로명주소
Text

Distinct215
Distinct (%)99.1%
Missing1
Missing (%)0.5%
Memory size1.8 KiB
2024-01-29T01:59:26.877997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length45
Mean length30.718894
Min length21

Characters and Unicode

Total characters6666
Distinct characters208
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

Unique213 ?
Unique (%)98.2%

Sample

1st row인천광역시 서구 가정로78번길 31 (가좌동)
2nd row인천광역시 서구 거북로 98-1 (석남동)
3rd row인천광역시 서구 염곡로 321 (석남동, 상일빌딩)
4th row인천광역시 서구 가정로 127-1 (가좌동)
5th row인천광역시 서구 원창로 240 (가정동)
ValueCountFrequency (%)
인천광역시 217
 
16.6%
서구 217
 
16.6%
석남동 35
 
2.7%
2층 32
 
2.5%
가좌동 28
 
2.1%
가정로 26
 
2.0%
3층 22
 
1.7%
마전동 16
 
1.2%
심곡동 15
 
1.1%
연희동 15
 
1.1%
Other values (433) 682
52.3%
2024-01-29T01:59:27.220269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1148
 
17.2%
, 234
 
3.5%
서 234
 
3.5%
) 228
 
3.4%
( 228
 
3.4%
동 225
 
3.4%
로 223
 
3.3%
인 220
 
3.3%
구 218
 
3.3%
천 218
 
3.3%
Other values (198) 3490
52.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3667
55.0%
Space Separator 1148
 
17.2%
Decimal Number 1122
 
16.8%
Other Punctuation 235
 
3.5%
Close Punctuation 228
 
3.4%
Open Punctuation 228
 
3.4%
Dash Punctuation 30
 
0.5%
Math Symbol 5
 
0.1%
Uppercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 234
 
6.4%
동 225
 
6.1%
로 223
 
6.1%
인 220
 
6.0%
구 218
 
5.9%
천 218
 
5.9%
시 218
 
5.9%
광 218
 
5.9%
역 217
 
5.9%
층 113
 
3.1%
Other values (178) 1563
42.6%
Decimal Number
ValueCountFrequency (%)
2 198
17.6%
1 184
16.4%
3 149
13.3%
0 132
11.8%
4 108
9.6%
5 85
7.6%
7 78
 
7.0%
8 67
 
6.0%
6 64
 
5.7%
9 57
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
M 1
33.3%
B 1
33.3%
K 1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 234
99.6%
. 1
 
0.4%
Space Separator
ValueCountFrequency (%)
1148
100.0%
Close Punctuation
ValueCountFrequency (%)
) 228
100.0%
Open Punctuation
ValueCountFrequency (%)
( 228
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3667
55.0%
Common 2996
44.9%
Latin 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 234
 
6.4%
동 225
 
6.1%
로 223
 
6.1%
인 220
 
6.0%
구 218
 
5.9%
천 218
 
5.9%
시 218
 
5.9%
광 218
 
5.9%
역 217
 
5.9%
층 113
 
3.1%
Other values (178) 1563
42.6%
Common
ValueCountFrequency (%)
1148
38.3%
, 234
 
7.8%
) 228
 
7.6%
( 228
 
7.6%
2 198
 
6.6%
1 184
 
6.1%
3 149
 
5.0%
0 132
 
4.4%
4 108
 
3.6%
5 85
 
2.8%
Other values (7) 302
 
10.1%
Latin
ValueCountFrequency (%)
M 1
33.3%
B 1
33.3%
K 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3667
55.0%
ASCII 2999
45.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1148
38.3%
, 234
 
7.8%
) 228
 
7.6%
( 228
 
7.6%
2 198
 
6.6%
1 184
 
6.1%
3 149
 
5.0%
0 132
 
4.4%
4 108
 
3.6%
5 85
 
2.8%
Other values (10) 305
 
10.2%
Hangul
ValueCountFrequency (%)
서 234
 
6.4%
동 225
 
6.1%
로 223
 
6.1%
인 220
 
6.0%
구 218
 
5.9%
천 218
 
5.9%
시 218
 
5.9%
광 218
 
5.9%
역 217
 
5.9%
층 113
 
3.1%
Other values (178) 1563
42.6%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023/04/03
218 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023/04/03
2nd row2023/04/03
3rd row2023/04/03
4th row2023/04/03
5th row2023/04/03

Common Values

ValueCountFrequency (%)
2023/04/03 218
100.0%

Length

2024-01-29T01:59:27.325417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:59:27.402786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023/04/03 218
100.0%

Missing values

2024-01-29T01:59:25.073608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T01:59:25.157994image/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.

Sample

업종상호지번주소도로명주소데이터기준일자
0당구장업유진당구장인천광역시 서구 가좌동 157-18인천광역시 서구 가정로78번길 31 (가좌동)2023/04/03
1당구장업o.k당구장인천광역시 서구 석남동 576-49인천광역시 서구 거북로 98-1 (석남동)2023/04/03
2당구장업경인당구장인천광역시 서구 석남동 179-109인천광역시 서구 염곡로 321 (석남동, 상일빌딩)2023/04/03
3당구장업신명당구장인천광역시 서구 가좌동 139-10인천광역시 서구 가정로 127-1 (가좌동)2023/04/03
4당구장업보성당구장인천광역시 서구 가정동 511인천광역시 서구 원창로 240 (가정동)2023/04/03
5당구장업약수당구장인천광역시 서구 석남동 472-4인천광역시 서구 서달로137번길 10 (석남동)2023/04/03
6당구장업석남당구클럽인천광역시 서구 석남동 583-47인천광역시 서구 거북로 88 (석남동)2023/04/03
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