Overview

Dataset statistics

Number of variables5
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 KiB
Average record size in memory45.7 B

Variable types

Categorical2
Text3

Dataset

Description대전광역시 문화재 매매업소 현황 데이터로 자치구별 문화재매매업소 현황(업소명, 주소, 허가번호)가 기록된 데이터 입니다.
URLhttps://www.data.go.kr/data/15081956/fileData.do

Alerts

시도 has constant value ""Constant
업소명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:27:42.964198
Analysis finished2023-12-12 16:27:43.328203
Duration0.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
대전
23 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대전 23
100.0%

Length

2023-12-13T01:27:43.394760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:27:43.509396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전 23
100.0%

시군구
Categorical

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
동구
13 
서구
4 
중구
3 
대덕구
2 
유성구
 
1

Length

Max length3
Median length2
Mean length2.1304348
Min length2

Unique

Unique1 ?
Unique (%)4.3%

Sample

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

Common Values

ValueCountFrequency (%)
동구 13
56.5%
서구 4
 
17.4%
중구 3
 
13.0%
대덕구 2
 
8.7%
유성구 1
 
4.3%

Length

2023-12-13T01:27:43.608564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:27:43.714282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동구 13
56.5%
서구 4
 
17.4%
중구 3
 
13.0%
대덕구 2
 
8.7%
유성구 1
 
4.3%

허가번호
Text

Distinct18
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-13T01:27:43.882109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters207
Distinct characters10
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

Unique14 ?
Unique (%)60.9%

Sample

1st row2008-0002
2nd row2008-0003
3rd row2008-0004
4th row2008-0005
5th row2008-0008
ValueCountFrequency (%)
2016-0001 3
 
13.0%
2017-0001 2
 
8.7%
2008-0002 2
 
8.7%
2008-0005 2
 
8.7%
2008-0003 1
 
4.3%
2012-0002 1
 
4.3%
2007-0005 1
 
4.3%
2007-0002 1
 
4.3%
2008-0007 1
 
4.3%
2016-0002 1
 
4.3%
Other values (8) 8
34.8%
2023-12-13T01:27:44.170139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 105
50.7%
2 29
 
14.0%
- 23
 
11.1%
1 22
 
10.6%
8 11
 
5.3%
7 6
 
2.9%
6 4
 
1.9%
5 3
 
1.4%
3 3
 
1.4%
4 1
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 184
88.9%
Dash Punctuation 23
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 105
57.1%
2 29
 
15.8%
1 22
 
12.0%
8 11
 
6.0%
7 6
 
3.3%
6 4
 
2.2%
5 3
 
1.6%
3 3
 
1.6%
4 1
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 105
50.7%
2 29
 
14.0%
- 23
 
11.1%
1 22
 
10.6%
8 11
 
5.3%
7 6
 
2.9%
6 4
 
1.9%
5 3
 
1.4%
3 3
 
1.4%
4 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 105
50.7%
2 29
 
14.0%
- 23
 
11.1%
1 22
 
10.6%
8 11
 
5.3%
7 6
 
2.9%
6 4
 
1.9%
5 3
 
1.4%
3 3
 
1.4%
4 1
 
0.5%

업소명
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-13T01:27:44.383453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.9130435
Min length3

Characters and Unicode

Total characters90
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row백양사
2nd row무등산방
3rd row만물상
4th row선비당
5th row민속촌
ValueCountFrequency (%)
백양사 1
 
4.3%
디갤러리 1
 
4.3%
송원고미술 1
 
4.3%
고향미술품 1
 
4.3%
현대서적 1
 
4.3%
집고당 1
 
4.3%
용화랑 1
 
4.3%
오원화랑 1
 
4.3%
한밭고전원 1
 
4.3%
영갤러리 1
 
4.3%
Other values (13) 13
56.5%
2023-12-13T01:27:44.740383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
고 6
 
6.7%
리 5
 
5.6%
러 5
 
5.6%
갤 5
 
5.6%
미 4
 
4.4%
당 4
 
4.4%
사 3
 
3.3%
원 3
 
3.3%
오 3
 
3.3%
술 3
 
3.3%
Other values (40) 49
54.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 90
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
고 6
 
6.7%
리 5
 
5.6%
러 5
 
5.6%
갤 5
 
5.6%
미 4
 
4.4%
당 4
 
4.4%
사 3
 
3.3%
원 3
 
3.3%
오 3
 
3.3%
술 3
 
3.3%
Other values (40) 49
54.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 90
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
고 6
 
6.7%
리 5
 
5.6%
러 5
 
5.6%
갤 5
 
5.6%
미 4
 
4.4%
당 4
 
4.4%
사 3
 
3.3%
원 3
 
3.3%
오 3
 
3.3%
술 3
 
3.3%
Other values (40) 49
54.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 90
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
고 6
 
6.7%
리 5
 
5.6%
러 5
 
5.6%
갤 5
 
5.6%
미 4
 
4.4%
당 4
 
4.4%
사 3
 
3.3%
원 3
 
3.3%
오 3
 
3.3%
술 3
 
3.3%
Other values (40) 49
54.4%

영업장소
Text

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-13T01:27:44.968998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length21
Mean length19.652174
Min length12

Characters and Unicode

Total characters452
Distinct characters70
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

Unique21 ?
Unique (%)91.3%

Sample

1st row대전 동구 대전로825번길4(정동)
2nd row대전 동구 대전로 938-1(삼성동)
3rd row대전 동구 미화7길 18(소제동)
4th row대전 동구 대전로906번길98(삼성동)
5th row대전 동구 가양로 142-6(가양동)
ValueCountFrequency (%)
대전 16
 
18.2%
동구 13
 
14.8%
서구 4
 
4.5%
대전로 4
 
4.5%
중구 3
 
3.4%
대전로845(정동 2
 
2.3%
대덕구 2
 
2.3%
선화로 2
 
2.3%
관저동로90번길 1
 
1.1%
문정로 1
 
1.1%
Other values (40) 40
45.5%
2023-12-13T01:27:45.306396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
65
 
14.4%
동 33
 
7.3%
대 30
 
6.6%
전 27
 
6.0%
구 23
 
5.1%
로 21
 
4.6%
1 18
 
4.0%
4 18
 
4.0%
( 17
 
3.8%
) 17
 
3.8%
Other values (60) 183
40.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 239
52.9%
Decimal Number 105
23.2%
Space Separator 65
 
14.4%
Open Punctuation 17
 
3.8%
Close Punctuation 17
 
3.8%
Other Punctuation 5
 
1.1%
Dash Punctuation 3
 
0.7%
Uppercase Letter 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 33
13.8%
대 30
12.6%
전 27
 
11.3%
구 23
 
9.6%
로 21
 
8.8%
길 10
 
4.2%
번 8
 
3.3%
성 7
 
2.9%
정 6
 
2.5%
삼 6
 
2.5%
Other values (44) 68
28.5%
Decimal Number
ValueCountFrequency (%)
1 18
17.1%
4 18
17.1%
9 13
12.4%
2 12
11.4%
3 11
10.5%
0 9
8.6%
8 9
8.6%
6 5
 
4.8%
7 5
 
4.8%
5 5
 
4.8%
Space Separator
ValueCountFrequency (%)
65
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 239
52.9%
Common 212
46.9%
Latin 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 33
13.8%
대 30
12.6%
전 27
 
11.3%
구 23
 
9.6%
로 21
 
8.8%
길 10
 
4.2%
번 8
 
3.3%
성 7
 
2.9%
정 6
 
2.5%
삼 6
 
2.5%
Other values (44) 68
28.5%
Common
ValueCountFrequency (%)
65
30.7%
1 18
 
8.5%
4 18
 
8.5%
( 17
 
8.0%
) 17
 
8.0%
9 13
 
6.1%
2 12
 
5.7%
3 11
 
5.2%
0 9
 
4.2%
8 9
 
4.2%
Other values (5) 23
 
10.8%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 239
52.9%
ASCII 213
47.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
65
30.5%
1 18
 
8.5%
4 18
 
8.5%
( 17
 
8.0%
) 17
 
8.0%
9 13
 
6.1%
2 12
 
5.6%
3 11
 
5.2%
0 9
 
4.2%
8 9
 
4.2%
Other values (6) 24
 
11.3%
Hangul
ValueCountFrequency (%)
동 33
13.8%
대 30
12.6%
전 27
 
11.3%
구 23
 
9.6%
로 21
 
8.8%
길 10
 
4.2%
번 8
 
3.3%
성 7
 
2.9%
정 6
 
2.5%
삼 6
 
2.5%
Other values (44) 68
28.5%

Correlations

2023-12-13T01:27:45.398071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구허가번호업소명영업장소
시군구1.0000.0001.0001.000
허가번호0.0001.0001.0000.918
업소명1.0001.0001.0001.000
영업장소1.0000.9181.0001.000

Missing values

2023-12-13T01:27:43.196720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:27:43.292566image/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

시도시군구허가번호업소명영업장소
0대전동구2008-0002백양사대전 동구 대전로825번길4(정동)
1대전동구2008-0003무등산방대전 동구 대전로 938-1(삼성동)
2대전동구2008-0004만물상대전 동구 미화7길 18(소제동)
3대전동구2008-0005선비당대전 동구 대전로906번길98(삼성동)
4대전동구2008-0008민속촌대전 동구 가양로 142-6(가양동)
5대전동구2008-0010동양화랑대전 동구 대전로 906번길 94(삼성동)
6대전동구2008-0011대보사대전 동구 대전로 942(삼성동)
7대전동구2010-0001백제당대전 동구 대전로845(정동)
8대전동구2011-0001명품고미술대전 동구 대전로 823-2(정동)
9대전동구2011-0003오왕갤러리대전 동구 대전로926(삼성동)
시도시군구허가번호업소명영업장소
13대전중구2017-0001갤러리미당대전 중구 중교로 9(대흥동, 나이스타운)
14대전중구2008-0005영갤러리대전 중구 선화로 97번길 3(선화동)
15대전중구2008-0007한밭고전원대전광역시 중구 선화로 84(선화동)
16대전서구2007-0002오원화랑서구 둔산중로78번길 36, B1호
17대전서구2007-0005용화랑서구 문정로 131, 11동 203호
18대전서구2008-0002집고당서구 벌곡로1349번길 31
19대전서구2017-0001현대서적서구 관저동로90번길 47, 1103동 1102호
20대전유성구2016-0001고향미술품대전 유성구 현충원로401 (우34144)
21대전대덕구2007-0003송원고미술대덕구 신탄진로 244
22대전대덕구2016-0001고려사대덕구 오정로75번길94