Overview

Dataset statistics

Number of variables5
Number of observations37
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory43.6 B

Variable types

Text4
Categorical1

Dataset

Description파일 다운로드
AuthorSH공사
URLhttps://data.seoul.go.kr/dataList/OA-12920/F/1/datasetView.do

Reproduction

Analysis started2023-12-11 04:51:11.047432
Analysis finished2023-12-11 04:51:11.521039
Duration0.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

과목
Text

Distinct34
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Memory size428.0 B
2023-12-11T13:51:11.662704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length7.2162162
Min length4

Characters and Unicode

Total characters267
Distinct characters73
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)83.8%

Sample

1st row1.유동자산
2nd row- 당좌자산
3rd row- 재고자산
4th row2. 비유동자산
5th row- 투자자산
ValueCountFrequency (%)
18
25.4%
2 4
 
5.6%
3 2
 
2.8%
주택건설사업 2
 
2.8%
4 2
 
2.8%
영업외 2
 
2.8%
1 2
 
2.8%
기타사업 2
 
2.8%
임대사업 2
 
2.8%
매출총이익 1
 
1.4%
Other values (34) 34
47.9%
2023-12-11T13:51:12.110868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
46
 
17.2%
- 18
 
6.7%
. 15
 
5.6%
자 14
 
5.2%
업 9
 
3.4%
산 9
 
3.4%
익 8
 
3.0%
비 7
 
2.6%
유 6
 
2.2%
사 6
 
2.2%
Other values (63) 129
48.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 173
64.8%
Space Separator 46
 
17.2%
Dash Punctuation 18
 
6.7%
Other Punctuation 15
 
5.6%
Decimal Number 15
 
5.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
자 14
 
8.1%
업 9
 
5.2%
산 9
 
5.2%
익 8
 
4.6%
비 7
 
4.0%
유 6
 
3.5%
사 6
 
3.5%
이 6
 
3.5%
기 5
 
2.9%
계 5
 
2.9%
Other values (53) 98
56.6%
Decimal Number
ValueCountFrequency (%)
1 4
26.7%
2 4
26.7%
4 2
13.3%
3 2
13.3%
5 1
 
6.7%
6 1
 
6.7%
7 1
 
6.7%
Space Separator
ValueCountFrequency (%)
46
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Other Punctuation
ValueCountFrequency (%)
. 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 173
64.8%
Common 94
35.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
자 14
 
8.1%
업 9
 
5.2%
산 9
 
5.2%
익 8
 
4.6%
비 7
 
4.0%
유 6
 
3.5%
사 6
 
3.5%
이 6
 
3.5%
기 5
 
2.9%
계 5
 
2.9%
Other values (53) 98
56.6%
Common
ValueCountFrequency (%)
46
48.9%
- 18
 
19.1%
. 15
 
16.0%
1 4
 
4.3%
2 4
 
4.3%
4 2
 
2.1%
3 2
 
2.1%
5 1
 
1.1%
6 1
 
1.1%
7 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 173
64.8%
ASCII 94
35.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
46
48.9%
- 18
 
19.1%
. 15
 
16.0%
1 4
 
4.3%
2 4
 
4.3%
4 2
 
2.1%
3 2
 
2.1%
5 1
 
1.1%
6 1
 
1.1%
7 1
 
1.1%
Hangul
ValueCountFrequency (%)
자 14
 
8.1%
업 9
 
5.2%
산 9
 
5.2%
익 8
 
4.6%
비 7
 
4.0%
유 6
 
3.5%
사 6
 
3.5%
이 6
 
3.5%
기 5
 
2.9%
계 5
 
2.9%
Other values (53) 98
56.6%

2021년
Text

Distinct35
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Memory size428.0 B
2023-12-11T13:51:12.398159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.1621622
Min length2

Characters and Unicode

Total characters191
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)89.2%

Sample

1st row68,014
2nd row34,313
3rd row33,701
4th row203,467
5th row4,043
ValueCountFrequency (%)
1,879 2
 
5.4%
271,481 2
 
5.4%
68,014 1
 
2.7%
58 1
 
2.7%
1,552 1
 
2.7%
67 1
 
2.7%
22,130 1
 
2.7%
7,088 1
 
2.7%
8,788 1
 
2.7%
12,709 1
 
2.7%
Other values (25) 25
67.6%
2023-12-11T13:51:12.847394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 32
16.8%
, 29
15.2%
8 19
9.9%
7 17
8.9%
4 17
8.9%
2 15
7.9%
0 15
7.9%
3 14
7.3%
9 11
 
5.8%
6 10
 
5.2%
Other values (2) 12
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 160
83.8%
Other Punctuation 29
 
15.2%
Dash Punctuation 2
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 32
20.0%
8 19
11.9%
7 17
10.6%
4 17
10.6%
2 15
9.4%
0 15
9.4%
3 14
8.8%
9 11
 
6.9%
6 10
 
6.2%
5 10
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 29
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 191
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 32
16.8%
, 29
15.2%
8 19
9.9%
7 17
8.9%
4 17
8.9%
2 15
7.9%
0 15
7.9%
3 14
7.3%
9 11
 
5.8%
6 10
 
5.2%
Other values (2) 12
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 191
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 32
16.8%
, 29
15.2%
8 19
9.9%
7 17
8.9%
4 17
8.9%
2 15
7.9%
0 15
7.9%
3 14
7.3%
9 11
 
5.8%
6 10
 
5.2%
Other values (2) 12
 
6.3%

2020년
Text

Distinct35
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Memory size428.0 B
2023-12-11T13:51:13.151029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.1351351
Min length2

Characters and Unicode

Total characters190
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)89.2%

Sample

1st row76,456
2nd row33,921
3rd row42,535
4th row189,783
5th row3,903
ValueCountFrequency (%)
1,781 2
 
5.4%
266,239 2
 
5.4%
76,456 1
 
2.7%
29 1
 
2.7%
1,397 1
 
2.7%
43 1
 
2.7%
20,771 1
 
2.7%
7,806 1
 
2.7%
7,223 1
 
2.7%
10,593 1
 
2.7%
Other values (25) 25
67.6%
2023-12-11T13:51:13.628564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 29
15.3%
1 24
12.6%
3 23
12.1%
2 20
10.5%
7 18
9.5%
9 16
8.4%
6 15
7.9%
4 14
7.4%
5 12
6.3%
0 9
 
4.7%
Other values (2) 10
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 159
83.7%
Other Punctuation 29
 
15.3%
Dash Punctuation 2
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 24
15.1%
3 23
14.5%
2 20
12.6%
7 18
11.3%
9 16
10.1%
6 15
9.4%
4 14
8.8%
5 12
7.5%
0 9
 
5.7%
8 8
 
5.0%
Other Punctuation
ValueCountFrequency (%)
, 29
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 190
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 29
15.3%
1 24
12.6%
3 23
12.1%
2 20
10.5%
7 18
9.5%
9 16
8.4%
6 15
7.9%
4 14
7.4%
5 12
6.3%
0 9
 
4.7%
Other values (2) 10
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 29
15.3%
1 24
12.6%
3 23
12.1%
2 20
10.5%
7 18
9.5%
9 16
8.4%
6 15
7.9%
4 14
7.4%
5 12
6.3%
0 9
 
4.7%
Other values (2) 10
 
5.3%

증감
Text

Distinct35
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Memory size428.0 B
2023-12-11T13:51:13.937698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length3.972973
Min length2

Characters and Unicode

Total characters147
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)89.2%

Sample

1st row-8,442
2nd row392
3rd row-8,834
4th row13,684
5th row140
ValueCountFrequency (%)
98 2
 
5.4%
5,242 2
 
5.4%
8,442 1
 
2.7%
29 1
 
2.7%
155 1
 
2.7%
24 1
 
2.7%
1,359 1
 
2.7%
718 1
 
2.7%
1,565 1
 
2.7%
2,116 1
 
2.7%
Other values (25) 25
67.6%
2023-12-11T13:51:14.446823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 23
15.6%
2 20
13.6%
, 16
10.9%
4 15
10.2%
8 14
9.5%
3 13
8.8%
- 11
7.5%
5 10
6.8%
9 9
 
6.1%
7 6
 
4.1%
Other values (2) 10
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 120
81.6%
Other Punctuation 16
 
10.9%
Dash Punctuation 11
 
7.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 23
19.2%
2 20
16.7%
4 15
12.5%
8 14
11.7%
3 13
10.8%
5 10
8.3%
9 9
 
7.5%
7 6
 
5.0%
6 5
 
4.2%
0 5
 
4.2%
Other Punctuation
ValueCountFrequency (%)
, 16
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 23
15.6%
2 20
13.6%
, 16
10.9%
4 15
10.2%
8 14
9.5%
3 13
8.8%
- 11
7.5%
5 10
6.8%
9 9
 
6.1%
7 6
 
4.1%
Other values (2) 10
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 23
15.6%
2 20
13.6%
, 16
10.9%
4 15
10.2%
8 14
9.5%
3 13
8.8%
- 11
7.5%
5 10
6.8%
9 9
 
6.1%
7 6
 
4.1%
Other values (2) 10
6.8%

비고
Categorical

Distinct2
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size428.0 B
손익계산서
19 
재무상태표 [요약]
18 

Length

Max length10
Median length5
Mean length7.4324324
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row재무상태표 [요약]
2nd row재무상태표 [요약]
3rd row재무상태표 [요약]
4th row재무상태표 [요약]
5th row재무상태표 [요약]

Common Values

ValueCountFrequency (%)
손익계산서 19
51.4%
재무상태표 [요약] 18
48.6%

Length

2023-12-11T13:51:14.684378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:51:14.836286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
손익계산서 19
34.5%
재무상태표 18
32.7%
요약 18
32.7%

Correlations

2023-12-11T13:51:14.964343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과목2021년2020년증감비고
과목1.0000.9430.9430.9431.000
2021년0.9431.0001.0001.0001.000
2020년0.9431.0001.0001.0001.000
증감0.9431.0001.0001.0001.000
비고1.0001.0001.0001.0001.000

Missing values

2023-12-11T13:51:11.356347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T13:51:11.465077image/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

과목2021년2020년증감비고
01.유동자산68,01476,456-8,442재무상태표 [요약]
1- 당좌자산34,31333,921392재무상태표 [요약]
2- 재고자산33,70142,535-8,834재무상태표 [요약]
32. 비유동자산203,467189,78313,684재무상태표 [요약]
4- 투자자산4,0433,903140재무상태표 [요약]
5- 유형자산186,411174,53011,881재무상태표 [요약]
6- 무형자산1404694재무상태표 [요약]
7- 기타비유동자산12,87311,3041,569재무상태표 [요약]
8자산합계271,481266,2395,242재무상태표 [요약]
91. 유동부채35,79652,967-17,171재무상태표 [요약]
과목2021년2020년증감비고
27- 기타사업582929손익계산서
283. 매출총이익2,7982,835-37손익계산서
29- 판매비와 관리비1,2071,379-172손익계산서
304. 영업이익1,5911,456135손익계산서
31- 영업외 수익571824-253손익계산서
32- 영업외 비용283499-216손익계산서
335. 경상이익1,8791,78198손익계산서
346. 세전순이익1,8791,78198손익계산서
35- 법인세비용48146714손익계산서
367. 당기순이익1,3981,31484손익계산서