Overview

Dataset statistics

Number of variables4
Number of observations510
Missing cells16
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.6 KiB
Average record size in memory33.3 B

Variable types

Text3
Numeric1

Dataset

Description중장기 개방 계획 관련 홈페이지 시스템 개방에 따른 부서 내역에 대한 내용으로 부서코드, 상위부서코드 .부서명 등 공개
Author(주)한국가스기술공사
URLhttps://www.data.go.kr/data/15068890/fileData.do

Alerts

상위부서코드 has 16 (3.1%) missing valuesMissing
부서코드 has unique valuesUnique

Reproduction

Analysis started2023-12-12 04:13:41.475687
Analysis finished2023-12-12 04:13:41.967259
Duration0.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

부서코드
Text

UNIQUE 

Distinct510
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
2023-12-12T13:13:42.270621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.9960784
Min length7

Characters and Unicode

Total characters4078
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique510 ?
Unique (%)100.0%

Sample

1st rowD0000003
2nd rowD0001739
3rd rowD0001453
4th rowD0001445
5th rowD0001742
ValueCountFrequency (%)
d0000003 1
 
0.2%
d0001187 1
 
0.2%
d0001396 1
 
0.2%
d0001196 1
 
0.2%
d0001530 1
 
0.2%
d0001195 1
 
0.2%
d0001194 1
 
0.2%
d0001193 1
 
0.2%
d0001015 1
 
0.2%
d0001487 1
 
0.2%
Other values (500) 500
98.0%
2023-12-12T13:13:42.775617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1830
44.9%
1 511
 
12.5%
D 507
 
12.4%
2 185
 
4.5%
4 177
 
4.3%
3 169
 
4.1%
9 158
 
3.9%
5 151
 
3.7%
7 134
 
3.3%
6 131
 
3.2%
Other values (3) 125
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3562
87.3%
Uppercase Letter 516
 
12.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1830
51.4%
1 511
 
14.3%
2 185
 
5.2%
4 177
 
5.0%
3 169
 
4.7%
9 158
 
4.4%
5 151
 
4.2%
7 134
 
3.8%
6 131
 
3.7%
8 116
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
D 507
98.3%
T 6
 
1.2%
F 3
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 3562
87.3%
Latin 516
 
12.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1830
51.4%
1 511
 
14.3%
2 185
 
5.2%
4 177
 
5.0%
3 169
 
4.7%
9 158
 
4.4%
5 151
 
4.2%
7 134
 
3.8%
6 131
 
3.7%
8 116
 
3.3%
Latin
ValueCountFrequency (%)
D 507
98.3%
T 6
 
1.2%
F 3
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1830
44.9%
1 511
 
12.5%
D 507
 
12.4%
2 185
 
4.5%
4 177
 
4.3%
3 169
 
4.1%
9 158
 
3.9%
5 151
 
3.7%
7 134
 
3.3%
6 131
 
3.2%
Other values (3) 125
 
3.1%

상위부서코드
Text

MISSING 

Distinct148
Distinct (%)30.0%
Missing16
Missing (%)3.1%
Memory size4.1 KiB
2023-12-12T13:13:43.176233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters3952
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)2.0%

Sample

1st rowD0000001
2nd rowD0000003
3rd rowD0001739
4th rowD0001739
5th rowD0001739
ValueCountFrequency (%)
d0001409 13
 
2.6%
d0000001 9
 
1.8%
d0000427 8
 
1.6%
d0000984 8
 
1.6%
d0000793 7
 
1.4%
d0000386 7
 
1.4%
d0000798 6
 
1.2%
d0000844 6
 
1.2%
d0000049 6
 
1.2%
d0000511 6
 
1.2%
Other values (138) 418
84.6%
2023-12-12T13:13:43.758940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2011
50.9%
D 494
 
12.5%
1 234
 
5.9%
9 183
 
4.6%
7 179
 
4.5%
4 172
 
4.4%
5 155
 
3.9%
8 148
 
3.7%
6 140
 
3.5%
3 129
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3458
87.5%
Uppercase Letter 494
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2011
58.2%
1 234
 
6.8%
9 183
 
5.3%
7 179
 
5.2%
4 172
 
5.0%
5 155
 
4.5%
8 148
 
4.3%
6 140
 
4.0%
3 129
 
3.7%
2 107
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
D 494
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3458
87.5%
Latin 494
 
12.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2011
58.2%
1 234
 
6.8%
9 183
 
5.3%
7 179
 
5.2%
4 172
 
5.0%
5 155
 
4.5%
8 148
 
4.3%
6 140
 
4.0%
3 129
 
3.7%
2 107
 
3.1%
Latin
ValueCountFrequency (%)
D 494
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2011
50.9%
D 494
 
12.5%
1 234
 
5.9%
9 183
 
4.6%
7 179
 
4.5%
4 172
 
4.4%
5 155
 
3.9%
8 148
 
3.7%
6 140
 
3.5%
3 129
 
3.3%

부서명
Text

Distinct248
Distinct (%)48.6%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
2023-12-12T13:13:44.186360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length5.127451
Min length2

Characters and Unicode

Total characters2615
Distinct characters227
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique189 ?
Unique (%)37.1%

Sample

1st row사장
2nd row인재경영실
3rd row정비기술교육팀
4th row비서파트
5th row인재육성부
ValueCountFrequency (%)
공무 27
 
5.1%
안전공무부 14
 
2.7%
지사부 14
 
2.7%
업무지원팀 11
 
2.1%
방식반 10
 
1.9%
기계파트 10
 
1.9%
기전부 10
 
1.9%
계전파트 10
 
1.9%
관로정비부 10
 
1.9%
정압기반 9
 
1.7%
Other values (244) 403
76.3%
2023-12-12T13:13:44.781139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
트 124
 
4.7%
부 120
 
4.6%
파 118
 
4.5%
기 117
 
4.5%
전 96
 
3.7%
계 96
 
3.7%
사 92
 
3.5%
반 88
 
3.4%
구 83
 
3.2%
간 76
 
2.9%
Other values (217) 1605
61.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2333
89.2%
Decimal Number 105
 
4.0%
Uppercase Letter 81
 
3.1%
Open Punctuation 33
 
1.3%
Close Punctuation 33
 
1.3%
Space Separator 18
 
0.7%
Other Punctuation 10
 
0.4%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
트 124
 
5.3%
부 120
 
5.1%
파 118
 
5.1%
기 117
 
5.0%
전 96
 
4.1%
계 96
 
4.1%
사 92
 
3.9%
반 88
 
3.8%
구 83
 
3.6%
간 76
 
3.3%
Other values (185) 1323
56.7%
Uppercase Letter
ValueCountFrequency (%)
T 20
24.7%
F 9
11.1%
G 8
 
9.9%
C 7
 
8.6%
L 6
 
7.4%
P 5
 
6.2%
M 5
 
6.2%
N 5
 
6.2%
I 5
 
6.2%
E 5
 
6.2%
Other values (3) 6
 
7.4%
Decimal Number
ValueCountFrequency (%)
1 28
26.7%
2 22
21.0%
3 9
 
8.6%
4 9
 
8.6%
7 8
 
7.6%
6 8
 
7.6%
5 8
 
7.6%
8 6
 
5.7%
9 5
 
4.8%
0 2
 
1.9%
Other Punctuation
ValueCountFrequency (%)
· 7
70.0%
& 2
 
20.0%
/ 1
 
10.0%
Open Punctuation
ValueCountFrequency (%)
( 31
93.9%
[ 2
 
6.1%
Close Punctuation
ValueCountFrequency (%)
) 31
93.9%
] 2
 
6.1%
Space Separator
ValueCountFrequency (%)
18
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2333
89.2%
Common 201
 
7.7%
Latin 81
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
트 124
 
5.3%
부 120
 
5.1%
파 118
 
5.1%
기 117
 
5.0%
전 96
 
4.1%
계 96
 
4.1%
사 92
 
3.9%
반 88
 
3.8%
구 83
 
3.6%
간 76
 
3.3%
Other values (185) 1323
56.7%
Common
ValueCountFrequency (%)
( 31
15.4%
) 31
15.4%
1 28
13.9%
2 22
10.9%
18
9.0%
3 9
 
4.5%
4 9
 
4.5%
7 8
 
4.0%
6 8
 
4.0%
5 8
 
4.0%
Other values (9) 29
14.4%
Latin
ValueCountFrequency (%)
T 20
24.7%
F 9
11.1%
G 8
 
9.9%
C 7
 
8.6%
L 6
 
7.4%
P 5
 
6.2%
M 5
 
6.2%
N 5
 
6.2%
I 5
 
6.2%
E 5
 
6.2%
Other values (3) 6
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2314
88.5%
ASCII 275
 
10.5%
Compat Jamo 19
 
0.7%
None 7
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
트 124
 
5.4%
부 120
 
5.2%
파 118
 
5.1%
기 117
 
5.1%
전 96
 
4.1%
계 96
 
4.1%
사 92
 
4.0%
반 88
 
3.8%
구 83
 
3.6%
간 76
 
3.3%
Other values (184) 1304
56.4%
ASCII
ValueCountFrequency (%)
( 31
 
11.3%
) 31
 
11.3%
1 28
 
10.2%
2 22
 
8.0%
T 20
 
7.3%
18
 
6.5%
F 9
 
3.3%
3 9
 
3.3%
4 9
 
3.3%
G 8
 
2.9%
Other values (21) 90
32.7%
Compat Jamo
ValueCountFrequency (%)
ㆍ 19
100.0%
None
ValueCountFrequency (%)
· 7
100.0%

정렬
Real number (ℝ)

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9019608
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-12-12T13:13:44.936682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0867103
Coefficient of variation (CV)0.18412699
Kurtosis1.002323
Mean5.9019608
Median Absolute Deviation (MAD)1
Skewness-0.87407837
Sum3010
Variance1.1809392
MonotonicityNot monotonic
2023-12-12T13:13:45.081134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 195
38.2%
5 140
27.5%
6 130
25.5%
4 36
 
7.1%
3 5
 
1.0%
2 2
 
0.4%
1 2
 
0.4%
ValueCountFrequency (%)
1 2
 
0.4%
2 2
 
0.4%
3 5
 
1.0%
4 36
 
7.1%
5 140
27.5%
6 130
25.5%
7 195
38.2%
ValueCountFrequency (%)
7 195
38.2%
6 130
25.5%
5 140
27.5%
4 36
 
7.1%
3 5
 
1.0%
2 2
 
0.4%
1 2
 
0.4%

Interactions

2023-12-12T13:13:41.694762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-12-12T13:13:41.838879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:13:41.932826image/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

부서코드상위부서코드부서명정렬
0D0000003D0000001사장2
1D0001739D0000003인재경영실3
2D0001453D0001739정비기술교육팀4
3D0001445D0001739비서파트4
4D0001742D0001739인재육성부4
5D0000004D0000001감사2
6D0001406D0000001경영전략본부3
7D0000925D0001406전략기획처4
8D0000926D0000925기획예산부5
9D0001523D0000926법무팀6
부서코드상위부서코드부서명정렬
500D0000983D0000793사천사업소5
501D0000812D0000983관로파트6
502D0001306D00008125구간7
503D0001307D00008126구간7
504D0001312D00008129구간(성포)7
505D0001308D0000812시설물관리7
506D0000813D0000983기전파트6
507D0001309D0000813기계반7
508D0001310D0000813계전반7
509D0001311D0000813공무7