Overview

Dataset statistics

Number of variables6
Number of observations306
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.8 KiB
Average record size in memory49.4 B

Variable types

Numeric1
Categorical2
Text3

Dataset

Description2023년도 기준 중앙행정기관 및 지자체(광역)의 성별영향평가책임관 지정현황 에 대한 정보입니다.(매년 1분기 데이터 생성)
URLhttps://www.data.go.kr/data/15049344/fileData.do

Alerts

연번 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
직급 is highly overall correlated with 구분High correlation
연번 has unique valuesUnique
기관명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 00:16:52.753637
Analysis finished2023-12-12 00:16:53.332393
Duration0.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct306
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.5
Minimum1
Maximum306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-12T09:16:53.412682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16.25
Q177.25
median153.5
Q3229.75
95-th percentile290.75
Maximum306
Range305
Interquartile range (IQR)152.5

Descriptive statistics

Standard deviation88.478811
Coefficient of variation (CV)0.57640919
Kurtosis-1.2
Mean153.5
Median Absolute Deviation (MAD)76.5
Skewness0
Sum46971
Variance7828.5
MonotonicityStrictly increasing
2023-12-12T09:16:53.553833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
203 1
 
0.3%
210 1
 
0.3%
209 1
 
0.3%
208 1
 
0.3%
207 1
 
0.3%
206 1
 
0.3%
205 1
 
0.3%
204 1
 
0.3%
202 1
 
0.3%
Other values (296) 296
96.7%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
306 1
0.3%
305 1
0.3%
304 1
0.3%
303 1
0.3%
302 1
0.3%
301 1
0.3%
300 1
0.3%
299 1
0.3%
298 1
0.3%
297 1
0.3%

구분
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
시군구
226 
중앙행정기관
46 
시도
 
17
시도 교육청
 
17

Length

Max length6
Median length3
Mean length3.5620915
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
시군구 226
73.9%
중앙행정기관 46
 
15.0%
시도 17
 
5.6%
시도 교육청 17
 
5.6%

Length

2023-12-12T09:16:53.720236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:16:53.826369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시군구 226
70.0%
중앙행정기관 46
 
14.2%
시도 34
 
10.5%
교육청 17
 
5.3%

기관명
Text

UNIQUE 

Distinct306
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2023-12-12T09:16:54.151891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length7.3627451
Min length3

Characters and Unicode

Total characters2253
Distinct characters188
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique306 ?
Unique (%)100.0%

Sample

1st row기획재정부
2nd row교육부
3rd row과학기술정보통신부
4th row외교부
5th row통일부
ValueCountFrequency (%)
경기도 32
 
6.0%
서울특별시 26
 
4.9%
경상북도 24
 
4.5%
전라남도 23
 
4.3%
경상남도 19
 
3.6%
강원도 19
 
3.6%
부산광역시 17
 
3.2%
충청남도 16
 
3.0%
전라북도 15
 
2.8%
충청북도 12
 
2.3%
Other values (274) 329
61.8%
2023-12-12T09:16:54.620755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
228
 
10.1%
도 176
 
7.8%
시 167
 
7.4%
군 85
 
3.8%
경 84
 
3.7%
구 84
 
3.7%
광 75
 
3.3%
남 73
 
3.2%
청 70
 
3.1%
역 61
 
2.7%
Other values (178) 1150
51.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2025
89.9%
Space Separator 228
 
10.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 176
 
8.7%
시 167
 
8.2%
군 85
 
4.2%
경 84
 
4.1%
구 84
 
4.1%
광 75
 
3.7%
남 73
 
3.6%
청 70
 
3.5%
역 61
 
3.0%
북 60
 
3.0%
Other values (177) 1090
53.8%
Space Separator
ValueCountFrequency (%)
228
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2025
89.9%
Common 228
 
10.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 176
 
8.7%
시 167
 
8.2%
군 85
 
4.2%
경 84
 
4.1%
구 84
 
4.1%
광 75
 
3.7%
남 73
 
3.6%
청 70
 
3.5%
역 61
 
3.0%
북 60
 
3.0%
Other values (177) 1090
53.8%
Common
ValueCountFrequency (%)
228
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2025
89.9%
ASCII 228
 
10.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
228
100.0%
Hangul
ValueCountFrequency (%)
도 176
 
8.7%
시 167
 
8.2%
군 85
 
4.2%
경 84
 
4.1%
구 84
 
4.1%
광 75
 
3.7%
남 73
 
3.6%
청 70
 
3.5%
역 61
 
3.0%
북 60
 
3.0%
Other values (177) 1090
53.8%

직위
Text

Distinct108
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2023-12-12T09:16:54.873394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length6
Mean length5.8300654
Min length4

Characters and Unicode

Total characters1784
Distinct characters88
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique75 ?
Unique (%)24.5%

Sample

1st row정책기획관
2nd row정책기획관
3rd row정책기획관
4th row조정기획관
5th row운영지원과장
ValueCountFrequency (%)
행정복지국장 30
 
9.7%
기획조정관 22
 
7.1%
복지국장 21
 
6.8%
복지환경국장 17
 
5.5%
복지교육국장 14
 
4.5%
복지문화국장 13
 
4.2%
교육국장 9
 
2.9%
가족행복과장 9
 
2.9%
정책기획관 9
 
2.9%
문화복지국장 9
 
2.9%
Other values (98) 155
50.3%
2023-12-12T09:16:55.243792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 261
14.6%
국 208
 
11.7%
복 199
 
11.2%
지 192
 
10.8%
정 99
 
5.5%
행 60
 
3.4%
과 48
 
2.7%
관 45
 
2.5%
기 42
 
2.4%
획 41
 
2.3%
Other values (78) 589
33.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1781
99.8%
Space Separator 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 261
14.7%
국 208
 
11.7%
복 199
 
11.2%
지 192
 
10.8%
정 99
 
5.6%
행 60
 
3.4%
과 48
 
2.7%
관 45
 
2.5%
기 42
 
2.4%
획 41
 
2.3%
Other values (77) 586
32.9%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1781
99.8%
Common 3
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 261
14.7%
국 208
 
11.7%
복 199
 
11.2%
지 192
 
10.8%
정 99
 
5.6%
행 60
 
3.4%
과 48
 
2.7%
관 45
 
2.5%
기 42
 
2.4%
획 41
 
2.3%
Other values (77) 586
32.9%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1781
99.8%
ASCII 3
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 261
14.7%
국 208
 
11.7%
복 199
 
11.2%
지 192
 
10.8%
정 99
 
5.6%
행 60
 
3.4%
과 48
 
2.7%
관 45
 
2.5%
기 42
 
2.4%
획 41
 
2.3%
Other values (77) 586
32.9%
ASCII
ValueCountFrequency (%)
3
100.0%

직급
Categorical

HIGH CORRELATION 

Distinct36
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
지방서기관
131 
지방행정서기관
35 
지방행정사무관
28 
일반직고위공무원
18 
지방사회복지사무관
16 
Other values (31)
78 

Length

Max length12
Median length10
Mean length5.9444444
Min length2

Unique

Unique22 ?
Unique (%)7.2%

Sample

1st row부이사관
2nd row일반직고위공무원
3rd row일반직고위공무원
4th row고위공무원
5th row서기관

Common Values

ValueCountFrequency (%)
지방서기관 131
42.8%
지방행정서기관 35
 
11.4%
지방행정사무관 28
 
9.2%
일반직고위공무원 18
 
5.9%
지방사회복지사무관 16
 
5.2%
지방부이사관 15
 
4.9%
장학관 13
 
4.2%
고위공무원 12
 
3.9%
일반직 고위공무원 5
 
1.6%
지방기술서기관 3
 
1.0%
Other values (26) 30
 
9.8%

Length

2023-12-12T09:16:55.383333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지방서기관 133
42.8%
지방행정서기관 35
 
11.3%
지방행정사무관 29
 
9.3%
일반직고위공무원 18
 
5.8%
고위공무원 17
 
5.5%
지방사회복지사무관 16
 
5.1%
지방부이사관 15
 
4.8%
장학관 13
 
4.2%
일반직 5
 
1.6%
지방기술서기관 3
 
1.0%
Other values (24) 27
 
8.7%

성명
Text

Distinct302
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2023-12-12T09:16:55.696419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.996732
Min length2

Characters and Unicode

Total characters917
Distinct characters146
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique298 ?
Unique (%)97.4%

Sample

1st row강기룡
2nd row김정연
3rd row송재성
4th row황소진
5th row마경조
ValueCountFrequency (%)
김동환 2
 
0.7%
김복수 2
 
0.7%
이은영 2
 
0.7%
김태진 2
 
0.7%
이강학 1
 
0.3%
박승보 1
 
0.3%
전필호 1
 
0.3%
이경학 1
 
0.3%
홍성갑 1
 
0.3%
김주숙 1
 
0.3%
Other values (292) 292
95.4%
2023-12-12T09:16:56.366579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 62
 
6.8%
이 56
 
6.1%
영 34
 
3.7%
정 31
 
3.4%
희 26
 
2.8%
박 24
 
2.6%
경 23
 
2.5%
종 17
 
1.9%
최 16
 
1.7%
호 15
 
1.6%
Other values (136) 613
66.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 916
99.9%
Space Separator 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 62
 
6.8%
이 56
 
6.1%
영 34
 
3.7%
정 31
 
3.4%
희 26
 
2.8%
박 24
 
2.6%
경 23
 
2.5%
종 17
 
1.9%
최 16
 
1.7%
호 15
 
1.6%
Other values (135) 612
66.8%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 916
99.9%
Common 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 62
 
6.8%
이 56
 
6.1%
영 34
 
3.7%
정 31
 
3.4%
희 26
 
2.8%
박 24
 
2.6%
경 23
 
2.5%
종 17
 
1.9%
최 16
 
1.7%
호 15
 
1.6%
Other values (135) 612
66.8%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 916
99.9%
ASCII 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 62
 
6.8%
이 56
 
6.1%
영 34
 
3.7%
정 31
 
3.4%
희 26
 
2.8%
박 24
 
2.6%
경 23
 
2.5%
종 17
 
1.9%
최 16
 
1.7%
호 15
 
1.6%
Other values (135) 612
66.8%
ASCII
ValueCountFrequency (%)
1
100.0%

Interactions

2023-12-12T09:16:53.078034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:16:56.501995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분직급
연번1.0000.8960.843
구분0.8961.0000.990
직급0.8430.9901.000
2023-12-12T09:16:56.592908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분직급
구분1.0000.834
직급0.8341.000
2023-12-12T09:16:56.684186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분직급
연번1.0000.7760.468
구분0.7761.0000.834
직급0.4680.8341.000

Missing values

2023-12-12T09:16:53.192494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:16:53.291441image/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

연번구분기관명직위직급성명
01중앙행정기관기획재정부정책기획관부이사관강기룡
12중앙행정기관교육부정책기획관일반직고위공무원김정연
23중앙행정기관과학기술정보통신부정책기획관일반직고위공무원송재성
34중앙행정기관외교부조정기획관고위공무원황소진
45중앙행정기관통일부운영지원과장서기관마경조
56중앙행정기관법무부기획조정실장검사장권순정
67중앙행정기관국방부보건복지관일반직고위공무원박길성
78중앙행정기관행정안전부정책기획관일반직 고위공무원정영준
89중앙행정기관문화체육관광부정책기획관일반직고위공무원최성희
910중앙행정기관농림축산식품부농촌정책국장일반직고위공무원이상만
연번구분기관명직위직급성명
296297시도 교육청경기도교육청교육과정국장장학관전성화
297298시도 교육청강원도교육청교육국장장학관김은숙
298299시도 교육청충청북도교육청기획국장지방부이사관주병호
299300시도 교육청충청남도교육청교육국장장학관이병도
300301시도 교육청전라북도교육청행정국장지방부이사관김형대
301302시도 교육청전라남도교육청교육국장장학관백도현
302303시도 교육청경상북도교육청교육국장장학관권영근
303304시도 교육청경상남도교육청미래교육국장장학관강신영
304305시도 교육청제주특별자치도교육청교육국장교육국장고경수
305306중앙행정기관질병관리청기획조정관고위공무원양동교