Overview

Dataset statistics

Number of variables8
Number of observations99
Missing cells1
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 KiB
Average record size in memory67.3 B

Variable types

Numeric1
Categorical4
Text2
DateTime1

Dataset

Description인천광역시 서구 주민참여예산위원회 현황에 대한 데이터로 의원구분, 이름, 연령대, 성별, 소속단체또는 직업, 분과 등을 제공합니다.
Author인천광역시 서구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15090922&srcSe=7661IVAWM27C61E190

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 분과High correlation
분과 is highly overall correlated with 연번High correlation
의원구분 is highly imbalanced (89.7%)Imbalance
소속단체또는 직업 has 1 (1.0%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-18 02:00:57.421085
Analysis finished2024-03-18 02:00:59.598752
Duration2.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct99
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2024-03-18T11:00:59.677164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.9
Q125.5
median50
Q374.5
95-th percentile94.1
Maximum99
Range98
Interquartile range (IQR)49

Descriptive statistics

Standard deviation28.722813
Coefficient of variation (CV)0.57445626
Kurtosis-1.2
Mean50
Median Absolute Deviation (MAD)25
Skewness0
Sum4950
Variance825
MonotonicityStrictly increasing
2024-03-18T11:00:59.781621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
64 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
67 1
 
1.0%
Other values (89) 89
89.9%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%
90 1
1.0%

의원구분
Categorical

IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
위원
97 
위원장
 
1
부위원장
 
1

Length

Max length4
Median length2
Mean length2.030303
Min length2

Unique

Unique2 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
위원 97
98.0%
위원장 1
 
1.0%
부위원장 1
 
1.0%

Length

2024-03-18T11:00:59.887836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:00:59.967706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
위원 97
98.0%
위원장 1
 
1.0%
부위원장 1
 
1.0%

이름
Text

Distinct95
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2024-03-18T11:01:00.183997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.979798
Min length2

Characters and Unicode

Total characters295
Distinct characters98
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

Unique91 ?
Unique (%)91.9%

Sample

1st row김동우
2nd row김수옥
3rd row김장환
4th row김제웅
5th row박명복
ValueCountFrequency (%)
이태호 2
 
2.0%
이수민 2
 
2.0%
이준호 2
 
2.0%
임희진 2
 
2.0%
이주현 1
 
1.0%
조민정 1
 
1.0%
오춘인 1
 
1.0%
노미숙 1
 
1.0%
김유미 1
 
1.0%
김미영 1
 
1.0%
Other values (85) 85
85.9%
2024-03-18T11:01:00.515177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 20
 
6.8%
정 14
 
4.7%
김 13
 
4.4%
박 11
 
3.7%
호 9
 
3.1%
희 9
 
3.1%
현 8
 
2.7%
민 8
 
2.7%
미 7
 
2.4%
최 6
 
2.0%
Other values (88) 190
64.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 295
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 20
 
6.8%
정 14
 
4.7%
김 13
 
4.4%
박 11
 
3.7%
호 9
 
3.1%
희 9
 
3.1%
현 8
 
2.7%
민 8
 
2.7%
미 7
 
2.4%
최 6
 
2.0%
Other values (88) 190
64.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 295
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 20
 
6.8%
정 14
 
4.7%
김 13
 
4.4%
박 11
 
3.7%
호 9
 
3.1%
희 9
 
3.1%
현 8
 
2.7%
민 8
 
2.7%
미 7
 
2.4%
최 6
 
2.0%
Other values (88) 190
64.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 295
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 20
 
6.8%
정 14
 
4.7%
김 13
 
4.4%
박 11
 
3.7%
호 9
 
3.1%
희 9
 
3.1%
현 8
 
2.7%
민 8
 
2.7%
미 7
 
2.4%
최 6
 
2.0%
Other values (88) 190
64.4%

연령대
Categorical

Distinct5
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size924.0 B
50
38 
60
26 
40
23 
30
11 
70
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row30
2nd row50
3rd row70
4th row30
5th row60

Common Values

ValueCountFrequency (%)
50 38
38.4%
60 26
26.3%
40 23
23.2%
30 11
 
11.1%
70 1
 
1.0%

Length

2024-03-18T11:01:00.626651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:01:00.707714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50 38
38.4%
60 26
26.3%
40 23
23.2%
30 11
 
11.1%
70 1
 
1.0%

성별
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
여성
52 
남성
47 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남성
2nd row여성
3rd row남성
4th row남성
5th row남성

Common Values

ValueCountFrequency (%)
여성 52
52.5%
남성 47
47.5%

Length

2024-03-18T11:01:00.804556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:01:00.877475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여성 52
52.5%
남성 47
47.5%

소속단체또는 직업
Text

MISSING 

Distinct61
Distinct (%)62.2%
Missing1
Missing (%)1.0%
Memory size924.0 B
2024-03-18T11:01:01.025464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length6.1836735
Min length1

Characters and Unicode

Total characters606
Distinct characters169
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)50.0%

Sample

1st row-
2nd row주민자치회
3rd row아파트 입주자대표
4th row직장인
5th row자영업
ValueCountFrequency (%)
주민자치회 14
 
13.0%
자영업 10
 
9.3%
8
 
7.4%
청년참여단 3
 
2.8%
통장자율회 2
 
1.9%
서구 2
 
1.9%
주부 2
 
1.9%
민간지원관 2
 
1.9%
마을기업 2
 
1.9%
사)한국코딩드론메이커스협회 2
 
1.9%
Other values (57) 61
56.5%
2024-03-18T11:01:01.302059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
회 36
 
5.9%
자 29
 
4.8%
주 28
 
4.6%
사 19
 
3.1%
업 18
 
3.0%
민 16
 
2.6%
치 14
 
2.3%
인 14
 
2.3%
협 13
 
2.1%
) 13
 
2.1%
Other values (159) 406
67.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 559
92.2%
Close Punctuation 13
 
2.1%
Open Punctuation 13
 
2.1%
Space Separator 10
 
1.7%
Dash Punctuation 8
 
1.3%
Uppercase Letter 2
 
0.3%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
회 36
 
6.4%
자 29
 
5.2%
주 28
 
5.0%
사 19
 
3.4%
업 18
 
3.2%
민 16
 
2.9%
치 14
 
2.5%
인 14
 
2.5%
협 13
 
2.3%
서 13
 
2.3%
Other values (152) 359
64.2%
Uppercase Letter
ValueCountFrequency (%)
M 1
50.0%
F 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Decimal Number
ValueCountFrequency (%)
4 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 559
92.2%
Common 45
 
7.4%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
회 36
 
6.4%
자 29
 
5.2%
주 28
 
5.0%
사 19
 
3.4%
업 18
 
3.2%
민 16
 
2.9%
치 14
 
2.5%
인 14
 
2.5%
협 13
 
2.3%
서 13
 
2.3%
Other values (152) 359
64.2%
Common
ValueCountFrequency (%)
) 13
28.9%
( 13
28.9%
10
22.2%
- 8
17.8%
4 1
 
2.2%
Latin
ValueCountFrequency (%)
M 1
50.0%
F 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 559
92.2%
ASCII 47
 
7.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
회 36
 
6.4%
자 29
 
5.2%
주 28
 
5.0%
사 19
 
3.4%
업 18
 
3.2%
민 16
 
2.9%
치 14
 
2.5%
인 14
 
2.5%
협 13
 
2.3%
서 13
 
2.3%
Other values (152) 359
64.2%
ASCII
ValueCountFrequency (%)
) 13
27.7%
( 13
27.7%
10
21.3%
- 8
17.0%
4 1
 
2.1%
M 1
 
2.1%
F 1
 
2.1%

분과
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size924.0 B
기획소통
17 
도시관리
17 
복지문화
17 
경제교통
16 
자치행정
16 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기획소통 17
17.2%
도시관리 17
17.2%
복지문화 17
17.2%
경제교통 16
16.2%
자치행정 16
16.2%
환경안전 16
16.2%

Length

2024-03-18T11:01:01.404349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:01:01.516372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기획소통 17
17.2%
도시관리 17
17.2%
복지문화 17
17.2%
경제교통 16
16.2%
자치행정 16
16.2%
환경안전 16
16.2%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
Minimum2022-08-30 00:00:00
Maximum2022-08-30 00:00:00
2024-03-18T11:01:01.613467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:01.747685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-18T11:00:59.253919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T11:01:01.847086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번의원구분이름연령대성별소속단체또는 직업분과
연번1.0000.0000.9640.2400.4530.6210.949
의원구분0.0001.0001.0000.0000.0310.0000.054
이름0.9641.0001.0000.9441.0000.9930.938
연령대0.2400.0000.9441.0000.0800.8940.023
성별0.4530.0311.0000.0801.0000.6440.440
소속단체또는 직업0.6210.0000.9930.8940.6441.0000.426
분과0.9490.0540.9380.0230.4400.4261.000
2024-03-18T11:01:01.932545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
의원구분연령대성별분과
의원구분1.0000.0000.0490.000
연령대0.0001.0000.0940.000
성별0.0490.0941.0000.310
분과0.0000.0000.3101.000
2024-03-18T11:01:02.008235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번의원구분연령대성별분과
연번1.0000.0000.0990.3240.860
의원구분0.0001.0000.0000.0490.000
연령대0.0990.0001.0000.0940.000
성별0.3240.0490.0941.0000.310
분과0.8600.0000.0000.3101.000

Missing values

2024-03-18T11:00:59.379370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T11:00:59.532837image/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

연번의원구분이름연령대성별소속단체또는 직업분과데이터기준일자
01위원김동우30남성-경제교통2022-08-30
12위원김수옥50여성주민자치회경제교통2022-08-30
23위원김장환70남성아파트 입주자대표경제교통2022-08-30
34위원김제웅30남성직장인경제교통2022-08-30
45위원박명복60남성자영업경제교통2022-08-30
56위원박상휘40여성마을기업 파라서경제교통2022-08-30
67위원박성규60남성서구 식품제조업협회장경제교통2022-08-30
78위원박은우40남성주민자치회경제교통2022-08-30
89위원박찬희30남성청년참여단경제교통2022-08-30
910위원심옥빈50여성인천사회적기업협의회경제교통2022-08-30
연번의원구분이름연령대성별소속단체또는 직업분과데이터기준일자
8990위원손규일50남성한국표준협회환경안전2022-08-30
9091위원안금만60남성자영업환경안전2022-08-30
9192위원이재근60남성-환경안전2022-08-30
9293위원이준호40남성미소디자인환경안전2022-08-30
9394위원이태호60남성연희동 주민자치회환경안전2022-08-30
9495위원조규호60남성사회적협동조합 하늘샘배움터환경안전2022-08-30
9596위원탁순금60여성새마을부녀회환경안전2022-08-30
9697위원이준호40남성미소디자인환경안전2022-08-30
9798위원이태호60남성연희동 주민자치회환경안전2022-08-30
9899위원최원희50여성<NA>환경안전2022-08-30