Overview

Dataset statistics

Number of variables4
Number of observations71
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory33.9 B

Variable types

Categorical2
Text2

Dataset

Description행정중심복합도시건설청의 업무 목적인 행복도시 건설추진에 따른 개발계획 주요지표입니다. 이전기관청사, 지방행정청사 및 교통 등의 지표 현황입니다.
URLhttps://www.data.go.kr/data/15064056/fileData.do

Alerts

부문 is highly overall correlated with 비고High correlation
비고 is highly overall correlated with 부문High correlation

Reproduction

Analysis started2023-12-12 03:35:40.195036
Analysis finished2023-12-12 03:35:40.598005
Duration0.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

부문
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Memory size700.0 B
지방행정청사
17 
문화
10 
보건의료복지
9 
공원녹지
7 
교통
6 
Other values (7)
22 

Length

Max length8
Median length6
Mean length4.3098592
Min length2

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st row주거 및 생활권
2nd row주거 및 생활권
3rd row주거 및 생활권
4th row주거 및 생활권
5th row상업

Common Values

ValueCountFrequency (%)
지방행정청사 17
23.9%
문화 10
14.1%
보건의료복지 9
12.7%
공원녹지 7
9.9%
교통 6
 
8.5%
교육 6
 
8.5%
공급처리 5
 
7.0%
주거 및 생활권 4
 
5.6%
공업 2
 
2.8%
상하수도 2
 
2.8%
Other values (2) 3
 
4.2%

Length

2023-12-12T12:35:40.672068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지방행정청사 17
21.5%
문화 10
12.7%
보건의료복지 9
11.4%
공원녹지 7
8.9%
교통 6
 
7.6%
교육 6
 
7.6%
공급처리 5
 
6.3%
주거 4
 
5.1%
및 4
 
5.1%
생활권 4
 
5.1%
Other values (4) 7
8.9%

관련항목
Text

Distinct70
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size700.0 B
2023-12-12T12:35:40.988412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length11
Mean length7.2112676
Min length3

Characters and Unicode

Total characters512
Distinct characters144
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)97.2%

Sample

1st row주거/세대당 가구원수
2nd row주거/순밀도
3rd row기초생활권/규모
4th row기초생활권/개수
5th row상업·업무시설
ValueCountFrequency (%)
에너지 3
 
3.7%
집단 3
 
3.7%
하천변 2
 
2.4%
폭원 2
 
2.4%
녹지 2
 
2.4%
복합문화시설 1
 
1.2%
대학교 1
 
1.2%
특수학교 1
 
1.2%
고등학교 1
 
1.2%
중학교 1
 
1.2%
Other values (65) 65
79.3%
2023-12-12T12:35:41.601355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 28
 
5.5%
지 23
 
4.5%
설 22
 
4.3%
/ 19
 
3.7%
원 13
 
2.5%
공 12
 
2.3%
도 11
 
2.1%
11
 
2.1%
기 11
 
2.1%
관 11
 
2.1%
Other values (134) 351
68.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 470
91.8%
Other Punctuation 20
 
3.9%
Space Separator 11
 
2.1%
Decimal Number 5
 
1.0%
Open Punctuation 3
 
0.6%
Close Punctuation 3
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 28
 
6.0%
지 23
 
4.9%
설 22
 
4.7%
원 13
 
2.8%
공 12
 
2.6%
도 11
 
2.3%
기 11
 
2.3%
관 11
 
2.3%
합 10
 
2.1%
복 10
 
2.1%
Other values (125) 319
67.9%
Decimal Number
ValueCountFrequency (%)
1 2
40.0%
4 1
20.0%
6 1
20.0%
9 1
20.0%
Other Punctuation
ValueCountFrequency (%)
/ 19
95.0%
· 1
 
5.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 470
91.8%
Common 42
 
8.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 28
 
6.0%
지 23
 
4.9%
설 22
 
4.7%
원 13
 
2.8%
공 12
 
2.6%
도 11
 
2.3%
기 11
 
2.3%
관 11
 
2.3%
합 10
 
2.1%
복 10
 
2.1%
Other values (125) 319
67.9%
Common
ValueCountFrequency (%)
/ 19
45.2%
11
26.2%
( 3
 
7.1%
) 3
 
7.1%
1 2
 
4.8%
4 1
 
2.4%
6 1
 
2.4%
· 1
 
2.4%
9 1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 470
91.8%
ASCII 41
 
8.0%
None 1
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 28
 
6.0%
지 23
 
4.9%
설 22
 
4.7%
원 13
 
2.8%
공 12
 
2.6%
도 11
 
2.3%
기 11
 
2.3%
관 11
 
2.3%
합 10
 
2.1%
복 10
 
2.1%
Other values (125) 319
67.9%
ASCII
ValueCountFrequency (%)
/ 19
46.3%
11
26.8%
( 3
 
7.3%
) 3
 
7.3%
1 2
 
4.9%
4 1
 
2.4%
6 1
 
2.4%
9 1
 
2.4%
None
ValueCountFrequency (%)
· 1
100.0%

계획지표
Text

Distinct36
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Memory size700.0 B
2023-12-12T12:35:41.860008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length5.2957746
Min length3

Characters and Unicode

Total characters376
Distinct characters39
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)39.4%

Sample

1st row2.5인/세대
2nd row300인/ha 내외
3rd row2~3만인 내외
4th row22개소 내외
5th row예정지역 면적의 3% 내외
ValueCountFrequency (%)
내외 28
25.7%
1개소 20
18.3%
5개소 5
 
4.6%
6개소 5
 
4.6%
2개소 5
 
4.6%
20개소 4
 
3.7%
폭원 3
 
2.8%
4~5개소 3
 
2.8%
20개 2
 
1.8%
2~3개소 2
 
1.8%
Other values (30) 32
29.4%
2023-12-12T12:35:42.244704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
개 58
15.4%
소 52
13.8%
38
10.1%
1 30
 
8.0%
내 28
 
7.4%
외 28
 
7.4%
0 24
 
6.4%
2 21
 
5.6%
5 13
 
3.5%
4 9
 
2.4%
Other values (29) 75
19.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 198
52.7%
Decimal Number 111
29.5%
Space Separator 38
 
10.1%
Other Punctuation 10
 
2.7%
Math Symbol 8
 
2.1%
Lowercase Letter 7
 
1.9%
Other Symbol 4
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
개 58
29.3%
소 52
26.3%
내 28
14.1%
외 28
14.1%
상 4
 
2.0%
이 4
 
2.0%
원 3
 
1.5%
폭 3
 
1.5%
인 3
 
1.5%
만 3
 
1.5%
Other values (11) 12
 
6.1%
Decimal Number
ValueCountFrequency (%)
1 30
27.0%
0 24
21.6%
2 21
18.9%
5 13
11.7%
4 9
 
8.1%
3 7
 
6.3%
6 6
 
5.4%
7 1
 
0.9%
Other Punctuation
ValueCountFrequency (%)
/ 4
40.0%
% 3
30.0%
, 2
20.0%
. 1
 
10.0%
Lowercase Letter
ValueCountFrequency (%)
m 5
71.4%
a 1
 
14.3%
h 1
 
14.3%
Space Separator
ValueCountFrequency (%)
38
100.0%
Math Symbol
ValueCountFrequency (%)
~ 8
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 198
52.7%
Common 171
45.5%
Latin 7
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
개 58
29.3%
소 52
26.3%
내 28
14.1%
외 28
14.1%
상 4
 
2.0%
이 4
 
2.0%
원 3
 
1.5%
폭 3
 
1.5%
인 3
 
1.5%
만 3
 
1.5%
Other values (11) 12
 
6.1%
Common
ValueCountFrequency (%)
38
22.2%
1 30
17.5%
0 24
14.0%
2 21
12.3%
5 13
 
7.6%
4 9
 
5.3%
~ 8
 
4.7%
3 7
 
4.1%
6 6
 
3.5%
/ 4
 
2.3%
Other values (5) 11
 
6.4%
Latin
ValueCountFrequency (%)
m 5
71.4%
a 1
 
14.3%
h 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 198
52.7%
ASCII 174
46.3%
CJK Compat 4
 
1.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
개 58
29.3%
소 52
26.3%
내 28
14.1%
외 28
14.1%
상 4
 
2.0%
이 4
 
2.0%
원 3
 
1.5%
폭 3
 
1.5%
인 3
 
1.5%
만 3
 
1.5%
Other values (11) 12
 
6.1%
ASCII
ValueCountFrequency (%)
38
21.8%
1 30
17.2%
0 24
13.8%
2 21
12.1%
5 13
 
7.5%
4 9
 
5.2%
~ 8
 
4.6%
3 7
 
4.0%
6 6
 
3.4%
m 5
 
2.9%
Other values (7) 13
 
7.5%
CJK Compat
ValueCountFrequency (%)
㎡ 4
100.0%

비고
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Memory size700.0 B
도시생활권 단위
23 
<NA>
12 
지역생활권 단위
10 
기초생활권 단위
8 
기초생활권 단위(유치원 3, 초교2, 중·고 각 1)
4 
Other values (11)
14 

Length

Max length29
Median length8
Mean length9.084507
Min length3

Unique

Unique9 ?
Unique (%)12.7%

Sample

1st row평균치
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
도시생활권 단위 23
32.4%
<NA> 12
16.9%
지역생활권 단위 10
14.1%
기초생활권 단위 8
 
11.3%
기초생활권 단위(유치원 3, 초교2, 중·고 각 1) 4
 
5.6%
복합화 설치 3
 
4.2%
자전저도로 등 포함 2
 
2.8%
평균치 1
 
1.4%
하천 등 포함 1
 
1.4%
국가하천 1
 
1.4%
Other values (6) 6
 
8.5%

Length

2023-12-12T12:35:42.434793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
단위 41
26.3%
도시생활권 24
15.4%
na 12
 
7.7%
기초생활권 12
 
7.7%
지역생활권 11
 
7.1%
포함 7
 
4.5%
3 4
 
2.6%
중·고 4
 
2.6%
각 4
 
2.6%
1 4
 
2.6%
Other values (20) 33
21.2%

Correlations

2023-12-12T12:35:42.537843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부문관련항목계획지표비고
부문1.0001.0000.9750.934
관련항목1.0001.0000.0000.000
계획지표0.9750.0001.0000.983
비고0.9340.0000.9831.000
2023-12-12T12:35:42.653847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비고부문
비고1.0000.658
부문0.6581.000
2023-12-12T12:35:42.751074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부문비고
부문1.0000.658
비고0.6581.000

Missing values

2023-12-12T12:35:40.468704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:35:40.561428image/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

부문관련항목계획지표비고
0주거 및 생활권주거/세대당 가구원수2.5인/세대평균치
1주거 및 생활권주거/순밀도300인/ha 내외<NA>
2주거 및 생활권기초생활권/규모2~3만인 내외<NA>
3주거 및 생활권기초생활권/개수22개소 내외<NA>
4상업상업·업무시설예정지역 면적의 3% 내외<NA>
5공업첨단지식기반산업1개 단지도시생활권 단위
6공업도시형산업3개소 내외지역생활권 단위
7공원녹지공원녹지비율50% 이상하천 등 포함
8공원녹지근린공원10,000㎡ 이상/개소기초생활권 단위
9공원녹지어린이공원1,500㎡ 이상/개소기초생활권 단위
부문관련항목계획지표비고
61문화복합체육시설1개소도시생활권 단위(골프장·연습장, 테니스장 포함)
62보건의료복지아동복지시설20개소 내외기초생활권 단위
63보건의료복지노인복지시설20개소 내외기초생활권 단위
64보건의료복지노인보건시설4~5개소 내외지역생활권 단위
65보건의료복지가족복지시설4~5개소 내외지역생활권 단위
66보건의료복지장애인복지시설4~5개소 내외지역생활권 단위
67보건의료복지종합복지시설1개소도시생활권 단위
68보건의료복지종합장애인복지시설1개소도시생활권 단위
69보건의료복지종합가족복지시설1개소도시생활권 단위
70보건의료복지종합의료시설2~3개소도시생활권 단위