Overview

Dataset statistics

Number of variables7
Number of observations42
Missing cells52
Missing cells (%)17.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory61.1 B

Variable types

Categorical2
Text4
Numeric1

Dataset

Description관내 재건축 재개발 위치 및 진행 현황에 대한 데이터로 ( 기준연도, 구분, 구역번호, 구역명) 등의 데이터를 제공합니다.
URLhttps://www.data.go.kr/data/3072395/fileData.do

Alerts

기준연도 has constant value ""Constant
비고 has constant value ""Constant
세대수 has 12 (28.6%) missing valuesMissing
비고 has 40 (95.2%) missing valuesMissing
구역명 has unique valuesUnique
위치 has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:01:27.161753
Analysis finished2023-12-12 22:01:27.804103
Duration0.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size468.0 B
2023
42 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2023 42
100.0%

Length

2023-12-13T07:01:27.860086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:01:27.968977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 42
100.0%

구분
Categorical

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size468.0 B
재건축
22 
재개발
20 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
재건축 22
52.4%
재개발 20
47.6%

Length

2023-12-13T07:01:28.081043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:01:28.190620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재건축 22
52.4%
재개발 20
47.6%

구역번호
Text

Distinct40
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size468.0 B
2023-12-13T07:01:28.387709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters420
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)90.5%

Sample

1st row제 10-01 구역
2nd row제 10-05 구역
3rd row제 10-09 구역
4th row제 10-10 구역
5th row제 10-04 구역
ValueCountFrequency (%)
제 42
33.3%
구역 42
33.3%
00-50 2
 
1.6%
00-52 2
 
1.6%
10-37 1
 
0.8%
10-45 1
 
0.8%
00-46 1
 
0.8%
00-47 1
 
0.8%
10-28 1
 
0.8%
10-29 1
 
0.8%
Other values (32) 32
25.4%
2023-12-13T07:01:28.740151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
84
20.0%
0 63
15.0%
제 42
10.0%
- 42
10.0%
구 42
10.0%
역 42
10.0%
1 39
9.3%
2 14
 
3.3%
4 14
 
3.3%
5 12
 
2.9%
Other values (5) 26
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 168
40.0%
Other Letter 126
30.0%
Space Separator 84
20.0%
Dash Punctuation 42
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 63
37.5%
1 39
23.2%
2 14
 
8.3%
4 14
 
8.3%
5 12
 
7.1%
3 11
 
6.5%
9 5
 
3.0%
6 5
 
3.0%
7 3
 
1.8%
8 2
 
1.2%
Other Letter
ValueCountFrequency (%)
제 42
33.3%
구 42
33.3%
역 42
33.3%
Space Separator
ValueCountFrequency (%)
84
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 294
70.0%
Hangul 126
30.0%

Most frequent character per script

Common
ValueCountFrequency (%)
84
28.6%
0 63
21.4%
- 42
14.3%
1 39
13.3%
2 14
 
4.8%
4 14
 
4.8%
5 12
 
4.1%
3 11
 
3.7%
9 5
 
1.7%
6 5
 
1.7%
Other values (2) 5
 
1.7%
Hangul
ValueCountFrequency (%)
제 42
33.3%
구 42
33.3%
역 42
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 294
70.0%
Hangul 126
30.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
84
28.6%
0 63
21.4%
- 42
14.3%
1 39
13.3%
2 14
 
4.8%
4 14
 
4.8%
5 12
 
4.1%
3 11
 
3.7%
9 5
 
1.7%
6 5
 
1.7%
Other values (2) 5
 
1.7%
Hangul
ValueCountFrequency (%)
제 42
33.3%
구 42
33.3%
역 42
33.3%

구역명
Text

UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size468.0 B
2023-12-13T07:01:29.263468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length16.261905
Min length11

Characters and Unicode

Total characters683
Distinct characters89
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)100.0%

Sample

1st row대명3동 뉴타운주택 재개발정비사업
2nd row대명2동 명덕지구주택 재개발정비사업
3rd row문화지구주택 재개발정비사업
4th row봉덕1동구역 주택 재개발정비사업
5th row상록주택 재개발정비사업
ValueCountFrequency (%)
재건축정비사업 22
22.0%
재개발정비사업 20
20.0%
봉덕2동 3
 
3.0%
주택 2
 
2.0%
봉덕1동 2
 
2.0%
봉덕동 2
 
2.0%
대명10동 2
 
2.0%
앵두주택 1
 
1.0%
파크아파트주택 1
 
1.0%
대명1동 1
 
1.0%
Other values (44) 44
44.0%
2023-12-13T07:01:29.629891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
97
 
14.2%
정 43
 
6.3%
업 42
 
6.1%
재 42
 
6.1%
비 42
 
6.1%
사 42
 
6.1%
주 34
 
5.0%
택 33
 
4.8%
건 22
 
3.2%
축 22
 
3.2%
Other values (79) 264
38.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 555
81.3%
Space Separator 97
 
14.2%
Decimal Number 29
 
4.2%
Uppercase Letter 1
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 43
 
7.7%
업 42
 
7.6%
재 42
 
7.6%
비 42
 
7.6%
사 42
 
7.6%
주 34
 
6.1%
택 33
 
5.9%
건 22
 
4.0%
축 22
 
4.0%
동 21
 
3.8%
Other values (69) 212
38.2%
Decimal Number
ValueCountFrequency (%)
2 7
24.1%
1 6
20.7%
3 5
17.2%
4 5
17.2%
0 3
10.3%
6 2
 
6.9%
5 1
 
3.4%
Space Separator
ValueCountFrequency (%)
97
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 555
81.3%
Common 127
 
18.6%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 43
 
7.7%
업 42
 
7.6%
재 42
 
7.6%
비 42
 
7.6%
사 42
 
7.6%
주 34
 
6.1%
택 33
 
5.9%
건 22
 
4.0%
축 22
 
4.0%
동 21
 
3.8%
Other values (69) 212
38.2%
Common
ValueCountFrequency (%)
97
76.4%
2 7
 
5.5%
1 6
 
4.7%
3 5
 
3.9%
4 5
 
3.9%
0 3
 
2.4%
6 2
 
1.6%
5 1
 
0.8%
- 1
 
0.8%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 555
81.3%
ASCII 128
 
18.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
97
75.8%
2 7
 
5.5%
1 6
 
4.7%
3 5
 
3.9%
4 5
 
3.9%
0 3
 
2.3%
6 2
 
1.6%
A 1
 
0.8%
5 1
 
0.8%
- 1
 
0.8%
Hangul
ValueCountFrequency (%)
정 43
 
7.7%
업 42
 
7.6%
재 42
 
7.6%
비 42
 
7.6%
사 42
 
7.6%
주 34
 
6.1%
택 33
 
5.9%
건 22
 
4.0%
축 22
 
4.0%
동 21
 
3.8%
Other values (69) 212
38.2%

위치
Text

UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size468.0 B
2023-12-13T07:01:29.877486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length47
Mean length42.97619
Min length24

Characters and Unicode

Total characters1805
Distinct characters126
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)100.0%

Sample

1st row대구광역시 남구 대명3동 2301-2번지(명덕로12길 116) 일원(구 대구대학교 남편)
2nd row대구광역시 남구 대명2동 2017-2번지(중앙대로52길 48) 일원(남구 보건소 동편)
3rd row대구광역시 남구 이천동 474-1번지(이천로 105-5) 일원(월드메르디앙 동편)
4th row대구광역시 남구 봉덕1동 512-8번지(봉덕로9길 89-194) 일원(캠프조지 동편)
5th row대구광역시 남구 대명2동 1959-27번지(중앙대로35길 22-6) 일원(대구교육대학 남편)
ValueCountFrequency (%)
대구광역시 42
 
14.3%
남구 42
 
14.3%
봉덕2동 12
 
4.1%
외 10
 
3.4%
남편 6
 
2.0%
대명10동 5
 
1.7%
이천동 5
 
1.7%
인근 5
 
1.7%
동편 5
 
1.7%
4
 
1.4%
Other values (132) 157
53.6%
2023-12-13T07:01:30.283578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
270
 
15.0%
1 100
 
5.5%
대 89
 
4.9%
구 89
 
4.9%
2 68
 
3.8%
) 63
 
3.5%
( 63
 
3.5%
지 53
 
2.9%
남 52
 
2.9%
동 49
 
2.7%
Other values (116) 909
50.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 963
53.4%
Decimal Number 398
22.0%
Space Separator 270
 
15.0%
Close Punctuation 63
 
3.5%
Open Punctuation 63
 
3.5%
Dash Punctuation 47
 
2.6%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 89
 
9.2%
구 89
 
9.2%
지 53
 
5.5%
남 52
 
5.4%
동 49
 
5.1%
시 44
 
4.6%
역 42
 
4.4%
광 42
 
4.4%
번 42
 
4.4%
원 33
 
3.4%
Other values (101) 428
44.4%
Decimal Number
ValueCountFrequency (%)
1 100
25.1%
2 68
17.1%
0 37
 
9.3%
3 37
 
9.3%
5 36
 
9.0%
6 27
 
6.8%
4 27
 
6.8%
9 24
 
6.0%
7 22
 
5.5%
8 20
 
5.0%
Space Separator
ValueCountFrequency (%)
270
100.0%
Close Punctuation
ValueCountFrequency (%)
) 63
100.0%
Open Punctuation
ValueCountFrequency (%)
( 63
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47
100.0%
Math Symbol
ValueCountFrequency (%)
～ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 963
53.4%
Common 842
46.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 89
 
9.2%
구 89
 
9.2%
지 53
 
5.5%
남 52
 
5.4%
동 49
 
5.1%
시 44
 
4.6%
역 42
 
4.4%
광 42
 
4.4%
번 42
 
4.4%
원 33
 
3.4%
Other values (101) 428
44.4%
Common
ValueCountFrequency (%)
270
32.1%
1 100
 
11.9%
2 68
 
8.1%
) 63
 
7.5%
( 63
 
7.5%
- 47
 
5.6%
0 37
 
4.4%
3 37
 
4.4%
5 36
 
4.3%
6 27
 
3.2%
Other values (5) 94
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 963
53.4%
ASCII 841
46.6%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
270
32.1%
1 100
 
11.9%
2 68
 
8.1%
) 63
 
7.5%
( 63
 
7.5%
- 47
 
5.6%
0 37
 
4.4%
3 37
 
4.4%
5 36
 
4.3%
6 27
 
3.2%
Other values (4) 93
 
11.1%
Hangul
ValueCountFrequency (%)
대 89
 
9.2%
구 89
 
9.2%
지 53
 
5.5%
남 52
 
5.4%
동 49
 
5.1%
시 44
 
4.6%
역 42
 
4.4%
광 42
 
4.4%
번 42
 
4.4%
원 33
 
3.4%
Other values (101) 428
44.4%
None
ValueCountFrequency (%)
～ 1
100.0%

세대수
Real number (ℝ)

MISSING 

Distinct29
Distinct (%)96.7%
Missing12
Missing (%)28.6%
Infinite0
Infinite (%)0.0%
Mean803.53333
Minimum186
Maximum3051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-13T07:01:30.425194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum186
5-th percentile296.8
Q1426.25
median579.5
Q3959
95-th percentile1903.75
Maximum3051
Range2865
Interquartile range (IQR)532.75

Descriptive statistics

Standard deviation624.12425
Coefficient of variation (CV)0.77672478
Kurtosis4.9527391
Mean803.53333
Median Absolute Deviation (MAD)238.5
Skewness2.0642527
Sum24106
Variance389531.09
MonotonicityNot monotonic
2023-12-13T07:01:30.584632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
433 2
 
4.8%
863 1
 
2.4%
843 1
 
2.4%
1051 1
 
2.4%
268 1
 
2.4%
425 1
 
2.4%
345 1
 
2.4%
332 1
 
2.4%
430 1
 
2.4%
337 1
 
2.4%
Other values (19) 19
45.2%
(Missing) 12
28.6%
ValueCountFrequency (%)
186 1
2.4%
268 1
2.4%
332 1
2.4%
337 1
2.4%
345 1
2.4%
361 1
2.4%
412 1
2.4%
425 1
2.4%
430 1
2.4%
431 1
2.4%
ValueCountFrequency (%)
3051 1
2.4%
2023 1
2.4%
1758 1
2.4%
1631 1
2.4%
1308 1
2.4%
1091 1
2.4%
1051 1
2.4%
975 1
2.4%
911 1
2.4%
863 1
2.4%

비고
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing40
Missing (%)95.2%
Memory size468.0 B
2023-12-13T07:01:30.718295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters6
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

Unique0 ?
Unique (%)0.0%

Sample

1st row정비구역해제
2nd row정비구역해제
ValueCountFrequency (%)
정비구역해제 2
100.0%
2023-12-13T07:01:31.005783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
정 2
16.7%
비 2
16.7%
구 2
16.7%
역 2
16.7%
해 2
16.7%
제 2
16.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 2
16.7%
비 2
16.7%
구 2
16.7%
역 2
16.7%
해 2
16.7%
제 2
16.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 2
16.7%
비 2
16.7%
구 2
16.7%
역 2
16.7%
해 2
16.7%
제 2
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
정 2
16.7%
비 2
16.7%
구 2
16.7%
역 2
16.7%
해 2
16.7%
제 2
16.7%

Interactions

2023-12-13T07:01:27.456852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:01:31.097349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분구역번호구역명위치세대수
구분1.0001.0001.0001.0000.324
구역번호1.0001.0001.0001.0001.000
구역명1.0001.0001.0001.0001.000
위치1.0001.0001.0001.0001.000
세대수0.3241.0001.0001.0001.000
2023-12-13T07:01:31.208757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수구분
세대수1.0000.337
구분0.3371.000

Missing values

2023-12-13T07:01:27.569805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:01:27.676195image/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.
2023-12-13T07:01:27.760413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

기준연도구분구역번호구역명위치세대수비고
02023재개발제 10-01 구역대명3동 뉴타운주택 재개발정비사업대구광역시 남구 대명3동 2301-2번지(명덕로12길 116) 일원(구 대구대학교 남편)2023<NA>
12023재개발제 10-05 구역대명2동 명덕지구주택 재개발정비사업대구광역시 남구 대명2동 2017-2번지(중앙대로52길 48) 일원(남구 보건소 동편)1758<NA>
22023재개발제 10-09 구역문화지구주택 재개발정비사업대구광역시 남구 이천동 474-1번지(이천로 105-5) 일원(월드메르디앙 동편)911<NA>
32023재개발제 10-10 구역봉덕1동구역 주택 재개발정비사업대구광역시 남구 봉덕1동 512-8번지(봉덕로9길 89-194) 일원(캠프조지 동편)621<NA>
42023재개발제 10-04 구역상록주택 재개발정비사업대구광역시 남구 대명2동 1959-27번지(중앙대로35길 22-6) 일원(대구교육대학 남편)975<NA>
52023재개발제 10-15 구역배나무골 주택 재개발정비사업대구광역시 남구 이천동 281-1번지(대봉로 146) 일원(대봉초등학교 서편)433<NA>
62023재개발제 10-17 구역봉덕1동 우리주택 재개발정비사업대구광역시 남구 봉덕1동 976-1번지 일원(봉덕시장 인근)1091<NA>
72023재개발제 10-19 구역서봉덕주택 재개발정비사업대구광역시 남구 봉덕2동 540-1번지(이천로10길 50) 일원(봉덕2동주민센터 인근)538<NA>
82023재개발제 10-22 구역용두지구주택 재개발정비사업대구광역시 남구 봉덕2동 916-10번지(용두방천4길 42-7) 일원(중동교 남편)622<NA>
92023재개발제 10-23 구역봉덕대덕주택 재개발정비사업대구광역시 남구 봉덕2동 1028-1번지(대덕로38길 19) 일원(현대홈타운 동편)800<NA>
기준연도구분구역번호구역명위치세대수비고
322023재건축제 00-50 구역봉덕1동 태양주택 재건축정비사업대구광역시 남구 봉덕1동 841-5번지 외 172필지337<NA>
332023재건축제 00-51 구역강변아파트 재건축정비사업대구광역시 남구 봉덕2동 873-19번지 외 196필지 - 코오롱 하늘채430<NA>
342023재건축제 00-52 구역봉덕2동 가변지구 재건축정비사업대구광역시 남구 봉덕2동 865-14번지(봉덕로28길 29) 외 127필지332<NA>
352023재건축제 00-50 구역봉덕동 새길지구주택 재건축정비사업대구광역시 남구 봉덕2동 1067-35번지(대덕로34길 10-10) 외 135필지345<NA>
362023재건축제 00-54 구역봉덕2동 봉림주택 재건축정비사업대구광역시 남구 봉덕2동 1019-90번지 외 170필지 - 현대 홈타운425<NA>
372023재건축제 00-56 구역대명2동주택 재건축정비사업대구광역시 남구 대명2동 2014-160번지( 중앙대로42길 52-6) 외 157필지268<NA>
382023재건축제 10-45 구역대명6동골안지구주택 재건축정비사업대구광역시 남구 대명6동 1400번지( 대명로14길 30) 일원(코스모스아파트 남편)1051<NA>
392023재건축제 10-37 구역정우맨션 재건축정비사업대구광역시 남구 대명1동 629-61번지 일원<NA>정비구역해제
402023재건축제 10-20 구역남봉덕 재건축정비사업대구광역시 남구 봉덕2동 532-1번지 일원843<NA>
412023재건축제 20-46 구역미리내 재건축정비사업대구광역시 남구 봉덕3동 산 89-3번지 일원<NA><NA>