Overview

Dataset statistics

Number of variables10
Number of observations80
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.8 KiB
Average record size in memory86.7 B

Variable types

Categorical3
Text2
Numeric5

Dataset

Description인천광역시 추정분담금정보시스템에 등록된 재개발재건축조합의 사업개요(구별, 구역명, 위치,면적, 추진단계, 동수, 세대수, 건폐율, 용적율)에 대한 데이터
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15080866&srcSe=7661IVAWM27C61E190

Alerts

면적(제곱미터) is highly overall correlated with 동수 and 1 other fieldsHigh correlation
동수 is highly overall correlated with 면적(제곱미터) and 1 other fieldsHigh correlation
세대수 is highly overall correlated with 면적(제곱미터) and 1 other fieldsHigh correlation
구역명 has unique valuesUnique
면적(제곱미터) has unique valuesUnique
동수 has 8 (10.0%) zerosZeros
건폐율(퍼센트) has 2 (2.5%) zerosZeros
용적률(퍼센트) has 2 (2.5%) zerosZeros

Reproduction

Analysis started2024-04-14 03:14:05.487549
Analysis finished2024-04-14 03:14:07.663998
Duration2.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구별
Categorical

Distinct8
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
부평구
32 
미추홀구
17 
동구
11 
계양구
6 
중구
5 
Other values (3)
9 

Length

Max length4
Median length3
Mean length2.975
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부평구 32
40.0%
미추홀구 17
21.2%
동구 11
 
13.8%
계양구 6
 
7.5%
중구 5
 
6.2%
남동구 4
 
5.0%
서구 3
 
3.8%
연수구 2
 
2.5%

Length

2024-04-14T12:14:07.716564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T12:14:08.011074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부평구 32
40.0%
미추홀구 17
21.2%
동구 11
 
13.8%
계양구 6
 
7.5%
중구 5
 
6.2%
남동구 4
 
5.0%
서구 3
 
3.8%
연수구 2
 
2.5%

구역명
Text

UNIQUE 

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
2024-04-14T12:14:08.218369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length4.1125
Min length2

Characters and Unicode

Total characters329
Distinct characters110
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

Unique80 ?
Unique (%)100.0%

Sample

1st row경동율목
2nd row송월
3rd row송월아파트
4th row경동
5th row인천여상주변
ValueCountFrequency (%)
경동율목 1
 
1.2%
송월 1
 
1.2%
삼산1 1
 
1.2%
산곡 1
 
1.2%
부평아파트 1
 
1.2%
청천2 1
 
1.2%
청천1 1
 
1.2%
십정5 1
 
1.2%
십정4 1
 
1.2%
신촌 1
 
1.2%
Other values (70) 70
87.5%
2024-04-14T12:14:08.545582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 16
 
4.9%
산 13
 
4.0%
1 13
 
4.0%
부 11
 
3.3%
주 11
 
3.3%
송 10
 
3.0%
2 10
 
3.0%
곡 9
 
2.7%
4 9
 
2.7%
3 7
 
2.1%
Other values (100) 220
66.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 258
78.4%
Decimal Number 47
 
14.3%
Uppercase Letter 16
 
4.9%
Open Punctuation 2
 
0.6%
Close Punctuation 2
 
0.6%
Dash Punctuation 2
 
0.6%
Other Punctuation 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 13
 
5.0%
부 11
 
4.3%
주 11
 
4.3%
송 10
 
3.9%
곡 9
 
3.5%
림 6
 
2.3%
변 6
 
2.3%
학 5
 
1.9%
개 5
 
1.9%
평 5
 
1.9%
Other values (87) 177
68.6%
Decimal Number
ValueCountFrequency (%)
1 13
27.7%
2 10
21.3%
4 9
19.1%
3 7
14.9%
5 4
 
8.5%
6 2
 
4.3%
7 1
 
2.1%
0 1
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
A 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 258
78.4%
Common 55
 
16.7%
Latin 16
 
4.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 13
 
5.0%
부 11
 
4.3%
주 11
 
4.3%
송 10
 
3.9%
곡 9
 
3.5%
림 6
 
2.3%
변 6
 
2.3%
학 5
 
1.9%
개 5
 
1.9%
평 5
 
1.9%
Other values (87) 177
68.6%
Common
ValueCountFrequency (%)
1 13
23.6%
2 10
18.2%
4 9
16.4%
3 7
12.7%
5 4
 
7.3%
( 2
 
3.6%
6 2
 
3.6%
) 2
 
3.6%
- 2
 
3.6%
, 2
 
3.6%
Other values (2) 2
 
3.6%
Latin
ValueCountFrequency (%)
A 16
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 258
78.4%
ASCII 71
 
21.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 16
22.5%
1 13
18.3%
2 10
14.1%
4 9
12.7%
3 7
9.9%
5 4
 
5.6%
( 2
 
2.8%
6 2
 
2.8%
) 2
 
2.8%
- 2
 
2.8%
Other values (3) 4
 
5.6%
Hangul
ValueCountFrequency (%)
산 13
 
5.0%
부 11
 
4.3%
주 11
 
4.3%
송 10
 
3.9%
곡 9
 
3.5%
림 6
 
2.3%
변 6
 
2.3%
학 5
 
1.9%
개 5
 
1.9%
평 5
 
1.9%
Other values (87) 177
68.6%

위치
Text

Distinct79
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size772.0 B
2024-04-14T12:14:08.752074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length21
Mean length14.0125
Min length10

Characters and Unicode

Total characters1121
Distinct characters71
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)97.5%

Sample

1st row경동 40번지 및 율목동 10번지 일원
2nd row송월동1가 12-16번지 일원(당초 : 송월동 11번지 일원)
3rd row송월동1가 10-1번지 일원
4th row경동 96-1번지 일원
5th row사동 23-4번지 일원
ValueCountFrequency (%)
일원 75
30.1%
산곡동 8
 
3.2%
송림동 7
 
2.8%
주안동 5
 
2.0%
십정동 5
 
2.0%
숭의동 4
 
1.6%
학익동 3
 
1.2%
삼산동 3
 
1.2%
효성동 3
 
1.2%
작전동 3
 
1.2%
Other values (120) 133
53.4%
2024-04-14T12:14:09.059388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
169
15.1%
지 83
 
7.4%
동 83
 
7.4%
번 82
 
7.3%
원 76
 
6.8%
일 76
 
6.8%
1 74
 
6.6%
- 54
 
4.8%
2 41
 
3.7%
3 31
 
2.8%
Other values (61) 352
31.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 571
50.9%
Decimal Number 322
28.7%
Space Separator 169
 
15.1%
Dash Punctuation 54
 
4.8%
Other Punctuation 3
 
0.3%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 83
14.5%
동 83
14.5%
번 82
14.4%
원 76
13.3%
일 76
13.3%
산 13
 
2.3%
송 12
 
2.1%
부 11
 
1.9%
림 8
 
1.4%
곡 8
 
1.4%
Other values (45) 119
20.8%
Decimal Number
ValueCountFrequency (%)
1 74
23.0%
2 41
12.7%
3 31
9.6%
0 30
9.3%
6 30
9.3%
4 27
 
8.4%
5 26
 
8.1%
8 24
 
7.5%
7 20
 
6.2%
9 19
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 2
66.7%
: 1
33.3%
Space Separator
ValueCountFrequency (%)
169
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 571
50.9%
Common 550
49.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 83
14.5%
동 83
14.5%
번 82
14.4%
원 76
13.3%
일 76
13.3%
산 13
 
2.3%
송 12
 
2.1%
부 11
 
1.9%
림 8
 
1.4%
곡 8
 
1.4%
Other values (45) 119
20.8%
Common
ValueCountFrequency (%)
169
30.7%
1 74
13.5%
- 54
 
9.8%
2 41
 
7.5%
3 31
 
5.6%
0 30
 
5.5%
6 30
 
5.5%
4 27
 
4.9%
5 26
 
4.7%
8 24
 
4.4%
Other values (6) 44
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 571
50.9%
ASCII 550
49.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
169
30.7%
1 74
13.5%
- 54
 
9.8%
2 41
 
7.5%
3 31
 
5.6%
0 30
 
5.5%
6 30
 
5.5%
4 27
 
4.9%
5 26
 
4.7%
8 24
 
4.4%
Other values (6) 44
 
8.0%
Hangul
ValueCountFrequency (%)
지 83
14.5%
동 83
14.5%
번 82
14.4%
원 76
13.3%
일 76
13.3%
산 13
 
2.3%
송 12
 
2.1%
부 11
 
1.9%
림 8
 
1.4%
곡 8
 
1.4%
Other values (45) 119
20.8%

면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58914.151
Minimum8548
Maximum223175.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-14T12:14:09.171362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8548
5-th percentile11912.89
Q121284.062
median44824.5
Q377297.8
95-th percentile163542.03
Maximum223175.2
Range214627.2
Interquartile range (IQR)56013.738

Descriptive statistics

Standard deviation48944.498
Coefficient of variation (CV)0.83077659
Kurtosis2.4145075
Mean58914.151
Median Absolute Deviation (MAD)25843.4
Skewness1.5832084
Sum4713132.1
Variance2.3955639 × 109
MonotonicityNot monotonic
2024-04-14T12:14:09.276181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34218.0 1
 
1.2%
57749.3 1
 
1.2%
93662.0 1
 
1.2%
33053.5 1
 
1.2%
115976.4 1
 
1.2%
11947.2 1
 
1.2%
219134.5 1
 
1.2%
74924.7 1
 
1.2%
94474.0 1
 
1.2%
45191.1 1
 
1.2%
Other values (70) 70
87.5%
ValueCountFrequency (%)
8548.0 1
1.2%
10146.1 1
1.2%
11007.5 1
1.2%
11261.0 1
1.2%
11947.2 1
1.2%
13109.1 1
1.2%
13767.8 1
1.2%
13968.8 1
1.2%
14042.7 1
1.2%
14512.1 1
1.2%
ValueCountFrequency (%)
223175.2 1
1.2%
219134.5 1
1.2%
193384.5 1
1.2%
180998.0 1
1.2%
162623.3 1
1.2%
153784.9 1
1.2%
137852.1 1
1.2%
123549.7 1
1.2%
122432.5 1
1.2%
117300.0 1
1.2%

사업유형
Categorical

Distinct3
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size772.0 B
재개발
58 
재건축
16 
주거환경개선(전면개량)
6 

Length

Max length12
Median length3
Mean length3.675
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
재개발 58
72.5%
재건축 16
 
20.0%
주거환경개선(전면개량) 6
 
7.5%

Length

2024-04-14T12:14:09.371310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T12:14:09.448804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재개발 58
72.5%
재건축 16
 
20.0%
주거환경개선(전면개량 6
 
7.5%

추진단계
Categorical

Distinct5
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
착공
28 
관리처분계획인가
18 
조합설립인가
17 
준공
10 
사업시행계획인가
7 

Length

Max length8
Median length6
Mean length4.725
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row조합설립인가
2nd row조합설립인가
3rd row조합설립인가
4th row조합설립인가
5th row관리처분계획인가

Common Values

ValueCountFrequency (%)
착공 28
35.0%
관리처분계획인가 18
22.5%
조합설립인가 17
21.2%
준공 10
 
12.5%
사업시행계획인가 7
 
8.8%

Length

2024-04-14T12:14:09.533030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T12:14:09.631646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
착공 28
35.0%
관리처분계획인가 18
22.5%
조합설립인가 17
21.2%
준공 10
 
12.5%
사업시행계획인가 7
 
8.8%

동수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.375
Minimum0
Maximum31
Zeros8
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-14T12:14:09.741148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.75
median9
Q313
95-th percentile23.05
Maximum31
Range31
Interquartile range (IQR)7.25

Descriptive statistics

Standard deviation6.3392988
Coefficient of variation (CV)0.67619187
Kurtosis1.9046036
Mean9.375
Median Absolute Deviation (MAD)4
Skewness1.0940572
Sum750
Variance40.186709
MonotonicityNot monotonic
2024-04-14T12:14:09.836680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
13 11
13.8%
6 10
12.5%
10 10
12.5%
0 8
10.0%
5 6
 
7.5%
7 6
 
7.5%
4 4
 
5.0%
12 3
 
3.8%
9 3
 
3.8%
8 3
 
3.8%
Other values (12) 16
20.0%
ValueCountFrequency (%)
0 8
10.0%
3 2
 
2.5%
4 4
 
5.0%
5 6
7.5%
6 10
12.5%
7 6
7.5%
8 3
 
3.8%
9 3
 
3.8%
10 10
12.5%
11 2
 
2.5%
ValueCountFrequency (%)
31 1
 
1.2%
28 1
 
1.2%
26 1
 
1.2%
24 1
 
1.2%
23 1
 
1.2%
20 1
 
1.2%
18 1
 
1.2%
17 1
 
1.2%
15 1
 
1.2%
14 3
3.8%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1317.7875
Minimum98
Maximum5678
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-14T12:14:09.934335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile335.25
Q1524
median957
Q31640
95-th percentile3584.05
Maximum5678
Range5580
Interquartile range (IQR)1116

Descriptive statistics

Standard deviation1114.3874
Coefficient of variation (CV)0.84565029
Kurtosis4.0555796
Mean1317.7875
Median Absolute Deviation (MAD)526.5
Skewness1.8991806
Sum105423
Variance1241859.2
MonotonicityNot monotonic
2024-04-14T12:14:10.027608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1500 2
 
2.5%
1115 2
 
2.5%
453 1
 
1.2%
526 1
 
1.2%
2005 1
 
1.2%
726 1
 
1.2%
2475 1
 
1.2%
494 1
 
1.2%
5050 1
 
1.2%
1623 1
 
1.2%
Other values (68) 68
85.0%
ValueCountFrequency (%)
98 1
1.2%
218 1
1.2%
295 1
1.2%
321 1
1.2%
336 1
1.2%
346 1
1.2%
378 1
1.2%
385 1
1.2%
386 1
1.2%
398 1
1.2%
ValueCountFrequency (%)
5678 1
1.2%
5050 1
1.2%
4715 1
1.2%
3965 1
1.2%
3564 1
1.2%
3180 1
1.2%
2706 1
1.2%
2568 1
1.2%
2562 1
1.2%
2475 1
1.2%

건폐율(퍼센트)
Real number (ℝ)

ZEROS 

Distinct74
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.67525
Minimum0
Maximum60
Zeros2
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-14T12:14:10.126375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.0965
Q115.6375
median18.245
Q321.545
95-th percentile28.3745
Maximum60
Range60
Interquartile range (IQR)5.9075

Descriptive statistics

Standard deviation8.0022755
Coefficient of variation (CV)0.40671785
Kurtosis10.467506
Mean19.67525
Median Absolute Deviation (MAD)2.885
Skewness2.2970705
Sum1574.02
Variance64.036413
MonotonicityNot monotonic
2024-04-14T12:14:10.228317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.0 2
 
2.5%
14.95 2
 
2.5%
14.99 2
 
2.5%
17.0 2
 
2.5%
18.08 2
 
2.5%
0.0 2
 
2.5%
18.22 1
 
1.2%
19.98 1
 
1.2%
16.26 1
 
1.2%
20.05 1
 
1.2%
Other values (64) 64
80.0%
ValueCountFrequency (%)
0.0 2
2.5%
12.27 1
1.2%
14.03 1
1.2%
14.1 1
1.2%
14.19 1
1.2%
14.28 1
1.2%
14.31 1
1.2%
14.6 1
1.2%
14.75 1
1.2%
14.86 1
1.2%
ValueCountFrequency (%)
60.0 1
1.2%
50.0 1
1.2%
41.21 1
1.2%
33.97 1
1.2%
28.08 1
1.2%
27.34 1
1.2%
26.48 1
1.2%
26.29 1
1.2%
26.19 1
1.2%
25.0 2
2.5%

용적률(퍼센트)
Real number (ℝ)

ZEROS 

Distinct75
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1200.9009
Minimum0
Maximum75213.33
Zeros2
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-14T12:14:10.329458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile223.3
Q1247.3625
median249.995
Q3285.7025
95-th percentile355.0665
Maximum75213.33
Range75213.33
Interquartile range (IQR)38.34

Descriptive statistics

Standard deviation8379.8003
Coefficient of variation (CV)6.9779284
Kurtosis79.991501
Mean1200.9009
Median Absolute Deviation (MAD)19.34
Skewness8.9435675
Sum96072.07
Variance70221053
MonotonicityNot monotonic
2024-04-14T12:14:10.460337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
275.0 2
 
2.5%
249.95 2
 
2.5%
0.0 2
 
2.5%
249.91 2
 
2.5%
250.0 2
 
2.5%
243.6 1
 
1.2%
294.14 1
 
1.2%
247.25 1
 
1.2%
284.51 1
 
1.2%
274.32 1
 
1.2%
Other values (65) 65
81.2%
ValueCountFrequency (%)
0.0 2
2.5%
194.0 1
1.2%
210.0 1
1.2%
224.0 1
1.2%
224.44 1
1.2%
224.99 1
1.2%
230.82 1
1.2%
231.23 1
1.2%
233.6 1
1.2%
236.86 1
1.2%
ValueCountFrequency (%)
75213.33 1
1.2%
449.97 1
1.2%
405.33 1
1.2%
358.61 1
1.2%
354.88 1
1.2%
349.98 1
1.2%
344.1 1
1.2%
342.97 1
1.2%
335.0 1
1.2%
329.64 1
1.2%

Interactions

2024-04-14T12:14:07.166160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:05.855195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:06.220079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:06.539685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:06.848610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:07.226526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:05.915803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:06.280788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:06.596894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:06.906248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:07.291823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:05.997361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:06.342796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:06.658964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:06.971645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:07.363233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:06.070969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:06.408159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:06.719374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:07.032573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:07.427748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:06.149415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:06.469815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:06.781496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:14:07.099172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-14T12:14:10.553032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구별구역명위치면적(제곱미터)사업유형추진단계동수세대수건폐율(퍼센트)용적률(퍼센트)
구별1.0001.0001.0000.0000.5170.1050.2600.0000.2910.000
구역명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위치1.0001.0001.0000.9801.0000.8450.9821.0001.0001.000
면적(제곱미터)0.0001.0000.9801.0000.5550.4340.9350.9220.2770.000
사업유형0.5171.0001.0000.5551.0000.2820.3130.6750.5690.000
추진단계0.1051.0000.8450.4340.2821.0000.0000.2860.0000.000
동수0.2601.0000.9820.9350.3130.0001.0000.8130.0000.000
세대수0.0001.0001.0000.9220.6750.2860.8131.0000.3820.000
건폐율(퍼센트)0.2911.0001.0000.2770.5690.0000.0000.3821.0000.000
용적률(퍼센트)0.0001.0001.0000.0000.0000.0000.0000.0000.0001.000
2024-04-14T12:14:10.642147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
추진단계사업유형구별
추진단계1.0000.2170.053
사업유형0.2171.0000.369
구별0.0530.3691.000
2024-04-14T12:14:10.708231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적(제곱미터)동수세대수건폐율(퍼센트)용적률(퍼센트)구별사업유형추진단계
면적(제곱미터)1.0000.7520.963-0.3910.2820.0000.3650.183
동수0.7521.0000.739-0.3350.2530.1190.1850.000
세대수0.9630.7391.000-0.3690.3620.0000.3690.161
건폐율(퍼센트)-0.391-0.335-0.3691.0000.1000.1000.3940.000
용적률(퍼센트)0.2820.2530.3620.1001.0000.0000.0000.000
구별0.0000.1190.0000.1000.0001.0000.3690.053
사업유형0.3650.1850.3690.3940.0000.3691.0000.217
추진단계0.1830.0000.1610.0000.0000.0530.2171.000

Missing values

2024-04-14T12:14:07.518027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-14T12:14:07.621880image/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

구별구역명위치면적(제곱미터)사업유형추진단계동수세대수건폐율(퍼센트)용적률(퍼센트)
0중구경동율목경동 40번지 및 율목동 10번지 일원34218.0재개발조합설립인가645318.22300.35
1중구송월송월동1가 12-16번지 일원(당초 : 송월동 11번지 일원)27338.0재개발조합설립인가651814.95249.69
2중구송월아파트송월동1가 10-1번지 일원33683.0재개발조합설립인가573014.6279.97
3중구경동경동 96-1번지 일원41970.0재개발조합설립인가681024.37349.98
4중구인천여상주변사동 23-4번지 일원20481.0재개발관리처분계획인가457919.26405.33
5동구대헌학교뒤송림동 37-10번지 일원39095.2주거환경개선(전면개량)착공792017.0264.62
6동구송림4송림동 2, 4번지 일원23915.0주거환경개선(전면개량)사업시행계획인가5119828.08254.85
7동구송림초교주변송림동 185번지 일원72616.5주거환경개선(전면개량)착공12256217.69354.88
8동구금송송림동 80-34 및 창영동 116-1번지 일원162623.3재개발관리처분계획인가26396516.01299.79
9동구서림송림동 64-55번지 일원19449.2재개발사업시행계획인가044521.85224.99
구별구역명위치면적(제곱미터)사업유형추진단계동수세대수건폐율(퍼센트)용적률(퍼센트)
70부평구한마음(아)부평동 758-31번지 외 7필지16100.74재건축착공641325.0250.0
71계양구계양1작전동 765번지 일원122432.5재개발착공15237118.0274.9
72계양구작전현대A작전동 439-7번지 일원64004.9재개발관리처분계획인가9137015.0275.0
73계양구효성1효성동 264-14번지 일원73363.9재개발준공12164614.95249.95
74계양구작전우영A작전동 869-17번지 일원11007.5재건축사업시행계획인가532126.29249.71
75계양구효성뉴서울A효성동 99-11번지14042.7재건축조합설립인가554822.79247.84
76계양구효성새사미A효성동 623-16번지15034.0재건축조합설립인가641217.94247.4
77서구가좌라이프빌라가좌동 344번지 일원53855.0재건축착공10121817.78299.65
78서구가좌진주1차A가좡동 30-2번지21484.45재건축조합설립인가771417.0257.05
79서구롯데우람A석남동 491-3번지 일원15244.0재건축착공651141.21449.97