Overview

Dataset statistics

Number of variables10
Number of observations64
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.4 KiB
Average record size in memory87.1 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://www.data.go.kr/data/15080866/fileData.do

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
위치 has unique valuesUnique
면적(제곱미터) has unique valuesUnique
동수 has 5 (7.8%) zerosZeros
건폐율(퍼센트) has 1 (1.6%) zerosZeros
용적률(퍼센트) has 1 (1.6%) zerosZeros

Reproduction

Analysis started2024-04-06 08:15:44.169797
Analysis finished2024-04-06 08:15:50.356886
Duration6.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구별
Categorical

Distinct8
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size644.0 B
부평구
21 
미추홀구
15 
동구
9 
중구
5 
계양구
5 
Other values (3)
9 

Length

Max length4
Median length3.5
Mean length2.96875
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부평구 21
32.8%
미추홀구 15
23.4%
동구 9
14.1%
중구 5
 
7.8%
계양구 5
 
7.8%
남동구 4
 
6.2%
서구 3
 
4.7%
연수구 2
 
3.1%

Length

2024-04-06T17:15:50.538848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:15:50.803887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부평구 21
32.8%
미추홀구 15
23.4%
동구 9
14.1%
중구 5
 
7.8%
계양구 5
 
7.8%
남동구 4
 
6.2%
서구 3
 
4.7%
연수구 2
 
3.1%

구역명
Text

UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size644.0 B
2024-04-06T17:15:51.331335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length4
Min length2

Characters and Unicode

Total characters256
Distinct characters102
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

Unique64 ?
Unique (%)100.0%

Sample

1st row경동율목
2nd row송월
3rd row송월아파트
4th row경동
5th row인천여상주변
ValueCountFrequency (%)
경동율목 1
 
1.6%
송월 1
 
1.6%
산곡 1
 
1.6%
상인천초교주변 1
 
1.6%
갈산1 1
 
1.6%
부평2 1
 
1.6%
부평4 1
 
1.6%
산곡2-1 1
 
1.6%
산곡3 1
 
1.6%
산곡5 1
 
1.6%
Other values (54) 54
84.4%
2024-04-06T17:15:52.076270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 14
 
5.5%
1 11
 
4.3%
송 9
 
3.5%
산 9
 
3.5%
4 8
 
3.1%
주 8
 
3.1%
곡 7
 
2.7%
천 5
 
2.0%
3 5
 
2.0%
림 5
 
2.0%
Other values (92) 175
68.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 200
78.1%
Decimal Number 37
 
14.5%
Uppercase Letter 14
 
5.5%
Other Punctuation 2
 
0.8%
Dash Punctuation 1
 
0.4%
Open Punctuation 1
 
0.4%
Close Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
송 9
 
4.5%
산 9
 
4.5%
주 8
 
4.0%
곡 7
 
3.5%
천 5
 
2.5%
림 5
 
2.5%
대 5
 
2.5%
부 5
 
2.5%
평 4
 
2.0%
진 4
 
2.0%
Other values (79) 139
69.5%
Decimal Number
ValueCountFrequency (%)
1 11
29.7%
4 8
21.6%
3 5
13.5%
2 5
13.5%
5 4
 
10.8%
6 2
 
5.4%
7 1
 
2.7%
0 1
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
A 14
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 200
78.1%
Common 42
 
16.4%
Latin 14
 
5.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
송 9
 
4.5%
산 9
 
4.5%
주 8
 
4.0%
곡 7
 
3.5%
천 5
 
2.5%
림 5
 
2.5%
대 5
 
2.5%
부 5
 
2.5%
평 4
 
2.0%
진 4
 
2.0%
Other values (79) 139
69.5%
Common
ValueCountFrequency (%)
1 11
26.2%
4 8
19.0%
3 5
11.9%
2 5
11.9%
5 4
 
9.5%
, 2
 
4.8%
6 2
 
4.8%
7 1
 
2.4%
- 1
 
2.4%
0 1
 
2.4%
Other values (2) 2
 
4.8%
Latin
ValueCountFrequency (%)
A 14
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 200
78.1%
ASCII 56
 
21.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 14
25.0%
1 11
19.6%
4 8
14.3%
3 5
 
8.9%
2 5
 
8.9%
5 4
 
7.1%
, 2
 
3.6%
6 2
 
3.6%
7 1
 
1.8%
- 1
 
1.8%
Other values (3) 3
 
5.4%
Hangul
ValueCountFrequency (%)
송 9
 
4.5%
산 9
 
4.5%
주 8
 
4.0%
곡 7
 
3.5%
천 5
 
2.5%
림 5
 
2.5%
대 5
 
2.5%
부 5
 
2.5%
평 4
 
2.0%
진 4
 
2.0%
Other values (79) 139
69.5%

위치
Text

UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size644.0 B
2024-04-06T17:15:52.563499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length24
Mean length14.359375
Min length10

Characters and Unicode

Total characters919
Distinct characters72
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)100.0%

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 60
29.7%
송림동 6
 
3.0%
산곡동 6
 
3.0%
주안동 4
 
2.0%
십정동 3
 
1.5%
숭의동 3
 
1.5%
작전동 3
 
1.5%
경동 2
 
1.0%
용현동 2
 
1.0%
가좌동 2
 
1.0%
Other values (102) 111
55.0%
2024-04-06T17:15:53.423168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
138
15.0%
동 67
 
7.3%
번 66
 
7.2%
지 65
 
7.1%
원 62
 
6.7%
일 62
 
6.7%
1 62
 
6.7%
- 45
 
4.9%
2 31
 
3.4%
3 28
 
3.0%
Other values (62) 293
31.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 466
50.7%
Decimal Number 262
28.5%
Space Separator 138
 
15.0%
Dash Punctuation 45
 
4.9%
Open Punctuation 2
 
0.2%
Close Punctuation 2
 
0.2%
Uppercase Letter 2
 
0.2%
Other Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 67
14.4%
번 66
14.2%
지 65
13.9%
원 62
13.3%
일 62
13.3%
송 11
 
2.4%
산 9
 
1.9%
림 7
 
1.5%
곡 7
 
1.5%
부 6
 
1.3%
Other values (44) 104
22.3%
Decimal Number
ValueCountFrequency (%)
1 62
23.7%
2 31
11.8%
3 28
10.7%
0 25
9.5%
4 23
 
8.8%
6 23
 
8.8%
5 20
 
7.6%
9 19
 
7.3%
8 16
 
6.1%
7 15
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
T 1
50.0%
K 1
50.0%
Other Punctuation
ValueCountFrequency (%)
: 1
50.0%
, 1
50.0%
Space Separator
ValueCountFrequency (%)
138
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 466
50.7%
Common 451
49.1%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 67
14.4%
번 66
14.2%
지 65
13.9%
원 62
13.3%
일 62
13.3%
송 11
 
2.4%
산 9
 
1.9%
림 7
 
1.5%
곡 7
 
1.5%
부 6
 
1.3%
Other values (44) 104
22.3%
Common
ValueCountFrequency (%)
138
30.6%
1 62
13.7%
- 45
 
10.0%
2 31
 
6.9%
3 28
 
6.2%
0 25
 
5.5%
4 23
 
5.1%
6 23
 
5.1%
5 20
 
4.4%
9 19
 
4.2%
Other values (6) 37
 
8.2%
Latin
ValueCountFrequency (%)
T 1
50.0%
K 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 466
50.7%
ASCII 453
49.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
138
30.5%
1 62
13.7%
- 45
 
9.9%
2 31
 
6.8%
3 28
 
6.2%
0 25
 
5.5%
4 23
 
5.1%
6 23
 
5.1%
5 20
 
4.4%
9 19
 
4.2%
Other values (8) 39
 
8.6%
Hangul
ValueCountFrequency (%)
동 67
14.4%
번 66
14.2%
지 65
13.9%
원 62
13.3%
일 62
13.3%
송 11
 
2.4%
산 9
 
1.9%
림 7
 
1.5%
곡 7
 
1.5%
부 6
 
1.3%
Other values (44) 104
22.3%

면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60854.604
Minimum8548
Maximum223175.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-04-06T17:15:53.761267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8548
5-th percentile11263.63
Q123307.362
median48981.15
Q381175.15
95-th percentile161297.54
Maximum223175.2
Range214627.2
Interquartile range (IQR)57867.788

Descriptive statistics

Standard deviation49830.346
Coefficient of variation (CV)0.81884266
Kurtosis2.1836745
Mean60854.604
Median Absolute Deviation (MAD)28306.2
Skewness1.5131865
Sum3894694.6
Variance2.4830633 × 109
MonotonicityNot monotonic
2024-04-06T17:15:54.120179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34218.0 1
 
1.6%
32366.5 1
 
1.6%
50415.9 1
 
1.6%
59954.0 1
 
1.6%
80720.2 1
 
1.6%
58457.1 1
 
1.6%
26195.0 1
 
1.6%
88025.5 1
 
1.6%
34552.0 1
 
1.6%
45191.1 1
 
1.6%
Other values (54) 54
84.4%
ValueCountFrequency (%)
8548.0 1
1.6%
10146.1 1
1.6%
11007.5 1
1.6%
11143.0 1
1.6%
11947.2 1
1.6%
14512.1 1
1.6%
15034.0 1
1.6%
15244.0 1
1.6%
15402.8 1
1.6%
17713.0 1
1.6%
ValueCountFrequency (%)
223175.2 1
1.6%
219169.5 1
1.6%
180998.0 1
1.6%
162623.3 1
1.6%
153784.9 1
1.6%
137852.1 1
1.6%
123549.7 1
1.6%
122432.5 1
1.6%
117300.0 1
1.6%
115976.4 1
1.6%

사업유형
Categorical

Distinct3
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size644.0 B
재개발
47 
재건축
14 
주거환경개선(전면개량)
 
3

Length

Max length12
Median length3
Mean length3.421875
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
재개발 47
73.4%
재건축 14
 
21.9%
주거환경개선(전면개량) 3
 
4.7%

Length

2024-04-06T17:15:54.494143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:15:54.708562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재개발 47
73.4%
재건축 14
 
21.9%
주거환경개선(전면개량 3
 
4.7%

추진단계
Categorical

Distinct5
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size644.0 B
착공
19 
관리처분계획인가
17 
조합설립인가
15 
사업시행계획인가
8 
준공
5 

Length

Max length8
Median length6
Mean length5.28125
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
착공 19
29.7%
관리처분계획인가 17
26.6%
조합설립인가 15
23.4%
사업시행계획인가 8
12.5%
준공 5
 
7.8%

Length

2024-04-06T17:15:54.979697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:15:55.263193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
착공 19
29.7%
관리처분계획인가 17
26.6%
조합설립인가 15
23.4%
사업시행계획인가 8
12.5%
준공 5
 
7.8%

동수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)32.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.71875
Minimum0
Maximum31
Zeros5
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-04-06T17:15:55.541738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median9.5
Q313
95-th percentile22.55
Maximum31
Range31
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.2168965
Coefficient of variation (CV)0.63968066
Kurtosis1.8217796
Mean9.71875
Median Absolute Deviation (MAD)3.5
Skewness1.043997
Sum622
Variance38.649802
MonotonicityNot monotonic
2024-04-06T17:15:55.797035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
13 10
15.6%
10 8
12.5%
6 7
10.9%
7 6
9.4%
5 6
9.4%
0 5
 
7.8%
14 3
 
4.7%
9 2
 
3.1%
8 2
 
3.1%
11 2
 
3.1%
Other values (11) 13
20.3%
ValueCountFrequency (%)
0 5
7.8%
3 2
 
3.1%
4 2
 
3.1%
5 6
9.4%
6 7
10.9%
7 6
9.4%
8 2
 
3.1%
9 2
 
3.1%
10 8
12.5%
11 2
 
3.1%
ValueCountFrequency (%)
31 1
 
1.6%
26 1
 
1.6%
24 1
 
1.6%
23 1
 
1.6%
20 1
 
1.6%
18 1
 
1.6%
17 1
 
1.6%
15 1
 
1.6%
14 3
 
4.7%
13 10
15.6%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1330.5156
Minimum218
Maximum5050
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-04-06T17:15:56.234020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum218
5-th percentile379.2
Q1549.5
median1094
Q31654.75
95-th percentile3506.4
Maximum5050
Range4832
Interquartile range (IQR)1105.25

Descriptive statistics

Standard deviation1054.6654
Coefficient of variation (CV)0.7926742
Kurtosis2.9689066
Mean1330.5156
Median Absolute Deviation (MAD)545
Skewness1.6867441
Sum85153
Variance1112319.1
MonotonicityNot monotonic
2024-04-06T17:15:56.553147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1115 2
 
3.1%
1500 2
 
3.1%
453 1
 
1.6%
2568 1
 
1.6%
2413 1
 
1.6%
1116 1
 
1.6%
398 1
 
1.6%
1498 1
 
1.6%
761 1
 
1.6%
962 1
 
1.6%
Other values (52) 52
81.2%
ValueCountFrequency (%)
218 1
1.6%
295 1
1.6%
321 1
1.6%
378 1
1.6%
386 1
1.6%
398 1
1.6%
404 1
1.6%
412 1
1.6%
445 1
1.6%
453 1
1.6%
ValueCountFrequency (%)
5050 1
1.6%
4715 1
1.6%
3965 1
1.6%
3564 1
1.6%
3180 1
1.6%
2706 1
1.6%
2568 1
1.6%
2475 1
1.6%
2413 1
1.6%
2371 1
1.6%

건폐율(퍼센트)
Real number (ℝ)

ZEROS 

Distinct61
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.046094
Minimum0
Maximum60
Zeros1
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-04-06T17:15:56.812579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.127
Q115.6375
median18.15
Q321.2525
95-th percentile33.0865
Maximum60
Range60
Interquartile range (IQR)5.615

Descriptive statistics

Standard deviation8.4359461
Coefficient of variation (CV)0.42082743
Kurtosis9.8928099
Mean20.046094
Median Absolute Deviation (MAD)2.87
Skewness2.5488563
Sum1282.95
Variance71.165186
MonotonicityNot monotonic
2024-04-06T17:15:57.054007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.08 2
 
3.1%
14.99 2
 
3.1%
17.0 2
 
3.1%
18.22 1
 
1.6%
14.31 1
 
1.6%
16.34 1
 
1.6%
19.98 1
 
1.6%
16.26 1
 
1.6%
20.05 1
 
1.6%
19.5 1
 
1.6%
Other values (51) 51
79.7%
ValueCountFrequency (%)
0.0 1
1.6%
12.27 1
1.6%
14.03 1
1.6%
14.1 1
1.6%
14.28 1
1.6%
14.31 1
1.6%
14.6 1
1.6%
14.86 1
1.6%
14.95 1
1.6%
14.98 1
1.6%
ValueCountFrequency (%)
60.0 1
1.6%
50.0 1
1.6%
41.21 1
1.6%
33.97 1
1.6%
28.08 1
1.6%
27.34 1
1.6%
26.48 1
1.6%
26.29 1
1.6%
26.19 1
1.6%
24.37 1
1.6%

용적률(퍼센트)
Real number (ℝ)

ZEROS 

Distinct62
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean267.26797
Minimum0
Maximum449.97
Zeros1
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-04-06T17:15:57.740929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile224.5225
Q1247.73
median249.995
Q3285.7025
95-th percentile349.098
Maximum449.97
Range449.97
Interquartile range (IQR)37.9725

Descriptive statistics

Standard deviation55.294523
Coefficient of variation (CV)0.20688795
Kurtosis9.6573263
Mean267.26797
Median Absolute Deviation (MAD)17.58
Skewness-0.8175175
Sum17105.15
Variance3057.4843
MonotonicityNot monotonic
2024-04-06T17:15:58.015910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
275.0 2
 
3.1%
249.91 2
 
3.1%
300.35 1
 
1.6%
344.1 1
 
1.6%
251.21 1
 
1.6%
273.6 1
 
1.6%
329.64 1
 
1.6%
224.44 1
 
1.6%
194.0 1
 
1.6%
249.19 1
 
1.6%
Other values (52) 52
81.2%
ValueCountFrequency (%)
0.0 1
1.6%
194.0 1
1.6%
210.0 1
1.6%
224.44 1
1.6%
224.99 1
1.6%
231.23 1
1.6%
233.6 1
1.6%
236.86 1
1.6%
239.98 1
1.6%
243.93 1
1.6%
ValueCountFrequency (%)
449.97 1
1.6%
405.33 1
1.6%
358.61 1
1.6%
349.98 1
1.6%
344.1 1
1.6%
342.97 1
1.6%
329.64 1
1.6%
329.32 1
1.6%
305.67 1
1.6%
300.35 1
1.6%

Interactions

2024-04-06T17:15:48.951391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:45.053105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:45.823439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:46.819268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:47.885889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:49.111134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:45.194615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:45.975797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:46.990891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:48.098357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:49.269786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:45.355787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:46.257212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:47.257661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:48.329756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:49.406904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:45.510248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:46.454071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:47.459949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:48.559204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:49.552260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:45.680726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:46.647259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:47.707507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:48.798575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:15:58.198883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구별구역명위치면적(제곱미터)사업유형추진단계동수세대수건폐율(퍼센트)용적률(퍼센트)
구별1.0001.0001.0000.0000.4770.0000.3550.0000.0000.000
구역명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위치1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
면적(제곱미터)0.0001.0001.0001.0000.5620.3040.9470.9070.0000.000
사업유형0.4771.0001.0000.5621.0000.2990.0000.6060.5990.356
추진단계0.0001.0001.0000.3040.2991.0000.0000.0000.2180.070
동수0.3551.0001.0000.9470.0000.0001.0000.8680.0000.000
세대수0.0001.0001.0000.9070.6060.0000.8681.0000.1860.000
건폐율(퍼센트)0.0001.0001.0000.0000.5990.2180.0000.1861.0000.785
용적률(퍼센트)0.0001.0001.0000.0000.3560.0700.0000.0000.7851.000
2024-04-06T17:15:58.425789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업유형추진단계구별
사업유형1.0000.2310.329
추진단계0.2311.0000.000
구별0.3290.0001.000
2024-04-06T17:15:58.603277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적(제곱미터)동수세대수건폐율(퍼센트)용적률(퍼센트)구별사업유형추진단계
면적(제곱미터)1.0000.7670.960-0.3810.2850.0000.3600.096
동수0.7671.0000.749-0.3950.2390.1680.0000.000
세대수0.9600.7491.000-0.3740.3630.0000.2960.000
건폐율(퍼센트)-0.381-0.395-0.3741.0000.0620.0000.4310.169
용적률(퍼센트)0.2850.2390.3630.0621.0000.0000.1500.030
구별0.0000.1680.0000.0000.0001.0000.3290.000
사업유형0.3600.0000.2960.4310.1500.3291.0000.231
추진단계0.0960.0000.0000.1690.0300.0000.2311.000

Missing values

2024-04-06T17:15:49.781077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:15:50.147037image/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 일원39943.9주거환경개선(전면개량)착공792017.0264.62
6동구송림4송림동 2, 4번지 일원23915.0주거환경개선(전면개량)사업시행계획인가5119828.08254.85
7동구금송송림동 80-34 및 창영동 116-1번지 일원162623.3재개발관리처분계획인가26396516.01299.79
8동구서림송림동 64-55번지 일원19477.1재개발사업시행계획인가044521.85224.99
9동구송림1,2동송림동 160번지 일원153784.9재개발관리처분계획인가20356421.11305.67
구별구역명위치면적(제곱미터)사업유형추진단계동수세대수건폐율(퍼센트)용적률(퍼센트)
54부평구삼산대보A삼산동 191번지 일원18513.0재건축관리처분계획인가650026.19289.28
55부평구청천대진A청천2동 236번지 일원14512.1재건축조합설립인가040418.73249.82
56계양구계양1작전동 765번지 일원122432.5재개발착공15237118.0274.9
57계양구작전현대A작전동 439-7번지 일원64004.9재개발착공9137015.0275.0
58계양구작전우영A작전동 869-17번지 일원11007.5재건축사업시행계획인가532126.29249.71
59계양구효성뉴서울A효성동 99-11번지17713.0재건축조합설립인가554822.79247.84
60계양구효성새사미A효성동 623-16번지15034.0재건축조합설립인가641217.94247.4
61서구가좌라이프빌라가좌동 344번지 일원53855.0재건축준공10121817.78299.65
62서구가좌진주1차A가좌동 30-2번지21484.45재건축조합설립인가771417.0257.05
63서구롯데우람A석남동 491-3번지 일원15244.0재건축착공651141.21449.97