Overview

Dataset statistics

Number of variables13
Number of observations80
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.9 KiB
Average record size in memory113.7 B

Variable types

Categorical6
Text2
Numeric5

Dataset

Description인천광역시 추정분담금 정보시스템 등록된 재개발 재건축 조합의 시설현황(구별, 구역명, 위치,면적,사업유형, 추진단계, 전용면적별 세대수 등)에 대한 데이터
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15080867&srcSe=7661IVAWM27C61E190

Alerts

건축계획(단위_세대)-오피스텔(전용면적)_60제곱미터 이하 has constant value ""Constant
면적(제곱미터) is highly overall correlated with 건축계획(단위_세대)-주택(전용면적)_40미터제곱 이하 and 2 other fieldsHigh correlation
건축계획(단위_세대)-주택(전용면적)_40미터제곱 이하 is highly overall correlated with 면적(제곱미터) and 1 other fieldsHigh correlation
건축계획(단위_세대)-주택(전용면적)_60미터제곱 이하 is highly overall correlated with 면적(제곱미터)High correlation
건축계획(단위_세대)-주택(전용면적)_85미터제곱 이하 is highly overall correlated with 면적(제곱미터) and 1 other fieldsHigh correlation
건축계획(단위_세대)-오피스텔(전용면적)_40제곱미터 이하 is highly overall correlated with 건축계획(단위_세대)-오피스텔(전용면적)_60제곱미터 초과High correlation
건축계획(단위_세대)-오피스텔(전용면적)_60제곱미터 초과 is highly overall correlated with 건축계획(단위_세대)-오피스텔(전용면적)_40제곱미터 이하High correlation
건축계획(단위_세대)-오피스텔(전용면적)_40제곱미터 이하 is highly imbalanced (87.8%)Imbalance
건축계획(단위_세대)-오피스텔(전용면적)_60제곱미터 초과 is highly imbalanced (85.5%)Imbalance
구 역 명 has unique valuesUnique
면적(제곱미터) has unique valuesUnique
건축계획(단위_세대)-주택(전용면적)_40미터제곱 이하 has 21 (26.2%) zerosZeros
건축계획(단위_세대)-주택(전용면적)_60미터제곱 이하 has 2 (2.5%) zerosZeros
건축계획(단위_세대)-주택(전용면적)_85미터제곱 이하 has 5 (6.2%) zerosZeros
건축계획(단위_세대)-주택(전용면적)_85미터제곱 초과 has 52 (65.0%) zerosZeros

Reproduction

Analysis started2024-04-14 03:12:32.568471
Analysis finished2024-04-14 03:12:35.242822
Duration2.67 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

2024-04-14T12:12:35.303644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T12:12:35.409431image/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%

구 역 명
Text

UNIQUE 

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
2024-04-14T12:12:35.629929image/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%
2024-04-14T12:12:35.967535image/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
2024-04-14T12:12:36.190656image/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%
2024-04-14T12:12:36.521672image/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
2024-04-14T12:12:36.641900image/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
2024-04-14T12:12:36.745987image/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

2024-04-14T12:12:36.844188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T12:12:36.929199image/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

2024-04-14T12:12:37.017992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T12:12:37.111884image/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%

건축계획(단위_세대)-주택(전용면적)_40미터제곱 이하
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.25
Minimum0
Maximum1500
Zeros21
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-14T12:12:37.220973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median78
Q3147.25
95-th percentile392.1
Maximum1500
Range1500
Interquartile range (IQR)147.25

Descriptive statistics

Standard deviation227.59644
Coefficient of variation (CV)1.6704326
Kurtosis20.407971
Mean136.25
Median Absolute Deviation (MAD)78
Skewness4.1293103
Sum10900
Variance51800.139
MonotonicityNot monotonic
2024-04-14T12:12:37.330299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21
26.2%
130 3
 
3.8%
48 2
 
2.5%
83 2
 
2.5%
45 1
 
1.2%
212 1
 
1.2%
76 1
 
1.2%
98 1
 
1.2%
102 1
 
1.2%
157 1
 
1.2%
Other values (46) 46
57.5%
ValueCountFrequency (%)
0 21
26.2%
24 1
 
1.2%
37 1
 
1.2%
38 1
 
1.2%
42 1
 
1.2%
45 1
 
1.2%
46 1
 
1.2%
48 2
 
2.5%
50 1
 
1.2%
51 1
 
1.2%
ValueCountFrequency (%)
1500 1
1.2%
1148 1
1.2%
667 1
1.2%
508 1
1.2%
386 1
1.2%
360 1
1.2%
311 1
1.2%
263 1
1.2%
257 1
1.2%
256 1
1.2%

건축계획(단위_세대)-주택(전용면적)_60미터제곱 이하
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean570.4
Minimum0
Maximum3012
Zeros2
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-14T12:12:37.440021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35.95
Q1183
median352
Q3711.25
95-th percentile1919.75
Maximum3012
Range3012
Interquartile range (IQR)528.25

Descriptive statistics

Standard deviation620.01198
Coefficient of variation (CV)1.0869775
Kurtosis5.0299384
Mean570.4
Median Absolute Deviation (MAD)248.5
Skewness2.1614187
Sum45632
Variance384414.85
MonotonicityNot monotonic
2024-04-14T12:12:37.545468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
2.5%
616 2
 
2.5%
321 2
 
2.5%
36 1
 
1.2%
1025 1
 
1.2%
800 1
 
1.2%
273 1
 
1.2%
3012 1
 
1.2%
379 1
 
1.2%
2105 1
 
1.2%
Other values (67) 67
83.8%
ValueCountFrequency (%)
0 2
2.5%
14 1
1.2%
35 1
1.2%
36 1
1.2%
43 1
1.2%
49 1
1.2%
50 1
1.2%
54 1
1.2%
64 1
1.2%
69 1
1.2%
ValueCountFrequency (%)
3012 1
1.2%
2769 1
1.2%
2557 1
1.2%
2105 1
1.2%
1910 1
1.2%
1813 1
1.2%
1351 1
1.2%
1337 1
1.2%
1135 1
1.2%
1094 1
1.2%

건축계획(단위_세대)-주택(전용면적)_85미터제곱 이하
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct74
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean533.9625
Minimum0
Maximum2454
Zeros5
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-14T12:12:37.655275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1214
median414
Q3682.75
95-th percentile1479.55
Maximum2454
Range2454
Interquartile range (IQR)468.75

Descriptive statistics

Standard deviation480.48031
Coefficient of variation (CV)0.89983905
Kurtosis3.7753537
Mean533.9625
Median Absolute Deviation (MAD)208.5
Skewness1.7983677
Sum42717
Variance230861.33
MonotonicityNot monotonic
2024-04-14T12:12:37.769939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
6.2%
214 2
 
2.5%
136 2
 
2.5%
282 1
 
1.2%
418 1
 
1.2%
1471 1
 
1.2%
165 1
 
1.2%
1642 1
 
1.2%
593 1
 
1.2%
296 1
 
1.2%
Other values (64) 64
80.0%
ValueCountFrequency (%)
0 5
6.2%
59 1
 
1.2%
67 1
 
1.2%
80 1
 
1.2%
83 1
 
1.2%
114 1
 
1.2%
119 1
 
1.2%
136 2
 
2.5%
162 1
 
1.2%
165 1
 
1.2%
ValueCountFrequency (%)
2454 1
1.2%
2078 1
1.2%
1848 1
1.2%
1642 1
1.2%
1471 1
1.2%
1427 1
1.2%
1252 1
1.2%
1246 1
1.2%
1101 1
1.2%
1096 1
1.2%

건축계획(단위_세대)-주택(전용면적)_85미터제곱 초과
Real number (ℝ)

ZEROS 

Distinct26
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.7125
Minimum0
Maximum432
Zeros52
Zeros (%)65.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-14T12:12:37.887455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q334.75
95-th percentile198.25
Maximum432
Range432
Interquartile range (IQR)34.75

Descriptive statistics

Standard deviation82.291885
Coefficient of variation (CV)2.304288
Kurtosis10.418261
Mean35.7125
Median Absolute Deviation (MAD)0
Skewness3.1794897
Sum2857
Variance6771.9543
MonotonicityNot monotonic
2024-04-14T12:12:37.977702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 52
65.0%
50 3
 
3.8%
52 2
 
2.5%
90 1
 
1.2%
74 1
 
1.2%
40 1
 
1.2%
345 1
 
1.2%
310 1
 
1.2%
60 1
 
1.2%
98 1
 
1.2%
Other values (16) 16
 
20.0%
ValueCountFrequency (%)
0 52
65.0%
2 1
 
1.2%
4 1
 
1.2%
10 1
 
1.2%
15 1
 
1.2%
25 1
 
1.2%
31 1
 
1.2%
32 1
 
1.2%
33 1
 
1.2%
40 1
 
1.2%
ValueCountFrequency (%)
432 1
1.2%
345 1
1.2%
310 1
1.2%
298 1
1.2%
193 1
1.2%
176 1
1.2%
142 1
1.2%
98 1
1.2%
90 1
1.2%
79 1
1.2%

건축계획(단위_세대)-오피스텔(전용면적)_40제곱미터 이하
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size772.0 B
0
78 
88
 
1
124
 
1

Length

Max length3
Median length1
Mean length1.0375
Min length1

Unique

Unique2 ?
Unique (%)2.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row88

Common Values

ValueCountFrequency (%)
0 78
97.5%
88 1
 
1.2%
124 1
 
1.2%

Length

2024-04-14T12:12:38.080728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T12:12:38.169002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 78
97.5%
88 1
 
1.2%
124 1
 
1.2%

건축계획(단위_세대)-오피스텔(전용면적)_60제곱미터 이하
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
0
80 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 80
100.0%

Length

2024-04-14T12:12:38.250170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T12:12:38.322993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 80
100.0%

건축계획(단위_세대)-오피스텔(전용면적)_60제곱미터 초과
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
0
77 
51
 
1
442
 
1
53
 
1

Length

Max length3
Median length1
Mean length1.05
Min length1

Unique

Unique3 ?
Unique (%)3.8%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 77
96.2%
51 1
 
1.2%
442 1
 
1.2%
53 1
 
1.2%

Length

2024-04-14T12:12:38.408386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T12:12:38.498030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 77
96.2%
51 1
 
1.2%
442 1
 
1.2%
53 1
 
1.2%

Interactions

2024-04-14T12:12:34.460558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:33.075200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:33.405929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:33.760866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:34.113017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:34.720973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:33.134631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:33.475362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:33.828589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:34.176057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:34.792045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:33.208431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:33.549328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:33.902888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:34.251276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:34.866813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:33.279676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:33.623391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:33.972488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:34.326242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:34.933737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:33.344327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:33.691401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:34.045151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:12:34.393313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-14T12:12:38.563540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구별구 역 명위치면적(제곱미터)사업유형추진단계건축계획(단위_세대)-주택(전용면적)_40미터제곱 이하건축계획(단위_세대)-주택(전용면적)_60미터제곱 이하건축계획(단위_세대)-주택(전용면적)_85미터제곱 이하건축계획(단위_세대)-주택(전용면적)_85미터제곱 초과건축계획(단위_세대)-오피스텔(전용면적)_40제곱미터 이하건축계획(단위_세대)-오피스텔(전용면적)_60제곱미터 초과
구별1.0001.0001.0000.0000.5170.1050.0000.2770.0000.0000.2100.000
구 역 명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위치1.0001.0001.0000.9801.0000.8450.9741.0001.0000.9741.0001.000
면적(제곱미터)0.0001.0000.9801.0000.5550.4340.7700.7700.9570.5460.0000.000
사업유형0.5171.0001.0000.5551.0000.2820.5600.3930.5850.0000.0000.000
추진단계0.1051.0000.8450.4340.2821.0000.1350.0000.0000.0000.0000.000
건축계획(단위_세대)-주택(전용면적)_40미터제곱 이하0.0001.0000.9740.7700.5600.1351.0000.6600.8440.7250.0000.000
건축계획(단위_세대)-주택(전용면적)_60미터제곱 이하0.2771.0001.0000.7700.3930.0000.6601.0000.8020.0000.0000.000
건축계획(단위_세대)-주택(전용면적)_85미터제곱 이하0.0001.0001.0000.9570.5850.0000.8440.8021.0000.3430.2530.485
건축계획(단위_세대)-주택(전용면적)_85미터제곱 초과0.0001.0000.9740.5460.0000.0000.7250.0000.3431.0000.0000.000
건축계획(단위_세대)-오피스텔(전용면적)_40제곱미터 이하0.2101.0001.0000.0000.0000.0000.0000.0000.2530.0001.0000.665
건축계획(단위_세대)-오피스텔(전용면적)_60제곱미터 초과0.0001.0001.0000.0000.0000.0000.0000.0000.4850.0000.6651.000
2024-04-14T12:12:38.682661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구별건축계획(단위_세대)-오피스텔(전용면적)_60제곱미터 초과추진단계건축계획(단위_세대)-오피스텔(전용면적)_40제곱미터 이하사업유형
구별1.0000.0000.0530.1270.369
건축계획(단위_세대)-오피스텔(전용면적)_60제곱미터 초과0.0001.0000.0000.6890.000
추진단계0.0530.0001.0000.0000.217
건축계획(단위_세대)-오피스텔(전용면적)_40제곱미터 이하0.1270.6890.0001.0000.000
사업유형0.3690.0000.2170.0001.000
2024-04-14T12:12:38.782066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적(제곱미터)건축계획(단위_세대)-주택(전용면적)_40미터제곱 이하건축계획(단위_세대)-주택(전용면적)_60미터제곱 이하건축계획(단위_세대)-주택(전용면적)_85미터제곱 이하건축계획(단위_세대)-주택(전용면적)_85미터제곱 초과구별사업유형추진단계건축계획(단위_세대)-오피스텔(전용면적)_40제곱미터 이하건축계획(단위_세대)-오피스텔(전용면적)_60제곱미터 초과
면적(제곱미터)1.0000.7200.6950.8640.3170.0000.3650.1830.0000.000
건축계획(단위_세대)-주택(전용면적)_40미터제곱 이하0.7201.0000.3620.6090.3420.0000.4370.0790.0000.000
건축계획(단위_세대)-주택(전용면적)_60미터제곱 이하0.6950.3621.0000.481-0.2040.0890.2610.0000.0000.000
건축계획(단위_세대)-주택(전용면적)_85미터제곱 이하0.8640.6090.4811.0000.3220.0000.4070.0000.1440.296
건축계획(단위_세대)-주택(전용면적)_85미터제곱 초과0.3170.342-0.2040.3221.0000.0000.0000.0000.0000.000
구별0.0000.0000.0890.0000.0001.0000.3690.0530.1270.000
사업유형0.3650.4370.2610.4070.0000.3691.0000.2170.0000.000
추진단계0.1830.0790.0000.0000.0000.0530.2171.0000.0000.000
건축계획(단위_세대)-오피스텔(전용면적)_40제곱미터 이하0.0000.0000.0000.1440.0000.1270.0000.0001.0000.689
건축계획(단위_세대)-오피스텔(전용면적)_60제곱미터 초과0.0000.0000.0000.2960.0000.0000.0000.0000.6891.000

Missing values

2024-04-14T12:12:35.030662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-14T12:12:35.175558image/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

구별구 역 명위치면적(제곱미터)사업유형추진단계건축계획(단위_세대)-주택(전용면적)_40미터제곱 이하건축계획(단위_세대)-주택(전용면적)_60미터제곱 이하건축계획(단위_세대)-주택(전용면적)_85미터제곱 이하건축계획(단위_세대)-주택(전용면적)_85미터제곱 초과건축계획(단위_세대)-오피스텔(전용면적)_40제곱미터 이하건축계획(단위_세대)-오피스텔(전용면적)_60제곱미터 이하건축계획(단위_세대)-오피스텔(전용면적)_60제곱미터 초과
0중구경동율목경동 40번지 및 율목동 10번지 일원34218.0재개발조합설립인가453628290000
1중구송월송월동1가 12-16번지 일원(당초 : 송월동 11번지 일원)27338.0재개발조합설립인가901435450000
2중구송월아파트송월동1가 10-1번지 일원33683.0재개발조합설립인가483952870000
3중구경동경동 96-1번지 일원41970.0재개발조합설립인가049392432000
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