Overview

Dataset statistics

Number of variables13
Number of observations64
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 KiB
Average record size in memory114.1 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://www.data.go.kr/data/15080867/fileData.do

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 2 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
사업유형 is highly overall correlated with 건축계획(단위_세대)-주택(전용면적)_40미터제곱 이하High 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 (85.4%)Imbalance
건축계획(단위_세대)-오피스텔(전용면적)_60제곱미터 초과 is highly imbalanced (82.6%)Imbalance
구 역 명 has unique valuesUnique
위치 has unique valuesUnique
면적(제곱미터) has unique valuesUnique
건축계획(단위_세대)-주택(전용면적)_40미터제곱 이하 has 17 (26.6%) zerosZeros
건축계획(단위_세대)-주택(전용면적)_60미터제곱 이하 has 1 (1.6%) zerosZeros
건축계획(단위_세대)-주택(전용면적)_85미터제곱 이하 has 4 (6.2%) zerosZeros
건축계획(단위_세대)-주택(전용면적)_85미터제곱 초과 has 40 (62.5%) zerosZeros

Reproduction

Analysis started2024-04-06 08:31:50.292789
Analysis finished2024-04-06 08:31:57.080593
Duration6.79 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

2024-04-06T17:31:57.239274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:31:57.537326image/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%

구 역 명
Text

UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size644.0 B
2024-04-06T17:31:58.094468image/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%
2024-04-06T17:31:58.968432image/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
2024-04-06T17:31:59.516141image/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%
2024-04-06T17:32:00.281350image/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
2024-04-06T17:32:00.595843image/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
2024-04-06T17:32:00.887257image/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

HIGH CORRELATION 

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

2024-04-06T17:32:01.196490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:32:01.433487image/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

2024-04-06T17:32:01.727744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:32:01.955201image/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%

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

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)70.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.20312
Minimum0
Maximum1500
Zeros17
Zeros (%)26.6%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-04-06T17:32:02.201616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median64.5
Q3136.5
95-th percentile382.1
Maximum1500
Range1500
Interquartile range (IQR)136.5

Descriptive statistics

Standard deviation241.50567
Coefficient of variation (CV)1.8130631
Kurtosis20.38282
Mean133.20312
Median Absolute Deviation (MAD)64.5
Skewness4.2335536
Sum8525
Variance58324.99
MonotonicityNot monotonic
2024-04-06T17:32:02.873804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 17
26.6%
130 3
 
4.7%
48 2
 
3.1%
106 1
 
1.6%
76 1
 
1.6%
98 1
 
1.6%
157 1
 
1.6%
257 1
 
1.6%
42 1
 
1.6%
50 1
 
1.6%
Other values (35) 35
54.7%
ValueCountFrequency (%)
0 17
26.6%
24 1
 
1.6%
37 1
 
1.6%
38 1
 
1.6%
42 1
 
1.6%
45 1
 
1.6%
46 1
 
1.6%
48 2
 
3.1%
50 1
 
1.6%
51 1
 
1.6%
ValueCountFrequency (%)
1500 1
1.6%
1148 1
1.6%
508 1
1.6%
386 1
1.6%
360 1
1.6%
311 1
1.6%
257 1
1.6%
256 1
1.6%
238 1
1.6%
212 1
1.6%

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

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean573.57812
Minimum0
Maximum3012
Zeros1
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-04-06T17:32:03.159506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43.9
Q1207.5
median368.5
Q3726.75
95-th percentile1826.15
Maximum3012
Range3012
Interquartile range (IQR)519.25

Descriptive statistics

Standard deviation602.58372
Coefficient of variation (CV)1.0505696
Kurtosis6.0348894
Mean573.57812
Median Absolute Deviation (MAD)243.5
Skewness2.2676246
Sum36709
Variance363107.14
MonotonicityNot monotonic
2024-04-06T17:32:03.450205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
321 2
 
3.1%
36 1
 
1.6%
800 1
 
1.6%
1337 1
 
1.6%
439 1
 
1.6%
970 1
 
1.6%
565 1
 
1.6%
191 1
 
1.6%
133 1
 
1.6%
213 1
 
1.6%
Other values (53) 53
82.8%
ValueCountFrequency (%)
0 1
1.6%
14 1
1.6%
36 1
1.6%
43 1
1.6%
49 1
1.6%
50 1
1.6%
54 1
1.6%
64 1
1.6%
71 1
1.6%
94 1
1.6%
ValueCountFrequency (%)
3012 1
1.6%
2769 1
1.6%
2105 1
1.6%
1910 1
1.6%
1351 1
1.6%
1337 1
1.6%
1094 1
1.6%
1069 1
1.6%
1025 1
1.6%
978 1
1.6%

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

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean539.40625
Minimum0
Maximum2078
Zeros4
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-04-06T17:32:03.762840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.85
Q1213.5
median409.5
Q3726.25
95-th percentile1464.4
Maximum2078
Range2078
Interquartile range (IQR)512.75

Descriptive statistics

Standard deviation462.98657
Coefficient of variation (CV)0.8583263
Kurtosis1.7737599
Mean539.40625
Median Absolute Deviation (MAD)225
Skewness1.3896275
Sum34522
Variance214356.56
MonotonicityNot monotonic
2024-04-06T17:32:04.074729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
6.2%
282 1
 
1.6%
1096 1
 
1.6%
1101 1
 
1.6%
554 1
 
1.6%
454 1
 
1.6%
1246 1
 
1.6%
484 1
 
1.6%
214 1
 
1.6%
835 1
 
1.6%
Other values (51) 51
79.7%
ValueCountFrequency (%)
0 4
6.2%
59 1
 
1.6%
67 1
 
1.6%
80 1
 
1.6%
83 1
 
1.6%
114 1
 
1.6%
136 1
 
1.6%
162 1
 
1.6%
165 1
 
1.6%
173 1
 
1.6%
ValueCountFrequency (%)
2078 1
1.6%
1848 1
1.6%
1642 1
1.6%
1471 1
1.6%
1427 1
1.6%
1252 1
1.6%
1246 1
1.6%
1101 1
1.6%
1096 1
1.6%
1050 1
1.6%

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

ZEROS 

Distinct24
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.46875
Minimum0
Maximum432
Zeros40
Zeros (%)62.5%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-04-06T17:32:04.349696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q350
95-th percentile282.25
Maximum432
Range432
Interquartile range (IQR)50

Descriptive statistics

Standard deviation90.437731
Coefficient of variation (CV)2.1295124
Kurtosis7.7790791
Mean42.46875
Median Absolute Deviation (MAD)0
Skewness2.8040132
Sum2718
Variance8178.9831
MonotonicityNot monotonic
2024-04-06T17:32:04.571502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 40
62.5%
50 2
 
3.1%
90 1
 
1.6%
2 1
 
1.6%
40 1
 
1.6%
345 1
 
1.6%
310 1
 
1.6%
60 1
 
1.6%
98 1
 
1.6%
10 1
 
1.6%
Other values (14) 14
 
21.9%
ValueCountFrequency (%)
0 40
62.5%
2 1
 
1.6%
10 1
 
1.6%
15 1
 
1.6%
25 1
 
1.6%
31 1
 
1.6%
32 1
 
1.6%
40 1
 
1.6%
50 2
 
3.1%
52 1
 
1.6%
ValueCountFrequency (%)
432 1
1.6%
345 1
1.6%
310 1
1.6%
298 1
1.6%
193 1
1.6%
176 1
1.6%
142 1
1.6%
98 1
1.6%
90 1
1.6%
79 1
1.6%

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

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size644.0 B
0
62 
88
 
1
124
 
1

Length

Max length3
Median length1
Mean length1.046875
Min length1

Unique

Unique2 ?
Unique (%)3.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 62
96.9%
88 1
 
1.6%
124 1
 
1.6%

Length

2024-04-06T17:32:04.914245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:32:05.248863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 62
96.9%
88 1
 
1.6%
124 1
 
1.6%

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

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size644.0 B
0
64 

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 64
100.0%

Length

2024-04-06T17:32:05.472492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:32:05.692145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 64
100.0%

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

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size644.0 B
0
61 
51
 
1
442
 
1
53
 
1

Length

Max length3
Median length1
Mean length1.0625
Min length1

Unique

Unique3 ?
Unique (%)4.7%

Sample

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

Common Values

ValueCountFrequency (%)
0 61
95.3%
51 1
 
1.6%
442 1
 
1.6%
53 1
 
1.6%

Length

2024-04-06T17:32:05.971810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:32:06.280925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 61
95.3%
51 1
 
1.6%
442 1
 
1.6%
53 1
 
1.6%

Interactions

2024-04-06T17:31:55.463862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:51.493031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:52.397697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:53.511046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:54.540375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:55.646266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:51.637915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:52.555222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:53.742343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:54.707620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:55.877385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:51.883208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:52.756560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:54.012216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:54.901320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:56.079680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:52.077158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:53.120808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:54.193396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:55.069978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:56.270832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:52.233606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:53.335968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:54.361346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:55.237719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:32:06.506014image/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
구별1.0001.0001.0000.0000.4770.0000.0000.0000.0000.0000.0290.000
구 역 명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위치1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
면적(제곱미터)0.0001.0001.0001.0000.5620.3040.8030.7540.9420.6600.0000.000
사업유형0.4771.0001.0000.5621.0000.2990.8460.0160.5720.0000.0000.000
추진단계0.0001.0001.0000.3040.2991.0000.0000.0000.0000.0000.0000.205
건축계획(단위_세대)-주택(전용면적)_40미터제곱 이하0.0001.0001.0000.8030.8460.0001.0000.2130.7870.7590.0000.000
건축계획(단위_세대)-주택(전용면적)_60미터제곱 이하0.0001.0001.0000.7540.0160.0000.2131.0000.6130.0000.0000.000
건축계획(단위_세대)-주택(전용면적)_85미터제곱 이하0.0001.0001.0000.9420.5720.0000.7870.6131.0000.5470.2000.340
건축계획(단위_세대)-주택(전용면적)_85미터제곱 초과0.0001.0001.0000.6600.0000.0000.7590.0000.5471.0000.0000.000
건축계획(단위_세대)-오피스텔(전용면적)_40제곱미터 이하0.0291.0001.0000.0000.0000.0000.0000.0000.2000.0001.0000.662
건축계획(단위_세대)-오피스텔(전용면적)_60제곱미터 초과0.0001.0001.0000.0000.0000.2050.0000.0000.3400.0000.6621.000
2024-04-06T17:32:06.857110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업유형추진단계건축계획(단위_세대)-오피스텔(전용면적)_40제곱미터 이하건축계획(단위_세대)-오피스텔(전용면적)_60제곱미터 초과구별
사업유형1.0000.2310.0000.0000.329
추진단계0.2311.0000.0000.1640.000
건축계획(단위_세대)-오피스텔(전용면적)_40제곱미터 이하0.0000.0001.0000.6840.000
건축계획(단위_세대)-오피스텔(전용면적)_60제곱미터 초과0.0000.1640.6841.0000.000
구별0.3290.0000.0000.0001.000
2024-04-06T17:32:07.113309image/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)1.0000.7320.6590.8450.3220.0000.3600.0960.0000.000
건축계획(단위_세대)-주택(전용면적)_40미터제곱 이하0.7321.0000.3210.5810.3670.0000.5240.0000.0000.000
건축계획(단위_세대)-주택(전용면적)_60미터제곱 이하0.6590.3211.0000.409-0.2630.0000.0000.0000.0000.000
건축계획(단위_세대)-주택(전용면적)_85미터제곱 이하0.8450.5810.4091.0000.3310.0000.3880.0000.1030.193
건축계획(단위_세대)-주택(전용면적)_85미터제곱 초과0.3220.367-0.2630.3311.0000.0000.0000.0000.0000.000
구별0.0000.0000.0000.0000.0001.0000.3290.0000.0000.000
사업유형0.3600.5240.0000.3880.0000.3291.0000.2310.0000.000
추진단계0.0960.0000.0000.0000.0000.0000.2311.0000.0000.164
건축계획(단위_세대)-오피스텔(전용면적)_40제곱미터 이하0.0000.0000.0000.1030.0000.0000.0000.0001.0000.684
건축계획(단위_세대)-오피스텔(전용면적)_60제곱미터 초과0.0000.0000.0000.1930.0000.0000.0000.1640.6841.000

Missing values

2024-04-06T17:31:56.502172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:31:56.912887image/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
4중구인천여상주변사동 23-4번지 일원20481.0재개발관리처분계획인가09948008800
5동구대헌학교뒤송림동 37-10번지 일원39943.9주거환경개선(전면개량)착공05184020000
6동구송림4송림동 2, 4번지 일원23915.0주거환경개선(전면개량)사업시행계획인가11485000000
7동구금송송림동 80-34 및 창영동 116-1번지 일원162623.3재개발관리처분계획인가207191018480000
8동구서림송림동 64-55번지 일원19477.1재개발사업시행계획인가657122115000
9동구송림1,2동송림동 160번지 일원153784.9재개발관리처분계획인가25627696680000
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
54부평구삼산대보A삼산동 191번지 일원18513.0재건축관리처분계획인가0644360000
55부평구청천대진A청천2동 236번지 일원14512.1재건축조합설립인가0321830000
56계양구계양1작전동 765번지 일원122432.5재개발착공13313518870000
57계양구작전현대A작전동 439-7번지 일원64004.9재개발착공727185800000
58계양구작전우영A작전동 869-17번지 일원11007.5재건축사업시행계획인가031900000
59계양구효성뉴서울A효성동 99-11번지17713.0재건축조합설립인가04561140000
60계양구효성새사미A효성동 623-16번지15034.0재건축조합설립인가02771360000
61서구가좌라이프빌라가좌동 344번지 일원53855.0재건축준공06495320000
62서구가좌진주1차A가좌동 30-2번지21484.45재건축조합설립인가23848900000
63서구롯데우람A석남동 491-3번지 일원15244.0재건축착공03212120000