Overview

Dataset statistics

Number of variables54
Number of observations462
Missing cells10705
Missing cells (%)42.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory206.8 KiB
Average record size in memory458.3 B

Variable types

Categorical5
Text8
Numeric26
DateTime15

Alerts

사업시행자 is highly imbalanced (52.0%)Imbalance
비고 is highly imbalanced (56.3%)Imbalance
(기존주택)준공년도 has 15 (3.2%) missing valuesMissing
(기존주택)동수 has 24 (5.2%) missing valuesMissing
(기존주택)세대수 has 7 (1.5%) missing valuesMissing
(기존주택)면적별세대수(40㎡미만) has 250 (54.1%) missing valuesMissing
(기존주택)면적별세대수(40~60㎡) has 197 (42.6%) missing valuesMissing
(기존주택)면적별세대수(60~85㎡) has 215 (46.5%) missing valuesMissing
(기존주택)면적별세대수(85~135㎡) has 306 (66.2%) missing valuesMissing
(기존주택)면적별세대수(135㎡이상) has 369 (79.9%) missing valuesMissing
사업시행세대수총계 has 205 (44.4%) missing valuesMissing
조합원분양세대수 has 221 (47.8%) missing valuesMissing
일반분양세대수 has 259 (56.1%) missing valuesMissing
임대세대수 has 318 (68.8%) missing valuesMissing
(신축주택)분양주택수 has 203 (43.9%) missing valuesMissing
(신축주택)면적별분양주택수(40㎡미만) has 420 (90.9%) missing valuesMissing
(신축주택)면적별분양주택수(40~60㎡) has 253 (54.8%) missing valuesMissing
(신축주택)면적별분양주택수(60~85㎡) has 232 (50.2%) missing valuesMissing
(신축주택)면적별분양주택수(85~135㎡) has 340 (73.6%) missing valuesMissing
(신축주택)면적별분양주택수(135㎡이상) has 421 (91.1%) missing valuesMissing
(신축주택)임대주택수 has 314 (68.0%) missing valuesMissing
(신축주택)면적별임대주택수(40㎡미만) has 365 (79.0%) missing valuesMissing
(신축주택)면적별임대주택수(40~60㎡) has 381 (82.5%) missing valuesMissing
(신축주택)면적별임대주택수(60~85㎡) has 455 (98.5%) missing valuesMissing
(기존)용적률 has 54 (11.7%) missing valuesMissing
(신축)용적률 has 68 (14.7%) missing valuesMissing
토지등소유자수(명) has 147 (31.8%) missing valuesMissing
조합원수(명) has 243 (52.6%) missing valuesMissing
사업예정기간(사업시작) has 167 (36.1%) missing valuesMissing
사업예정기간(사업완료) has 228 (49.4%) missing valuesMissing
정비예정구역고시일자 has 98 (21.2%) missing valuesMissing
정비구역지정예정일자 has 213 (46.1%) missing valuesMissing
정비계획승인일자 has 316 (68.4%) missing valuesMissing
정비구역지정일자(최초지정) has 182 (39.4%) missing valuesMissing
정비구역지정일자(변경지정) has 278 (60.2%) missing valuesMissing
추진위승인일자 has 277 (60.0%) missing valuesMissing
예비평가일자 has 341 (73.8%) missing valuesMissing
안전진단일자 has 300 (64.9%) missing valuesMissing
조합설립인가일자 has 242 (52.4%) missing valuesMissing
사업시행인가일자 has 224 (48.5%) missing valuesMissing
관리처분인가일자 has 274 (59.3%) missing valuesMissing
착공일자 has 273 (59.1%) missing valuesMissing
일반분양일자 has 317 (68.6%) missing valuesMissing
준공일자 has 313 (67.7%) missing valuesMissing
이전고시일자 has 356 (77.1%) missing valuesMissing
현추진상황 has 24 (5.2%) missing valuesMissing
위치 has unique valuesUnique
(기존주택)세대수 has 8 (1.7%) zerosZeros
사업시행세대수총계 has 8 (1.7%) zerosZeros
(신축주택)분양주택수 has 8 (1.7%) zerosZeros

Reproduction

Analysis started2024-04-11 02:59:48.338977
Analysis finished2024-04-11 02:59:49.523472
Duration1.18 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

Distinct24
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
안산시
69 
용인시
49 
안양시
42 
부천시
41 
수원시
40 
Other values (19)
221 

Length

Max length4
Median length3
Mean length3.0779221
Min length3

Unique

Unique3 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
안산시 69
14.9%
용인시 49
10.6%
안양시 42
9.1%
부천시 41
8.9%
수원시 40
8.7%
성남시 38
8.2%
평택시 34
 
7.4%
의왕시 23
 
5.0%
화성시 18
 
3.9%
남양주시 17
 
3.7%
Other values (14) 91
19.7%

Length

2024-04-11T11:59:49.597039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
안산시 69
14.9%
용인시 49
10.6%
안양시 42
9.1%
부천시 41
8.9%
수원시 40
8.7%
성남시 38
8.2%
평택시 34
 
7.4%
의왕시 23
 
5.0%
화성시 18
 
3.9%
남양주시 17
 
3.7%
Other values (14) 91
19.7%

사업단계
Categorical

Distinct8
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
예정구역
176 
준공
149 
착공
41 
관리처분
33 
조합설립
23 
Other values (3)
40 

Length

Max length5
Median length4
Mean length3.1948052
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사업시행
2nd row준공
3rd row조합설립
4th row예정구역
5th row관리처분

Common Values

ValueCountFrequency (%)
예정구역 176
38.1%
준공 149
32.3%
착공 41
 
8.9%
관리처분 33
 
7.1%
조합설립 23
 
5.0%
사업시행 21
 
4.5%
정비구역 11
 
2.4%
추진위원회 8
 
1.7%

Length

2024-04-11T11:59:49.727205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-11T11:59:49.834629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
예정구역 176
38.1%
준공 149
32.3%
착공 41
 
8.9%
관리처분 33
 
7.1%
조합설립 23
 
5.0%
사업시행 21
 
4.5%
정비구역 11
 
2.4%
추진위원회 8
 
1.7%

사업유형
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
재건축
274 
재개발
140 
주거환경개선
48 

Length

Max length6
Median length3
Mean length3.3116883
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
재건축 274
59.3%
재개발 140
30.3%
주거환경개선 48
 
10.4%

Length

2024-04-11T11:59:49.949359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-11T11:59:50.047422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재건축 274
59.3%
재개발 140
30.3%
주거환경개선 48
 
10.4%
Distinct461
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2024-04-11T11:59:50.273535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length5.7987013
Min length2

Characters and Unicode

Total characters2679
Distinct characters243
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique460 ?
Unique (%)99.6%

Sample

1st row고양Ⅰ-2구역
2nd row원당주공2단지
3rd row일산Ⅰ-2구역
4th row관산Ⅱ-1구역
5th row행신 Ⅱ-1구역
ValueCountFrequency (%)
주변 7
 
1.3%
괴안 6
 
1.1%
원종 3
 
0.6%
주공6단지 2
 
0.4%
종합운동장 2
 
0.4%
대야동 2
 
0.4%
소사본 2
 
0.4%
금정역 2
 
0.4%
송내 2
 
0.4%
2단지아파트 2
 
0.4%
Other values (488) 495
94.3%
2024-04-11T11:59:50.639524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 190
 
7.1%
107
 
4.0%
107
 
4.0%
106
 
4.0%
105
 
3.9%
96
 
3.6%
2 89
 
3.3%
83
 
3.1%
74
 
2.8%
- 71
 
2.7%
Other values (233) 1651
61.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2059
76.9%
Decimal Number 461
 
17.2%
Dash Punctuation 71
 
2.7%
Space Separator 63
 
2.4%
Other Punctuation 14
 
0.5%
Letter Number 7
 
0.3%
Uppercase Letter 3
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
107
 
5.2%
107
 
5.2%
106
 
5.1%
105
 
5.1%
96
 
4.7%
83
 
4.0%
74
 
3.6%
62
 
3.0%
54
 
2.6%
45
 
2.2%
Other values (213) 1220
59.3%
Decimal Number
ValueCountFrequency (%)
1 190
41.2%
2 89
19.3%
3 64
 
13.9%
5 28
 
6.1%
0 24
 
5.2%
4 18
 
3.9%
6 15
 
3.3%
7 14
 
3.0%
8 10
 
2.2%
9 9
 
2.0%
Other Punctuation
ValueCountFrequency (%)
, 10
71.4%
. 3
 
21.4%
? 1
 
7.1%
Letter Number
ValueCountFrequency (%)
4
57.1%
3
42.9%
Uppercase Letter
ValueCountFrequency (%)
D 2
66.7%
C 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 71
100.0%
Space Separator
ValueCountFrequency (%)
63
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2059
76.9%
Common 610
 
22.8%
Latin 10
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
107
 
5.2%
107
 
5.2%
106
 
5.1%
105
 
5.1%
96
 
4.7%
83
 
4.0%
74
 
3.6%
62
 
3.0%
54
 
2.6%
45
 
2.2%
Other values (213) 1220
59.3%
Common
ValueCountFrequency (%)
1 190
31.1%
2 89
14.6%
- 71
 
11.6%
3 64
 
10.5%
63
 
10.3%
5 28
 
4.6%
0 24
 
3.9%
4 18
 
3.0%
6 15
 
2.5%
7 14
 
2.3%
Other values (6) 34
 
5.6%
Latin
ValueCountFrequency (%)
4
40.0%
3
30.0%
D 2
20.0%
C 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2059
76.9%
ASCII 613
 
22.9%
Number Forms 7
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 190
31.0%
2 89
14.5%
- 71
 
11.6%
3 64
 
10.4%
63
 
10.3%
5 28
 
4.6%
0 24
 
3.9%
4 18
 
2.9%
6 15
 
2.4%
7 14
 
2.3%
Other values (8) 37
 
6.0%
Hangul
ValueCountFrequency (%)
107
 
5.2%
107
 
5.2%
106
 
5.1%
105
 
5.1%
96
 
4.7%
83
 
4.0%
74
 
3.6%
62
 
3.0%
54
 
2.6%
45
 
2.2%
Other values (213) 1220
59.3%
Number Forms
ValueCountFrequency (%)
4
57.1%
3
42.9%

위치
Text

UNIQUE 

Distinct462
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2024-04-11T11:59:50.855502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length27
Mean length18.666667
Min length12

Characters and Unicode

Total characters8624
Distinct characters175
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique462 ?
Unique (%)100.0%

Sample

1st row경기도 고양시 덕양구 고양동 92-1 한양연립 주변
2nd row경기도 고양시 성사동 715
3rd row경기도 고양시 일산동 960-16
4th row경기도 고양시 덕양구 관산동 178-57
5th row경기도 고양시 행신동 173-1행신지구 재건축 주변
ValueCountFrequency (%)
경기도 462
 
21.9%
안산시 69
 
3.3%
용인시 49
 
2.3%
수원시 49
 
2.3%
안양시 43
 
2.0%
부천시 41
 
1.9%
성남시 38
 
1.8%
평택시 36
 
1.7%
단원구 25
 
1.2%
만안구 24
 
1.1%
Other values (684) 1274
60.4%
2024-04-11T11:59:51.198481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1649
19.1%
491
 
5.7%
474
 
5.5%
465
 
5.4%
462
 
5.4%
457
 
5.3%
1 288
 
3.3%
- 228
 
2.6%
223
 
2.6%
2 218
 
2.5%
Other values (165) 3669
42.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5031
58.3%
Decimal Number 1711
 
19.8%
Space Separator 1649
 
19.1%
Dash Punctuation 228
 
2.6%
Other Punctuation 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
491
 
9.8%
474
 
9.4%
465
 
9.2%
462
 
9.2%
457
 
9.1%
223
 
4.4%
193
 
3.8%
127
 
2.5%
123
 
2.4%
103
 
2.0%
Other values (152) 1913
38.0%
Decimal Number
ValueCountFrequency (%)
1 288
16.8%
2 218
12.7%
3 191
11.2%
4 168
9.8%
6 159
9.3%
8 154
9.0%
5 153
8.9%
9 129
7.5%
7 129
7.5%
0 122
7.1%
Space Separator
ValueCountFrequency (%)
1649
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 228
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5031
58.3%
Common 3593
41.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
491
 
9.8%
474
 
9.4%
465
 
9.2%
462
 
9.2%
457
 
9.1%
223
 
4.4%
193
 
3.8%
127
 
2.5%
123
 
2.4%
103
 
2.0%
Other values (152) 1913
38.0%
Common
ValueCountFrequency (%)
1649
45.9%
1 288
 
8.0%
- 228
 
6.3%
2 218
 
6.1%
3 191
 
5.3%
4 168
 
4.7%
6 159
 
4.4%
8 154
 
4.3%
5 153
 
4.3%
9 129
 
3.6%
Other values (3) 256
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5031
58.3%
ASCII 3593
41.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1649
45.9%
1 288
 
8.0%
- 228
 
6.3%
2 218
 
6.1%
3 191
 
5.3%
4 168
 
4.7%
6 159
 
4.4%
8 154
 
4.3%
5 153
 
4.3%
9 129
 
3.6%
Other values (3) 256
 
7.1%
Hangul
ValueCountFrequency (%)
491
 
9.8%
474
 
9.4%
465
 
9.2%
462
 
9.2%
457
 
9.1%
223
 
4.4%
193
 
3.8%
127
 
2.5%
123
 
2.4%
103
 
2.0%
Other values (152) 1913
38.0%

구역면적(㎡)
Real number (ℝ)

Distinct460
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52741.387
Minimum2274
Maximum427629
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:51.325676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2274
5-th percentile10239.1
Q119972.8
median35759.95
Q363530
95-th percentile153217.85
Maximum427629
Range425355
Interquartile range (IQR)43557.2

Descriptive statistics

Standard deviation52296.245
Coefficient of variation (CV)0.9915599
Kurtosis10.625183
Mean52741.387
Median Absolute Deviation (MAD)19043.35
Skewness2.7135592
Sum24366521
Variance2.7348972 × 109
MonotonicityNot monotonic
2024-04-11T11:59:51.434182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22374.0 2
 
0.4%
16000.0 2
 
0.4%
16154.0 1
 
0.2%
108017.0 1
 
0.2%
56782.0 1
 
0.2%
5330.0 1
 
0.2%
66682.2 1
 
0.2%
184607.0 1
 
0.2%
133419.6 1
 
0.2%
48205.1 1
 
0.2%
Other values (450) 450
97.4%
ValueCountFrequency (%)
2274.0 1
0.2%
4413.2 1
0.2%
5135.0 1
0.2%
5330.0 1
0.2%
5614.0 1
0.2%
6067.0 1
0.2%
6718.0 1
0.2%
6986.0 1
0.2%
7138.0 1
0.2%
7301.0 1
0.2%
ValueCountFrequency (%)
427629.0 1
0.2%
361976.0 1
0.2%
296082.0 1
0.2%
261831.4 1
0.2%
257590.19 1
0.2%
242045.0 1
0.2%
233191.6 1
0.2%
230282.8 1
0.2%
223011.0 1
0.2%
222842.0 1
0.2%
Distinct146
Distinct (%)32.7%
Missing15
Missing (%)3.2%
Memory size3.7 KiB
2024-04-11T11:59:51.685542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length4
Mean length5.8903803
Min length4

Characters and Unicode

Total characters2633
Distinct characters18
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique104 ?
Unique (%)23.3%

Sample

1st row1980~1995
2nd row1986
3rd row1940~2001
4th row1980~1992
5th row1980~2003
ValueCountFrequency (%)
1985 38
 
8.3%
1986 34
 
7.4%
1984 24
 
5.2%
1982 20
 
4.4%
1990 19
 
4.1%
1994 18
 
3.9%
1987 18
 
3.9%
1992 16
 
3.5%
1970~1990 15
 
3.3%
1989 12
 
2.6%
Other values (129) 245
53.4%
2024-04-11T11:59:52.079807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 695
26.4%
1 575
21.8%
0 310
11.8%
8 294
11.2%
2 163
 
6.2%
~ 153
 
5.8%
5 110
 
4.2%
7 90
 
3.4%
4 76
 
2.9%
6 66
 
2.5%
Other values (8) 101
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2432
92.4%
Math Symbol 154
 
5.8%
Other Letter 26
 
1.0%
Space Separator 12
 
0.5%
Other Punctuation 6
 
0.2%
Dash Punctuation 3
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 695
28.6%
1 575
23.6%
0 310
12.7%
8 294
12.1%
2 163
 
6.7%
5 110
 
4.5%
7 90
 
3.7%
4 76
 
3.1%
6 66
 
2.7%
3 53
 
2.2%
Math Symbol
ValueCountFrequency (%)
~ 153
99.4%
1
 
0.6%
Other Letter
ValueCountFrequency (%)
13
50.0%
13
50.0%
Other Punctuation
ValueCountFrequency (%)
/ 5
83.3%
, 1
 
16.7%
Space Separator
ValueCountFrequency (%)
12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2607
99.0%
Hangul 26
 
1.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 695
26.7%
1 575
22.1%
0 310
11.9%
8 294
11.3%
2 163
 
6.3%
~ 153
 
5.9%
5 110
 
4.2%
7 90
 
3.5%
4 76
 
2.9%
6 66
 
2.5%
Other values (6) 75
 
2.9%
Hangul
ValueCountFrequency (%)
13
50.0%
13
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2606
99.0%
Hangul 26
 
1.0%
Math Operators 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 695
26.7%
1 575
22.1%
0 310
11.9%
8 294
11.3%
2 163
 
6.3%
~ 153
 
5.9%
5 110
 
4.2%
7 90
 
3.5%
4 76
 
2.9%
6 66
 
2.5%
Other values (5) 74
 
2.8%
Hangul
ValueCountFrequency (%)
13
50.0%
13
50.0%
Math Operators
ValueCountFrequency (%)
1
100.0%

(기존주택)동수
Real number (ℝ)

MISSING 

Distinct182
Distinct (%)41.6%
Missing24
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean134.8105
Minimum1
Maximum2573
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:52.208415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110.25
median31.5
Q3105.75
95-th percentile562.6
Maximum2573
Range2572
Interquartile range (IQR)95.5

Descriptive statistics

Standard deviation312.15176
Coefficient of variation (CV)2.3154855
Kurtosis26.028211
Mean134.8105
Median Absolute Deviation (MAD)25.5
Skewness4.7186804
Sum59047
Variance97438.721
MonotonicityNot monotonic
2024-04-11T11:59:52.327000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 19
 
4.1%
7 17
 
3.7%
3 16
 
3.5%
5 13
 
2.8%
9 13
 
2.8%
6 13
 
2.8%
10 12
 
2.6%
13 10
 
2.2%
8 9
 
1.9%
12 9
 
1.9%
Other values (172) 307
66.5%
(Missing) 24
 
5.2%
ValueCountFrequency (%)
1 3
 
0.6%
2 8
1.7%
3 16
3.5%
4 6
 
1.3%
5 13
2.8%
6 13
2.8%
7 17
3.7%
8 9
1.9%
9 13
2.8%
10 12
2.6%
ValueCountFrequency (%)
2573 1
0.2%
2377 1
0.2%
2092 1
0.2%
1985 1
0.2%
1950 1
0.2%
1772 1
0.2%
1516 1
0.2%
1398 1
0.2%
1397 1
0.2%
1241 1
0.2%

(기존주택)세대수
Real number (ℝ)

MISSING  ZEROS 

Distinct371
Distinct (%)81.5%
Missing7
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean846.6044
Minimum0
Maximum11000
Zeros8
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:52.443941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile93.7
Q1280.5
median519
Q31020
95-th percentile2449.9
Maximum11000
Range11000
Interquartile range (IQR)739.5

Descriptive statistics

Standard deviation1113.1246
Coefficient of variation (CV)1.3148108
Kurtosis25.668738
Mean846.6044
Median Absolute Deviation (MAD)286
Skewness4.3100407
Sum385205
Variance1239046.3
MonotonicityNot monotonic
2024-04-11T11:59:52.572094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
1.7%
420 5
 
1.1%
299 4
 
0.9%
576 4
 
0.9%
570 4
 
0.9%
264 4
 
0.9%
450 4
 
0.9%
470 3
 
0.6%
1080 3
 
0.6%
370 3
 
0.6%
Other values (361) 413
89.4%
(Missing) 7
 
1.5%
ValueCountFrequency (%)
0 8
1.7%
9 1
 
0.2%
33 2
 
0.4%
37 1
 
0.2%
41 1
 
0.2%
42 1
 
0.2%
44 1
 
0.2%
48 1
 
0.2%
50 1
 
0.2%
53 1
 
0.2%
ValueCountFrequency (%)
11000 1
0.2%
7669 1
0.2%
7499 1
0.2%
6544 1
0.2%
6488 1
0.2%
6075 1
0.2%
5822 1
0.2%
5709 1
0.2%
4910 1
0.2%
4625 1
0.2%
Distinct177
Distinct (%)83.5%
Missing250
Missing (%)54.1%
Infinite0
Infinite (%)0.0%
Mean601.58491
Minimum0
Maximum11000
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:52.697142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q166
median260
Q3557
95-th percentile2446.75
Maximum11000
Range11000
Interquartile range (IQR)491

Descriptive statistics

Standard deviation1185.06
Coefficient of variation (CV)1.9698964
Kurtosis32.972995
Mean601.58491
Median Absolute Deviation (MAD)208
Skewness4.9836443
Sum127536
Variance1404367.1
MonotonicityNot monotonic
2024-04-11T11:59:52.817094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 5
 
1.1%
66 5
 
1.1%
6 4
 
0.9%
2 3
 
0.6%
180 3
 
0.6%
54 3
 
0.6%
18 2
 
0.4%
8 2
 
0.4%
264 2
 
0.4%
270 2
 
0.4%
Other values (167) 181
39.2%
(Missing) 250
54.1%
ValueCountFrequency (%)
0 1
 
0.2%
1 1
 
0.2%
2 3
0.6%
4 1
 
0.2%
6 4
0.9%
7 2
0.4%
8 2
0.4%
10 2
0.4%
15 2
0.4%
17 1
 
0.2%
ValueCountFrequency (%)
11000 1
0.2%
6544 1
0.2%
5709 1
0.2%
4649 1
0.2%
4625 1
0.2%
4022 1
0.2%
3768 1
0.2%
3646 1
0.2%
2789 1
0.2%
2510 1
0.2%
Distinct222
Distinct (%)83.8%
Missing197
Missing (%)42.6%
Infinite0
Infinite (%)0.0%
Mean418.01887
Minimum1
Maximum2820
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:52.928762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13.2
Q1134
median275
Q3575
95-th percentile1164.8
Maximum2820
Range2819
Interquartile range (IQR)441

Descriptive statistics

Standard deviation441.07483
Coefficient of variation (CV)1.0551553
Kurtosis8.0650834
Mean418.01887
Median Absolute Deviation (MAD)181
Skewness2.4118933
Sum110775
Variance194547
MonotonicityNot monotonic
2024-04-11T11:59:53.041945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 3
 
0.6%
400 3
 
0.6%
590 3
 
0.6%
210 3
 
0.6%
300 3
 
0.6%
30 3
 
0.6%
240 3
 
0.6%
5 3
 
0.6%
326 2
 
0.4%
810 2
 
0.4%
Other values (212) 237
51.3%
(Missing) 197
42.6%
ValueCountFrequency (%)
1 1
 
0.2%
3 1
 
0.2%
4 1
 
0.2%
5 3
0.6%
6 2
0.4%
7 1
 
0.2%
9 1
 
0.2%
11 1
 
0.2%
12 2
0.4%
13 1
 
0.2%
ValueCountFrequency (%)
2820 1
0.2%
2664 1
0.2%
2560 1
0.2%
2111 1
0.2%
1975 1
0.2%
1725 1
0.2%
1555 1
0.2%
1492 1
0.2%
1470 1
0.2%
1397 1
0.2%
Distinct203
Distinct (%)82.2%
Missing215
Missing (%)46.5%
Infinite0
Infinite (%)0.0%
Mean385.99595
Minimum1
Maximum7669
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:53.161274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13.6
Q1102
median260
Q3529.5
95-th percentile1024.1
Maximum7669
Range7668
Interquartile range (IQR)427.5

Descriptive statistics

Standard deviation569.98436
Coefficient of variation (CV)1.4766589
Kurtosis108.47416
Mean385.99595
Median Absolute Deviation (MAD)186
Skewness8.7593034
Sum95341
Variance324882.17
MonotonicityNot monotonic
2024-04-11T11:59:53.467083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 4
 
0.9%
80 4
 
0.9%
60 3
 
0.6%
4 3
 
0.6%
235 2
 
0.4%
102 2
 
0.4%
132 2
 
0.4%
390 2
 
0.4%
212 2
 
0.4%
108 2
 
0.4%
Other values (193) 221
47.8%
(Missing) 215
46.5%
ValueCountFrequency (%)
1 1
 
0.2%
3 1
 
0.2%
4 3
0.6%
5 2
0.4%
8 2
0.4%
9 2
0.4%
12 1
 
0.2%
13 1
 
0.2%
15 4
0.9%
16 2
0.4%
ValueCountFrequency (%)
7669 1
0.2%
1580 1
0.2%
1519 1
0.2%
1482 1
0.2%
1414 1
0.2%
1380 1
0.2%
1260 1
0.2%
1254 1
0.2%
1219 1
0.2%
1080 1
0.2%
Distinct107
Distinct (%)68.6%
Missing306
Missing (%)66.2%
Infinite0
Infinite (%)0.0%
Mean150.94231
Minimum1
Maximum1398
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:53.589956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.75
Q136
median86
Q3184.75
95-th percentile501
Maximum1398
Range1397
Interquartile range (IQR)148.75

Descriptive statistics

Standard deviation210.26138
Coefficient of variation (CV)1.3929917
Kurtosis13.26161
Mean150.94231
Median Absolute Deviation (MAD)60
Skewness3.2743154
Sum23547
Variance44209.848
MonotonicityNot monotonic
2024-04-11T11:59:53.710706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 5
 
1.1%
60 4
 
0.9%
4 4
 
0.9%
11 3
 
0.6%
36 3
 
0.6%
120 3
 
0.6%
90 3
 
0.6%
26 3
 
0.6%
37 3
 
0.6%
19 3
 
0.6%
Other values (97) 122
 
26.4%
(Missing) 306
66.2%
ValueCountFrequency (%)
1 2
0.4%
2 1
 
0.2%
4 4
0.9%
6 1
 
0.2%
7 1
 
0.2%
9 2
0.4%
10 2
0.4%
11 3
0.6%
12 2
0.4%
15 1
 
0.2%
ValueCountFrequency (%)
1398 1
0.2%
1146 1
0.2%
1008 1
0.2%
997 1
0.2%
800 1
0.2%
534 1
0.2%
504 2
0.4%
500 1
0.2%
477 1
0.2%
460 1
0.2%
Distinct67
Distinct (%)72.0%
Missing369
Missing (%)79.9%
Infinite0
Infinite (%)0.0%
Mean70.010753
Minimum1
Maximum372
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:53.833413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.6
Q118
median45
Q3104
95-th percentile201.8
Maximum372
Range371
Interquartile range (IQR)86

Descriptive statistics

Standard deviation74.286972
Coefficient of variation (CV)1.0610795
Kurtosis4.1474142
Mean70.010753
Median Absolute Deviation (MAD)37
Skewness1.8365568
Sum6511
Variance5518.5542
MonotonicityNot monotonic
2024-04-11T11:59:53.958931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5
 
1.1%
21 3
 
0.6%
45 3
 
0.6%
18 3
 
0.6%
24 3
 
0.6%
8 3
 
0.6%
20 2
 
0.4%
19 2
 
0.4%
104 2
 
0.4%
136 2
 
0.4%
Other values (57) 65
 
14.1%
(Missing) 369
79.9%
ValueCountFrequency (%)
1 5
1.1%
2 1
 
0.2%
3 1
 
0.2%
4 1
 
0.2%
5 1
 
0.2%
6 2
 
0.4%
7 1
 
0.2%
8 3
0.6%
9 2
 
0.4%
13 1
 
0.2%
ValueCountFrequency (%)
372 1
0.2%
324 2
0.4%
240 1
0.2%
206 1
0.2%
199 1
0.2%
168 1
0.2%
166 1
0.2%
156 1
0.2%
150 1
0.2%
148 1
0.2%

사업시행세대수총계
Real number (ℝ)

MISSING  ZEROS 

Distinct243
Distinct (%)94.6%
Missing205
Missing (%)44.4%
Infinite0
Infinite (%)0.0%
Mean1251.5331
Minimum0
Maximum5320
Zeros8
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:54.101612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile198.8
Q1477
median951
Q31651
95-th percentile3510.2
Maximum5320
Range5320
Interquartile range (IQR)1174

Descriptive statistics

Standard deviation1065.3055
Coefficient of variation (CV)0.85120046
Kurtosis2.2628064
Mean1251.5331
Median Absolute Deviation (MAD)510
Skewness1.5332644
Sum321644
Variance1134875.9
MonotonicityNot monotonic
2024-04-11T11:59:54.225443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
1.7%
1021 2
 
0.4%
2737 2
 
0.4%
576 2
 
0.4%
2571 2
 
0.4%
553 2
 
0.4%
1105 2
 
0.4%
1305 2
 
0.4%
542 1
 
0.2%
281 1
 
0.2%
Other values (233) 233
50.4%
(Missing) 205
44.4%
ValueCountFrequency (%)
0 8
1.7%
66 1
 
0.2%
100 1
 
0.2%
115 1
 
0.2%
133 1
 
0.2%
198 1
 
0.2%
199 1
 
0.2%
200 1
 
0.2%
213 1
 
0.2%
226 1
 
0.2%
ValueCountFrequency (%)
5320 1
0.2%
5090 1
0.2%
5041 1
0.2%
4871 1
0.2%
4774 1
0.2%
4250 1
0.2%
4089 1
0.2%
4002 1
0.2%
3964 1
0.2%
3850 1
0.2%

조합원분양세대수
Real number (ℝ)

MISSING 

Distinct220
Distinct (%)91.3%
Missing221
Missing (%)47.8%
Infinite0
Infinite (%)0.0%
Mean757.3029
Minimum27
Maximum4470
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:54.339952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile109
Q1268
median506
Q31026
95-th percentile2120
Maximum4470
Range4443
Interquartile range (IQR)758

Descriptive statistics

Standard deviation685.79485
Coefficient of variation (CV)0.90557536
Kurtosis4.3513311
Mean757.3029
Median Absolute Deviation (MAD)309
Skewness1.821932
Sum182510
Variance470314.58
MonotonicityNot monotonic
2024-04-11T11:59:54.464707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110 3
 
0.6%
441 3
 
0.6%
477 3
 
0.6%
440 3
 
0.6%
488 2
 
0.4%
203 2
 
0.4%
334 2
 
0.4%
144 2
 
0.4%
905 2
 
0.4%
351 2
 
0.4%
Other values (210) 217
47.0%
(Missing) 221
47.8%
ValueCountFrequency (%)
27 1
0.2%
37 1
0.2%
43 1
0.2%
45 1
0.2%
50 1
0.2%
58 1
0.2%
71 1
0.2%
75 1
0.2%
91 1
0.2%
96 1
0.2%
ValueCountFrequency (%)
4470 1
0.2%
3179 1
0.2%
3048 1
0.2%
2836 1
0.2%
2817 1
0.2%
2675 1
0.2%
2434 1
0.2%
2387 1
0.2%
2384 1
0.2%
2230 1
0.2%

일반분양세대수
Real number (ℝ)

MISSING 

Distinct179
Distinct (%)88.2%
Missing259
Missing (%)56.1%
Infinite0
Infinite (%)0.0%
Mean529.3399
Minimum15
Maximum2567
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:54.582742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile57.4
Q1188.5
median369
Q3696
95-th percentile1604
Maximum2567
Range2552
Interquartile range (IQR)507.5

Descriptive statistics

Standard deviation487.45697
Coefficient of variation (CV)0.92087705
Kurtosis2.8788997
Mean529.3399
Median Absolute Deviation (MAD)222
Skewness1.6924426
Sum107456
Variance237614.29
MonotonicityNot monotonic
2024-04-11T11:59:54.700977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142 5
 
1.1%
178 3
 
0.6%
414 3
 
0.6%
222 2
 
0.4%
138 2
 
0.4%
341 2
 
0.4%
275 2
 
0.4%
185 2
 
0.4%
258 2
 
0.4%
310 2
 
0.4%
Other values (169) 178
38.5%
(Missing) 259
56.1%
ValueCountFrequency (%)
15 1
0.2%
18 1
0.2%
19 2
0.4%
23 1
0.2%
29 2
0.4%
44 1
0.2%
50 1
0.2%
54 1
0.2%
57 1
0.2%
61 1
0.2%
ValueCountFrequency (%)
2567 1
0.2%
2339 1
0.2%
2165 1
0.2%
2022 1
0.2%
1963 1
0.2%
1799 1
0.2%
1795 1
0.2%
1737 1
0.2%
1705 1
0.2%
1655 1
0.2%

임대세대수
Real number (ℝ)

MISSING 

Distinct112
Distinct (%)77.8%
Missing318
Missing (%)68.8%
Infinite0
Infinite (%)0.0%
Mean163.63194
Minimum5
Maximum1399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:54.805515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile19
Q139.5
median77.5
Q3174.25
95-th percentile719.25
Maximum1399
Range1394
Interquartile range (IQR)134.75

Descriptive statistics

Standard deviation243.40802
Coefficient of variation (CV)1.4875336
Kurtosis10.776254
Mean163.63194
Median Absolute Deviation (MAD)48
Skewness3.16681
Sum23563
Variance59247.465
MonotonicityNot monotonic
2024-04-11T11:59:54.916655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34 4
 
0.9%
21 4
 
0.9%
150 3
 
0.6%
132 3
 
0.6%
33 3
 
0.6%
19 2
 
0.4%
47 2
 
0.4%
54 2
 
0.4%
62 2
 
0.4%
37 2
 
0.4%
Other values (102) 117
 
25.3%
(Missing) 318
68.8%
ValueCountFrequency (%)
5 1
 
0.2%
8 1
 
0.2%
10 1
 
0.2%
11 1
 
0.2%
14 1
 
0.2%
16 1
 
0.2%
18 1
 
0.2%
19 2
0.4%
20 1
 
0.2%
21 4
0.9%
ValueCountFrequency (%)
1399 1
0.2%
1310 1
0.2%
1234 1
0.2%
908 1
0.2%
892 1
0.2%
875 1
0.2%
812 1
0.2%
729 1
0.2%
664 1
0.2%
620 1
0.2%

(신축주택)분양주택수
Real number (ℝ)

MISSING  ZEROS 

Distinct236
Distinct (%)91.1%
Missing203
Missing (%)43.9%
Infinite0
Infinite (%)0.0%
Mean1170.9035
Minimum0
Maximum4470
Zeros8
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:55.038045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile191
Q1470.5
median900
Q31532.5
95-th percentile3309.1
Maximum4470
Range4470
Interquartile range (IQR)1062

Descriptive statistics

Standard deviation964.06788
Coefficient of variation (CV)0.82335385
Kurtosis1.3297649
Mean1170.9035
Median Absolute Deviation (MAD)463
Skewness1.3397582
Sum303264
Variance929426.88
MonotonicityNot monotonic
2024-04-11T11:59:55.173625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
1.7%
271 3
 
0.6%
440 2
 
0.4%
836 2
 
0.4%
642 2
 
0.4%
1305 2
 
0.4%
2361 2
 
0.4%
507 2
 
0.4%
360 2
 
0.4%
536 2
 
0.4%
Other values (226) 232
50.2%
(Missing) 203
43.9%
ValueCountFrequency (%)
0 8
1.7%
66 1
 
0.2%
100 1
 
0.2%
115 1
 
0.2%
133 1
 
0.2%
182 1
 
0.2%
192 1
 
0.2%
196 1
 
0.2%
198 1
 
0.2%
199 1
 
0.2%
ValueCountFrequency (%)
4470 1
0.2%
4412 1
0.2%
4149 1
0.2%
4089 1
0.2%
4002 1
0.2%
3962 1
0.2%
3712 1
0.2%
3657 1
0.2%
3535 1
0.2%
3521 1
0.2%
Distinct40
Distinct (%)95.2%
Missing420
Missing (%)90.9%
Infinite0
Infinite (%)0.0%
Mean82.904762
Minimum0
Maximum463
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:55.282326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.05
Q113.25
median45.5
Q390.25
95-th percentile369.6
Maximum463
Range463
Interquartile range (IQR)77

Descriptive statistics

Standard deviation112.98971
Coefficient of variation (CV)1.3628856
Kurtosis4.1822895
Mean82.904762
Median Absolute Deviation (MAD)34.5
Skewness2.1807144
Sum3482
Variance12766.674
MonotonicityNot monotonic
2024-04-11T11:59:55.387563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
67 2
 
0.4%
2 2
 
0.4%
16 1
 
0.2%
124 1
 
0.2%
10 1
 
0.2%
28 1
 
0.2%
169 1
 
0.2%
463 1
 
0.2%
32 1
 
0.2%
12 1
 
0.2%
Other values (30) 30
 
6.5%
(Missing) 420
90.9%
ValueCountFrequency (%)
0 1
0.2%
2 2
0.4%
3 1
0.2%
4 1
0.2%
5 1
0.2%
7 1
0.2%
8 1
0.2%
10 1
0.2%
12 1
0.2%
13 1
0.2%
ValueCountFrequency (%)
463 1
0.2%
397 1
0.2%
371 1
0.2%
343 1
0.2%
191 1
0.2%
169 1
0.2%
159 1
0.2%
124 1
0.2%
105 1
0.2%
104 1
0.2%
Distinct186
Distinct (%)89.0%
Missing253
Missing (%)54.8%
Infinite0
Infinite (%)0.0%
Mean522.33493
Minimum0
Maximum2409
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:55.495099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile54.8
Q1202
median375
Q3665
95-th percentile1512.4
Maximum2409
Range2409
Interquartile range (IQR)463

Descriptive statistics

Standard deviation467.51469
Coefficient of variation (CV)0.89504772
Kurtosis2.2633406
Mean522.33493
Median Absolute Deviation (MAD)220
Skewness1.5592044
Sum109168
Variance218569.98
MonotonicityNot monotonic
2024-04-11T11:59:55.612694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
976 3
 
0.6%
462 3
 
0.6%
306 2
 
0.4%
80 2
 
0.4%
557 2
 
0.4%
407 2
 
0.4%
538 2
 
0.4%
595 2
 
0.4%
602 2
 
0.4%
92 2
 
0.4%
Other values (176) 187
40.5%
(Missing) 253
54.8%
ValueCountFrequency (%)
0 1
0.2%
23 1
0.2%
28 1
0.2%
30 1
0.2%
42 1
0.2%
45 1
0.2%
46 1
0.2%
48 1
0.2%
50 1
0.2%
52 1
0.2%
ValueCountFrequency (%)
2409 1
0.2%
2051 1
0.2%
2026 1
0.2%
1944 1
0.2%
1922 1
0.2%
1901 1
0.2%
1727 1
0.2%
1719 1
0.2%
1635 1
0.2%
1603 1
0.2%
Distinct199
Distinct (%)86.5%
Missing232
Missing (%)50.2%
Infinite0
Infinite (%)0.0%
Mean578.09565
Minimum25
Maximum3425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:55.726282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile78.5
Q1208.5
median399
Q3715.25
95-th percentile1635.65
Maximum3425
Range3400
Interquartile range (IQR)506.75

Descriptive statistics

Standard deviation551.3558
Coefficient of variation (CV)0.95374494
Kurtosis5.0977644
Mean578.09565
Median Absolute Deviation (MAD)221
Skewness2.0179176
Sum132962
Variance303993.22
MonotonicityNot monotonic
2024-04-11T11:59:55.855350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
210 4
 
0.9%
178 3
 
0.6%
104 3
 
0.6%
62 3
 
0.6%
245 2
 
0.4%
183 2
 
0.4%
283 2
 
0.4%
431 2
 
0.4%
46 2
 
0.4%
351 2
 
0.4%
Other values (189) 205
44.4%
(Missing) 232
50.2%
ValueCountFrequency (%)
25 1
 
0.2%
46 2
0.4%
52 1
 
0.2%
55 1
 
0.2%
62 3
0.6%
64 1
 
0.2%
65 1
 
0.2%
72 1
 
0.2%
74 1
 
0.2%
84 1
 
0.2%
ValueCountFrequency (%)
3425 1
0.2%
2820 1
0.2%
2817 1
0.2%
2527 1
0.2%
2018 1
0.2%
2003 1
0.2%
1996 1
0.2%
1986 1
0.2%
1765 1
0.2%
1748 1
0.2%
Distinct108
Distinct (%)88.5%
Missing340
Missing (%)73.6%
Infinite0
Infinite (%)0.0%
Mean222.02459
Minimum2
Maximum1257
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:55.989100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8.2
Q156.25
median123
Q3328
95-th percentile681.6
Maximum1257
Range1255
Interquartile range (IQR)271.75

Descriptive statistics

Standard deviation244.16004
Coefficient of variation (CV)1.0996982
Kurtosis4.16798
Mean222.02459
Median Absolute Deviation (MAD)92
Skewness1.9274522
Sum27087
Variance59614.123
MonotonicityNot monotonic
2024-04-11T11:59:56.334903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 4
 
0.9%
68 3
 
0.6%
32 2
 
0.4%
34 2
 
0.4%
33 2
 
0.4%
268 2
 
0.4%
102 2
 
0.4%
104 2
 
0.4%
62 2
 
0.4%
4 2
 
0.4%
Other values (98) 99
 
21.4%
(Missing) 340
73.6%
ValueCountFrequency (%)
2 1
 
0.2%
3 1
 
0.2%
4 2
0.4%
6 1
 
0.2%
7 1
 
0.2%
8 1
 
0.2%
12 1
 
0.2%
21 1
 
0.2%
23 1
 
0.2%
28 4
0.9%
ValueCountFrequency (%)
1257 1
0.2%
1148 1
0.2%
971 1
0.2%
956 1
0.2%
814 1
0.2%
768 1
0.2%
682 1
0.2%
674 1
0.2%
652 1
0.2%
603 1
0.2%
Distinct36
Distinct (%)87.8%
Missing421
Missing (%)91.1%
Infinite0
Infinite (%)0.0%
Mean123.21951
Minimum0
Maximum680
Zeros3
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:56.442470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median82
Q3180
95-th percentile432
Maximum680
Range680
Interquartile range (IQR)172

Descriptive statistics

Standard deviation154.36653
Coefficient of variation (CV)1.2527767
Kurtosis4.6686108
Mean123.21951
Median Absolute Deviation (MAD)78
Skewness2.0629448
Sum5052
Variance23829.026
MonotonicityNot monotonic
2024-04-11T11:59:56.554275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 3
 
0.6%
8 2
 
0.4%
2 2
 
0.4%
3 2
 
0.4%
680 1
 
0.2%
31 1
 
0.2%
145 1
 
0.2%
300 1
 
0.2%
148 1
 
0.2%
165 1
 
0.2%
Other values (26) 26
 
5.6%
(Missing) 421
91.1%
ValueCountFrequency (%)
0 3
0.6%
1 1
 
0.2%
2 2
0.4%
3 2
0.4%
4 1
 
0.2%
8 2
0.4%
20 1
 
0.2%
21 1
 
0.2%
31 1
 
0.2%
34 1
 
0.2%
ValueCountFrequency (%)
680 1
0.2%
582 1
0.2%
432 1
0.2%
300 1
0.2%
293 1
0.2%
239 1
0.2%
236 1
0.2%
212 1
0.2%
195 1
0.2%
184 1
0.2%

(신축주택)임대주택수
Real number (ℝ)

MISSING 

Distinct115
Distinct (%)77.7%
Missing314
Missing (%)68.0%
Infinite0
Infinite (%)0.0%
Mean166.66216
Minimum0
Maximum1399
Zeros2
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:56.693747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.7
Q138
median77.5
Q3175.75
95-th percentile706.25
Maximum1399
Range1399
Interquartile range (IQR)137.75

Descriptive statistics

Standard deviation245.21831
Coefficient of variation (CV)1.4713496
Kurtosis9.9547147
Mean166.66216
Median Absolute Deviation (MAD)49.5
Skewness3.0322535
Sum24666
Variance60132.021
MonotonicityNot monotonic
2024-04-11T11:59:56.810097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34 4
 
0.9%
21 4
 
0.9%
150 3
 
0.6%
132 3
 
0.6%
33 3
 
0.6%
37 2
 
0.4%
19 2
 
0.4%
47 2
 
0.4%
54 2
 
0.4%
62 2
 
0.4%
Other values (105) 121
 
26.2%
(Missing) 314
68.0%
ValueCountFrequency (%)
0 2
0.4%
5 1
0.2%
8 1
0.2%
10 1
0.2%
11 1
0.2%
14 1
0.2%
16 1
0.2%
18 1
0.2%
19 2
0.4%
20 1
0.2%
ValueCountFrequency (%)
1399 1
0.2%
1310 1
0.2%
1234 1
0.2%
908 1
0.2%
892 1
0.2%
875 1
0.2%
812 1
0.2%
729 1
0.2%
664 1
0.2%
635 1
0.2%
Distinct77
Distinct (%)79.4%
Missing365
Missing (%)79.0%
Infinite0
Infinite (%)0.0%
Mean117.42268
Minimum0
Maximum633
Zeros2
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:56.931492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.4
Q142
median62
Q3140
95-th percentile412.2
Maximum633
Range633
Interquartile range (IQR)98

Descriptive statistics

Standard deviation128.65691
Coefficient of variation (CV)1.0956734
Kurtosis4.4014452
Mean117.42268
Median Absolute Deviation (MAD)32
Skewness2.1280759
Sum11390
Variance16552.601
MonotonicityNot monotonic
2024-04-11T11:59:57.054814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54 3
 
0.6%
34 3
 
0.6%
44 3
 
0.6%
62 3
 
0.6%
42 3
 
0.6%
20 2
 
0.4%
47 2
 
0.4%
114 2
 
0.4%
311 2
 
0.4%
132 2
 
0.4%
Other values (67) 72
 
15.6%
(Missing) 365
79.0%
ValueCountFrequency (%)
0 2
0.4%
11 1
0.2%
12 1
0.2%
16 1
0.2%
19 1
0.2%
20 2
0.4%
24 1
0.2%
26 1
0.2%
29 1
0.2%
30 1
0.2%
ValueCountFrequency (%)
633 1
0.2%
580 1
0.2%
500 1
0.2%
452 1
0.2%
421 1
0.2%
410 1
0.2%
401 1
0.2%
390 1
0.2%
311 2
0.4%
289 1
0.2%
Distinct64
Distinct (%)79.0%
Missing381
Missing (%)82.5%
Infinite0
Infinite (%)0.0%
Mean113.1358
Minimum3
Maximum1051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:57.183201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10
Q126
median48
Q3111
95-th percentile597
Maximum1051
Range1048
Interquartile range (IQR)85

Descriptive statistics

Standard deviation182.37501
Coefficient of variation (CV)1.6120008
Kurtosis11.739489
Mean113.1358
Median Absolute Deviation (MAD)29
Skewness3.2875021
Sum9164
Variance33260.644
MonotonicityNot monotonic
2024-04-11T11:59:57.309147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 3
 
0.6%
21 3
 
0.6%
33 3
 
0.6%
150 2
 
0.4%
48 2
 
0.4%
22 2
 
0.4%
34 2
 
0.4%
89 2
 
0.4%
3 2
 
0.4%
36 2
 
0.4%
Other values (54) 58
 
12.6%
(Missing) 381
82.5%
ValueCountFrequency (%)
3 2
0.4%
5 1
 
0.2%
8 1
 
0.2%
10 1
 
0.2%
11 1
 
0.2%
14 1
 
0.2%
15 1
 
0.2%
17 2
0.4%
18 2
0.4%
19 3
0.6%
ValueCountFrequency (%)
1051 1
0.2%
809 1
0.2%
654 1
0.2%
614 1
0.2%
597 1
0.2%
360 1
0.2%
274 1
0.2%
272 1
0.2%
223 1
0.2%
219 1
0.2%
Distinct7
Distinct (%)100.0%
Missing455
Missing (%)98.5%
Infinite0
Infinite (%)0.0%
Mean136.42857
Minimum5
Maximum285
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:57.408187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile23
Q174.5
median127
Q3194.5
95-th percentile262.5
Maximum285
Range280
Interquartile range (IQR)120

Descriptive statistics

Standard deviation95.316765
Coefficient of variation (CV)0.69865692
Kurtosis-0.58428336
Mean136.42857
Median Absolute Deviation (MAD)62
Skewness0.26277656
Sum955
Variance9085.2857
MonotonicityNot monotonic
2024-04-11T11:59:57.487581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
65 1
 
0.2%
84 1
 
0.2%
5 1
 
0.2%
210 1
 
0.2%
285 1
 
0.2%
127 1
 
0.2%
179 1
 
0.2%
(Missing) 455
98.5%
ValueCountFrequency (%)
5 1
0.2%
65 1
0.2%
84 1
0.2%
127 1
0.2%
179 1
0.2%
210 1
0.2%
285 1
0.2%
ValueCountFrequency (%)
285 1
0.2%
210 1
0.2%
179 1
0.2%
127 1
0.2%
84 1
0.2%
65 1
0.2%
5 1
0.2%

(기존)용적률
Text

MISSING 

Distinct187
Distinct (%)45.8%
Missing54
Missing (%)11.7%
Memory size3.7 KiB
2024-04-11T11:59:57.795248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length4.372549
Min length1

Characters and Unicode

Total characters1784
Distinct characters16
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique129 ?
Unique (%)31.6%

Sample

1st row0.55
2nd row0.77
3rd row1.5
4th row2.3
5th row1.04
ValueCountFrequency (%)
100~200 45
 
11.0%
2.5 26
 
6.4%
2 24
 
5.9%
2.3 14
 
3.4%
0.8 12
 
2.9%
1.8 11
 
2.7%
2.4 9
 
2.2%
2.8 6
 
1.5%
1.5 6
 
1.5%
2.1 4
 
1.0%
Other values (178) 252
61.6%
2024-04-11T11:59:58.236387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 342
19.2%
. 311
17.4%
1 279
15.6%
2 228
12.8%
8 105
 
5.9%
5 95
 
5.3%
% 70
 
3.9%
7 68
 
3.8%
~ 65
 
3.6%
3 64
 
3.6%
Other values (6) 157
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1335
74.8%
Other Punctuation 383
 
21.5%
Math Symbol 65
 
3.6%
Space Separator 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 342
25.6%
1 279
20.9%
2 228
17.1%
8 105
 
7.9%
5 95
 
7.1%
7 68
 
5.1%
3 64
 
4.8%
6 52
 
3.9%
4 51
 
3.8%
9 51
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 311
81.2%
% 70
 
18.3%
, 1
 
0.3%
/ 1
 
0.3%
Math Symbol
ValueCountFrequency (%)
~ 65
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1784
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 342
19.2%
. 311
17.4%
1 279
15.6%
2 228
12.8%
8 105
 
5.9%
5 95
 
5.3%
% 70
 
3.9%
7 68
 
3.8%
~ 65
 
3.6%
3 64
 
3.6%
Other values (6) 157
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1784
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 342
19.2%
. 311
17.4%
1 279
15.6%
2 228
12.8%
8 105
 
5.9%
5 95
 
5.3%
% 70
 
3.9%
7 68
 
3.8%
~ 65
 
3.6%
3 64
 
3.6%
Other values (6) 157
8.8%

(신축)용적률
Text

MISSING 

Distinct144
Distinct (%)36.5%
Missing68
Missing (%)14.7%
Memory size3.7 KiB
2024-04-11T11:59:58.508188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.9137056
Min length1

Characters and Unicode

Total characters1542
Distinct characters14
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)25.4%

Sample

1st row2.18
2nd row2.66
3rd row2.3
4th row2.3
5th row2.3
ValueCountFrequency (%)
2.5 69
 
17.5%
2.3 31
 
7.9%
2.9 23
 
5.8%
3 16
 
4.1%
2.6 13
 
3.3%
200%/300 12
 
3.0%
2.4 9
 
2.3%
2.49 8
 
2.0%
2.2 8
 
2.0%
2.7 8
 
2.0%
Other values (134) 197
50.0%
2024-04-11T11:59:58.907277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 411
26.7%
. 336
21.8%
5 127
 
8.2%
9 100
 
6.5%
0 96
 
6.2%
3 94
 
6.1%
4 76
 
4.9%
6 69
 
4.5%
7 62
 
4.0%
8 58
 
3.8%
Other values (4) 113
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1132
73.4%
Other Punctuation 398
 
25.8%
Math Symbol 12
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 411
36.3%
5 127
 
11.2%
9 100
 
8.8%
0 96
 
8.5%
3 94
 
8.3%
4 76
 
6.7%
6 69
 
6.1%
7 62
 
5.5%
8 58
 
5.1%
1 39
 
3.4%
Other Punctuation
ValueCountFrequency (%)
. 336
84.4%
% 46
 
11.6%
/ 16
 
4.0%
Math Symbol
ValueCountFrequency (%)
~ 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1542
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 411
26.7%
. 336
21.8%
5 127
 
8.2%
9 100
 
6.5%
0 96
 
6.2%
3 94
 
6.1%
4 76
 
4.9%
6 69
 
4.5%
7 62
 
4.0%
8 58
 
3.8%
Other values (4) 113
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1542
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 411
26.7%
. 336
21.8%
5 127
 
8.2%
9 100
 
6.5%
0 96
 
6.2%
3 94
 
6.1%
4 76
 
4.9%
6 69
 
4.5%
7 62
 
4.0%
8 58
 
3.8%
Other values (4) 113
 
7.3%

토지등소유자수(명)
Real number (ℝ)

MISSING 

Distinct270
Distinct (%)85.7%
Missing147
Missing (%)31.8%
Infinite0
Infinite (%)0.0%
Mean763.27619
Minimum21
Maximum3988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:59.039759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile99
Q1270.5
median519
Q31021.5
95-th percentile2301.1
Maximum3988
Range3967
Interquartile range (IQR)751

Descriptive statistics

Standard deviation705.44856
Coefficient of variation (CV)0.9242376
Kurtosis3.3081844
Mean763.27619
Median Absolute Deviation (MAD)302
Skewness1.749426
Sum240432
Variance497657.67
MonotonicityNot monotonic
2024-04-11T11:59:59.172144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
124 3
 
0.6%
223 3
 
0.6%
479 3
 
0.6%
110 3
 
0.6%
330 3
 
0.6%
454 3
 
0.6%
99 3
 
0.6%
225 2
 
0.4%
285 2
 
0.4%
962 2
 
0.4%
Other values (260) 288
62.3%
(Missing) 147
31.8%
ValueCountFrequency (%)
21 1
0.2%
37 1
0.2%
47 2
0.4%
51 2
0.4%
53 2
0.4%
75 1
0.2%
78 1
0.2%
79 1
0.2%
80 1
0.2%
81 1
0.2%
ValueCountFrequency (%)
3988 1
0.2%
3669 1
0.2%
3534 1
0.2%
3307 1
0.2%
2973 1
0.2%
2918 1
0.2%
2713 1
0.2%
2647 1
0.2%
2560 1
0.2%
2510 1
0.2%

조합원수(명)
Real number (ℝ)

MISSING 

Distinct205
Distinct (%)93.6%
Missing243
Missing (%)52.6%
Infinite0
Infinite (%)0.0%
Mean675.94064
Minimum17
Maximum3179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:59.350926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile78.7
Q1240.5
median482
Q3906
95-th percentile2007.7
Maximum3179
Range3162
Interquartile range (IQR)665.5

Descriptive statistics

Standard deviation589.81303
Coefficient of variation (CV)0.87258111
Kurtosis2.2743154
Mean675.94064
Median Absolute Deviation (MAD)288
Skewness1.5445976
Sum148031
Variance347879.41
MonotonicityNot monotonic
2024-04-11T11:59:59.479720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
374 3
 
0.6%
1004 2
 
0.4%
110 2
 
0.4%
296 2
 
0.4%
144 2
 
0.4%
438 2
 
0.4%
429 2
 
0.4%
472 2
 
0.4%
267 2
 
0.4%
1110 2
 
0.4%
Other values (195) 198
42.9%
(Missing) 243
52.6%
ValueCountFrequency (%)
17 1
0.2%
37 1
0.2%
42 1
0.2%
45 1
0.2%
50 1
0.2%
54 1
0.2%
56 1
0.2%
63 1
0.2%
71 1
0.2%
75 1
0.2%
ValueCountFrequency (%)
3179 1
0.2%
2595 1
0.2%
2382 1
0.2%
2376 1
0.2%
2353 1
0.2%
2312 1
0.2%
2270 1
0.2%
2198 1
0.2%
2183 1
0.2%
2051 1
0.2%

사업시행자
Categorical

IMBALANCE 

Distinct11
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
조합
217 
<NA>
189 
28 
LH
 
15
추진위
 
5
Other values (6)
 
8

Length

Max length15
Median length2
Mean length2.8614719
Min length1

Unique

Unique5 ?
Unique (%)1.1%

Sample

1st row조합
2nd row조합
3rd row조합
4th row<NA>
5th row조합

Common Values

ValueCountFrequency (%)
조합 217
47.0%
<NA> 189
40.9%
28
 
6.1%
LH 15
 
3.2%
추진위 5
 
1.1%
지정개발자신탁 3
 
0.6%
市+LH 1
 
0.2%
㈜한국토지신탁, ㈜무궁화신탁 1
 
0.2%
코리아신탁 1
 
0.2%
경기주택도시공사 1
 
0.2%

Length

2024-04-11T11:59:59.596881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
조합 217
46.9%
na 189
40.8%
28
 
6.0%
lh 15
 
3.2%
추진위 5
 
1.1%
지정개발자신탁 3
 
0.6%
市+lh 1
 
0.2%
㈜한국토지신탁 1
 
0.2%
㈜무궁화신탁 1
 
0.2%
코리아신탁 1
 
0.2%
Other values (2) 2
 
0.4%

사업예정기간(사업시작)
Real number (ℝ)

MISSING 

Distinct29
Distinct (%)9.8%
Missing167
Missing (%)36.1%
Infinite0
Infinite (%)0.0%
Mean2010.4034
Minimum1996
Maximum2026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:59.696713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1996
5-th percentile2003
Q12006
median2009
Q32015
95-th percentile2022
Maximum2026
Range30
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.1757141
Coefficient of variation (CV)0.0030718781
Kurtosis-0.66047111
Mean2010.4034
Median Absolute Deviation (MAD)4
Skewness0.39509292
Sum593069
Variance38.139444
MonotonicityNot monotonic
2024-04-11T11:59:59.802398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2006 41
 
8.9%
2003 30
 
6.5%
2007 23
 
5.0%
2008 19
 
4.1%
2014 16
 
3.5%
2013 15
 
3.2%
2011 13
 
2.8%
2015 13
 
2.8%
2010 12
 
2.6%
2005 11
 
2.4%
Other values (19) 102
22.1%
(Missing) 167
36.1%
ValueCountFrequency (%)
1996 1
 
0.2%
1997 2
 
0.4%
1999 1
 
0.2%
2000 2
 
0.4%
2001 1
 
0.2%
2002 6
 
1.3%
2003 30
6.5%
2004 5
 
1.1%
2005 11
 
2.4%
2006 41
8.9%
ValueCountFrequency (%)
2026 1
 
0.2%
2024 2
 
0.4%
2023 9
1.9%
2022 5
1.1%
2021 3
 
0.6%
2020 10
2.2%
2019 9
1.9%
2018 9
1.9%
2017 8
1.7%
2016 10
2.2%

사업예정기간(사업완료)
Real number (ℝ)

MISSING 

Distinct27
Distinct (%)11.5%
Missing228
Missing (%)49.4%
Infinite0
Infinite (%)0.0%
Mean2019.6752
Minimum2001
Maximum2032
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-04-11T11:59:59.920866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2009
Q12013.25
median2021
Q32025
95-th percentile2030
Maximum2032
Range31
Interquartile range (IQR)11.75

Descriptive statistics

Standard deviation7.1319014
Coefficient of variation (CV)0.003531212
Kurtosis-0.94237427
Mean2019.6752
Median Absolute Deviation (MAD)5
Skewness-0.27728574
Sum472604
Variance50.864018
MonotonicityNot monotonic
2024-04-11T12:00:00.027432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
2030 27
 
5.8%
2009 23
 
5.0%
2023 20
 
4.3%
2021 18
 
3.9%
2025 16
 
3.5%
2022 15
 
3.2%
2024 11
 
2.4%
2015 10
 
2.2%
2026 9
 
1.9%
2010 9
 
1.9%
Other values (17) 76
 
16.5%
(Missing) 228
49.4%
ValueCountFrequency (%)
2001 1
 
0.2%
2002 1
 
0.2%
2007 2
 
0.4%
2008 4
 
0.9%
2009 23
5.0%
2010 9
 
1.9%
2011 3
 
0.6%
2012 8
 
1.7%
2013 8
 
1.7%
2014 6
 
1.3%
ValueCountFrequency (%)
2032 2
 
0.4%
2030 27
5.8%
2029 2
 
0.4%
2028 4
 
0.9%
2027 5
 
1.1%
2026 9
 
1.9%
2025 16
3.5%
2024 11
2.4%
2023 20
4.3%
2022 15
3.2%
Distinct47
Distinct (%)12.9%
Missing98
Missing (%)21.2%
Memory size3.7 KiB
Minimum2003-03-28 00:00:00
Maximum2023-02-01 00:00:00
2024-04-11T12:00:00.152549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T12:00:00.275601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
Distinct53
Distinct (%)21.3%
Missing213
Missing (%)46.1%
Memory size3.7 KiB
Minimum2006-12-21 00:00:00
Maximum2030-12-31 00:00:00
2024-04-11T12:00:00.398295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T12:00:00.518001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct123
Distinct (%)84.2%
Missing316
Missing (%)68.4%
Memory size3.7 KiB
Minimum2004-07-22 00:00:00
Maximum2023-12-31 00:00:00
2024-04-11T12:00:00.642628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T12:00:00.759082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct214
Distinct (%)76.4%
Missing182
Missing (%)39.4%
Memory size3.7 KiB
Minimum1996-02-03 00:00:00
Maximum2023-12-31 00:00:00
2024-04-11T12:00:00.877797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T12:00:01.309714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct175
Distinct (%)95.1%
Missing278
Missing (%)60.2%
Memory size3.7 KiB
Minimum2006-01-16 00:00:00
Maximum2023-12-19 00:00:00
2024-04-11T12:00:01.422442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T12:00:01.558296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

추진위승인일자
Date

MISSING 

Distinct152
Distinct (%)82.2%
Missing277
Missing (%)60.0%
Memory size3.7 KiB
Minimum2003-07-24 00:00:00
Maximum2024-03-18 00:00:00
2024-04-11T12:00:01.670237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T12:00:01.794600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

예비평가일자
Date

MISSING 

Distinct98
Distinct (%)81.0%
Missing341
Missing (%)73.8%
Memory size3.7 KiB
Minimum1999-10-01 00:00:00
Maximum2023-10-27 00:00:00
2024-04-11T12:00:01.910384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T12:00:02.029822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

안전진단일자
Date

MISSING 

Distinct122
Distinct (%)75.3%
Missing300
Missing (%)64.9%
Memory size3.7 KiB
Minimum1996-01-30 00:00:00
Maximum2024-03-20 00:00:00
2024-04-11T12:00:02.161825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T12:00:02.283999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct206
Distinct (%)93.6%
Missing242
Missing (%)52.4%
Memory size3.7 KiB
Minimum1996-09-26 00:00:00
Maximum2023-09-27 00:00:00
2024-04-11T12:00:02.400375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T12:00:02.518497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct205
Distinct (%)86.1%
Missing224
Missing (%)48.5%
Memory size3.7 KiB
Minimum1996-11-14 00:00:00
Maximum2024-03-21 00:00:00
2024-04-11T12:00:02.628278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T12:00:02.739745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct171
Distinct (%)91.0%
Missing274
Missing (%)59.3%
Memory size3.7 KiB
Minimum2004-01-16 00:00:00
Maximum2024-02-23 00:00:00
2024-04-11T12:00:02.842485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T12:00:02.956420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

착공일자
Date

MISSING 

Distinct181
Distinct (%)95.8%
Missing273
Missing (%)59.1%
Memory size3.7 KiB
Minimum1998-08-28 00:00:00
Maximum2024-01-19 00:00:00
2024-04-11T12:00:03.071958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T12:00:03.191336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

일반분양일자
Date

MISSING 

Distinct139
Distinct (%)95.9%
Missing317
Missing (%)68.6%
Memory size3.7 KiB
Minimum2000-10-10 00:00:00
Maximum2023-12-13 00:00:00
2024-04-11T12:00:03.311136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T12:00:03.430789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

준공일자
Date

MISSING 

Distinct138
Distinct (%)92.6%
Missing313
Missing (%)67.7%
Memory size3.7 KiB
Minimum2001-10-15 00:00:00
Maximum2024-01-15 00:00:00
2024-04-11T12:00:03.562435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T12:00:03.681642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

이전고시일자
Date

MISSING 

Distinct100
Distinct (%)94.3%
Missing356
Missing (%)77.1%
Memory size3.7 KiB
Minimum2007-06-27 00:00:00
Maximum2023-10-30 00:00:00
2024-04-11T12:00:03.801671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T12:00:03.921108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

현추진상황
Text

MISSING 

Distinct93
Distinct (%)21.2%
Missing24
Missing (%)5.2%
Memory size3.7 KiB
2024-04-11T12:00:04.096412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length20
Mean length7.06621
Min length2

Characters and Unicode

Total characters3095
Distinct characters105
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)10.0%

Sample

1st row사업시행계획변경인가 진행 중
2nd row준공
3rd row사업시행계획인가 준비 중
4th row안전진단 용역 수행 중
5th row관리처분계획 수립 완료
ValueCountFrequency (%)
준공 104
 
12.5%
고시 100
 
12.0%
정비예정구역 94
 
11.3%
71
 
8.6%
착공 33
 
4.0%
준비 32
 
3.9%
현지조사 23
 
2.8%
결과안전진단 23
 
2.8%
이전고시 14
 
1.7%
이주 13
 
1.6%
Other values (82) 323
38.9%
2024-04-11T12:00:04.396505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
392
 
12.7%
255
 
8.2%
167
 
5.4%
161
 
5.2%
150
 
4.8%
150
 
4.8%
132
 
4.3%
116
 
3.7%
114
 
3.7%
98
 
3.2%
Other values (95) 1360
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2646
85.5%
Space Separator 392
 
12.7%
Decimal Number 35
 
1.1%
Other Punctuation 19
 
0.6%
Uppercase Letter 1
 
< 0.1%
Dash Punctuation 1
 
< 0.1%
Initial Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
255
 
9.6%
167
 
6.3%
161
 
6.1%
150
 
5.7%
150
 
5.7%
132
 
5.0%
116
 
4.4%
114
 
4.3%
98
 
3.7%
91
 
3.4%
Other values (81) 1212
45.8%
Decimal Number
ValueCountFrequency (%)
2 15
42.9%
0 8
22.9%
3 6
 
17.1%
8 3
 
8.6%
1 2
 
5.7%
6 1
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 10
52.6%
/ 4
 
21.1%
' 3
 
15.8%
, 2
 
10.5%
Space Separator
ValueCountFrequency (%)
392
100.0%
Uppercase Letter
ValueCountFrequency (%)
D 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2646
85.5%
Common 448
 
14.5%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
255
 
9.6%
167
 
6.3%
161
 
6.1%
150
 
5.7%
150
 
5.7%
132
 
5.0%
116
 
4.4%
114
 
4.3%
98
 
3.7%
91
 
3.4%
Other values (81) 1212
45.8%
Common
ValueCountFrequency (%)
392
87.5%
2 15
 
3.3%
. 10
 
2.2%
0 8
 
1.8%
3 6
 
1.3%
/ 4
 
0.9%
8 3
 
0.7%
' 3
 
0.7%
1 2
 
0.4%
, 2
 
0.4%
Other values (3) 3
 
0.7%
Latin
ValueCountFrequency (%)
D 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2646
85.5%
ASCII 448
 
14.5%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
392
87.5%
2 15
 
3.3%
. 10
 
2.2%
0 8
 
1.8%
3 6
 
1.3%
/ 4
 
0.9%
8 3
 
0.7%
' 3
 
0.7%
1 2
 
0.4%
, 2
 
0.4%
Other values (3) 3
 
0.7%
Hangul
ValueCountFrequency (%)
255
 
9.6%
167
 
6.3%
161
 
6.1%
150
 
5.7%
150
 
5.7%
132
 
5.0%
116
 
4.4%
114
 
4.3%
98
 
3.7%
91
 
3.4%
Other values (81) 1212
45.8%
Punctuation
ValueCountFrequency (%)
1
100.0%
Distinct95
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2024-04-11T12:00:04.881749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9935065
Min length2

Characters and Unicode

Total characters1383
Distinct characters87
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

Unique22 ?
Unique (%)4.8%

Sample

1st row김석조
2nd row김석조
3rd row김석조
4th row이상범
5th row이상범
ValueCountFrequency (%)
김명선 29
 
6.3%
전병구 21
 
4.5%
임우섭 17
 
3.7%
이준혁 17
 
3.7%
김성우 16
 
3.5%
김선민 14
 
3.0%
백종승 13
 
2.8%
강석영 13
 
2.8%
김은수 12
 
2.6%
김규희 12
 
2.6%
Other values (85) 298
64.5%
2024-04-11T12:00:05.244723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
148
 
10.7%
95
 
6.9%
75
 
5.4%
45
 
3.3%
45
 
3.3%
44
 
3.2%
41
 
3.0%
40
 
2.9%
33
 
2.4%
33
 
2.4%
Other values (77) 784
56.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1383
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
148
 
10.7%
95
 
6.9%
75
 
5.4%
45
 
3.3%
45
 
3.3%
44
 
3.2%
41
 
3.0%
40
 
2.9%
33
 
2.4%
33
 
2.4%
Other values (77) 784
56.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1383
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
148
 
10.7%
95
 
6.9%
75
 
5.4%
45
 
3.3%
45
 
3.3%
44
 
3.2%
41
 
3.0%
40
 
2.9%
33
 
2.4%
33
 
2.4%
Other values (77) 784
56.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1383
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
148
 
10.7%
95
 
6.9%
75
 
5.4%
45
 
3.3%
45
 
3.3%
44
 
3.2%
41
 
3.0%
40
 
2.9%
33
 
2.4%
33
 
2.4%
Other values (77) 784
56.7%
Distinct92
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2024-04-11T12:00:05.459725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.231602
Min length12

Characters and Unicode

Total characters5651
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)3.9%

Sample

1st row031-8075-3434
2nd row031-8075-3434
3rd row031-8075-3434
4th row031-8075-3432
5th row031-8075-3432
ValueCountFrequency (%)
031-481-2909 29
 
6.3%
031-324-2408 21
 
4.5%
031-5189-2406 17
 
3.7%
032-625-3743 17
 
3.7%
031-481-2388 16
 
3.5%
031-8024-4174 14
 
3.0%
031-729-4163 13
 
2.8%
031-481-2398 13
 
2.8%
031-8045-5065 12
 
2.6%
031-324-3232 12
 
2.6%
Other values (82) 298
64.5%
2024-04-11T12:00:05.787001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 924
16.4%
3 882
15.6%
0 741
13.1%
2 623
11.0%
1 569
10.1%
4 546
9.7%
8 417
7.4%
5 265
 
4.7%
7 251
 
4.4%
9 236
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4727
83.6%
Dash Punctuation 924
 
16.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 882
18.7%
0 741
15.7%
2 623
13.2%
1 569
12.0%
4 546
11.6%
8 417
8.8%
5 265
 
5.6%
7 251
 
5.3%
9 236
 
5.0%
6 197
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 924
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5651
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 924
16.4%
3 882
15.6%
0 741
13.1%
2 623
11.0%
1 569
10.1%
4 546
9.7%
8 417
7.4%
5 265
 
4.7%
7 251
 
4.4%
9 236
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 924
16.4%
3 882
15.6%
0 741
13.1%
2 623
11.0%
1 569
10.1%
4 546
9.7%
8 417
7.4%
5 265
 
4.7%
7 251
 
4.4%
9 236
 
4.2%

비고
Categorical

IMBALANCE 

Distinct34
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
<NA>
308 
신설
 
26
주택과
 
21
재건축과
 
18
재개발과
 
14
Other values (29)
75 

Length

Max length36
Median length4
Mean length5.2792208
Min length2

Unique

Unique21 ?
Unique (%)4.5%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 308
66.7%
신설 26
 
5.6%
주택과 21
 
4.5%
재건축과 18
 
3.9%
재개발과 14
 
3.0%
2030 도정기본계획 재수립 14
 
3.0%
정비계획 수립시기 조정 13
 
2.8%
스마트도시과 13
 
2.8%
도시개발행정과 6
 
1.3%
정비구역해제요청처리불가통지취소, 조합설립인가무효확인소송 진행중 2
 
0.4%
Other values (24) 27
 
5.8%

Length

2024-04-11T12:00:05.918217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 308
53.2%
신설 26
 
4.5%
주택과 21
 
3.6%
재건축과 18
 
3.1%
재개발과 14
 
2.4%
2030 14
 
2.4%
도정기본계획 14
 
2.4%
재수립 14
 
2.4%
정비계획 14
 
2.4%
스마트도시과 13
 
2.2%
Other values (66) 123
 
21.2%

Sample

시군명사업단계사업유형정비구역명위치구역면적(㎡)(기존주택)준공년도(기존주택)동수(기존주택)세대수(기존주택)면적별세대수(40㎡미만)(기존주택)면적별세대수(40~60㎡)(기존주택)면적별세대수(60~85㎡)(기존주택)면적별세대수(85~135㎡)(기존주택)면적별세대수(135㎡이상)사업시행세대수총계조합원분양세대수일반분양세대수임대세대수(신축주택)분양주택수(신축주택)면적별분양주택수(40㎡미만)(신축주택)면적별분양주택수(40~60㎡)(신축주택)면적별분양주택수(60~85㎡)(신축주택)면적별분양주택수(85~135㎡)(신축주택)면적별분양주택수(135㎡이상)(신축주택)임대주택수(신축주택)면적별임대주택수(40㎡미만)(신축주택)면적별임대주택수(40~60㎡)(신축주택)면적별임대주택수(60~85㎡)(기존)용적률(신축)용적률토지등소유자수(명)조합원수(명)사업시행자사업예정기간(사업시작)사업예정기간(사업완료)정비예정구역고시일자정비구역지정예정일자정비계획승인일자정비구역지정일자(최초지정)정비구역지정일자(변경지정)추진위승인일자예비평가일자안전진단일자조합설립인가일자사업시행인가일자관리처분인가일자착공일자일반분양일자준공일자이전고시일자현추진상황업무담당자담당자전화번호비고
0고양시사업시행재개발고양Ⅰ-2구역경기도 고양시 덕양구 고양동 92-1 한양연립 주변16154.01980~19951416010608010<NA>30912914238271720262<NA><NA>3838<NA><NA>0.552.18132129조합2008<NA>2006-12-21<NA><NA>2009-06-092011-06-172007-04-23<NA><NA>2009-11-202011-06-24<NA><NA><NA><NA><NA>사업시행계획변경인가 진행 중김석조031-8075-3434<NA>
1고양시준공재건축원당주공2단지경기도 고양시 성사동 71577755.01986371260<NA>800460<NA><NA>165112521112881363<NA>11768547685288<NA>223650.772.6612601252조합20072010<NA><NA><NA>2006-01-162006-05-22<NA><NA>2003-03-242003-06-302006-05-242006-10-232007-04-232009-11-172009-10-302010-03-30준공김석조031-8075-3434<NA>
2고양시조합설립재개발일산Ⅰ-2구역경기도 고양시 일산동 960-1617738.01940~200199<NA>45<NA><NA>3089817238270<NA>19674<NA><NA>382018<NA>1.52.39998조합2015<NA>2006-12-212008-01-012014-01-142015-04-102017-04-142010-02-01<NA><NA>2019-01-15<NA><NA><NA><NA><NA><NA>사업시행계획인가 준비 중김석조031-8075-3434<NA>
3고양시예정구역재건축관산Ⅱ-1구역경기도 고양시 덕양구 관산동 178-5716404.01980~199284352080235100<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2.32.3435<NA><NA><NA><NA>2014-01-062020-12-31<NA><NA><NA><NA>2019-05-13<NA><NA><NA><NA><NA><NA><NA><NA>안전진단 용역 수행 중이상범031-8075-3432<NA>
4고양시관리처분재건축행신 Ⅱ-1구역경기도 고양시 행신동 173-1행신지구 재건축 주변13062.01980~200313267209012037<NA>272169103<NA>272<NA>132140<NA><NA><NA><NA><NA><NA>1.042.3186169조합2010<NA>2006-12-212006-12-212009-09-012010-03-092017-03-24<NA><NA><NA>2001-09-212022-05-032024-02-23<NA><NA><NA><NA>관리처분계획 수립 완료이상범031-8075-3432<NA>
5고양시착공재건축능곡 2-1 능곡연합경기도 고양시 덕양구 토당동 251-1233263.1198545534<NA>416118<NA><NA>83448927075759<NA>374385<NA><NA>75<NA>75<NA>1.232.8252541489조합2007<NA>2006-12-21<NA><NA>2007-08-272018-12-212003-12-292003-05-282003-11-042007-08-312008-03-142018-05-102020-02-192020-10-16<NA><NA>공사 중이상범031-8075-3432<NA>
6고양시조합설립재개발고양 Ⅰ-1구역경기도 고양시 덕양구 고양동 22-2 제일복지회관 주변24500.01970~20044519615709021<NA>35212316960292<NA>12014032<NA>6060<NA><NA>0.372.22124123조합2009<NA>2006-12-212006-12-212009-06-302009-12-24<NA>2007-03-05<NA><NA>2010-06-18<NA><NA><NA><NA><NA><NA>정비계획 변경 진행 중김석조031-8075-3434<NA>
7고양시추진위원회재개발행신Ⅰ-1구역경기도 고양시 덕양구 행신동 22229320.01980~20128472343180350150<NA>6635335080583<NA>423160<NA><NA>806119<NA>22.3553<NA><NA><NA><NA>2014-01-062020-12-31<NA>2022-12-27<NA>2023-04-06<NA><NA><NA><NA><NA><NA><NA><NA><NA>조합설립인가 준비 중김석조031-8075-3434<NA>
8고양시준공재개발관산1차경기도 고양시 관산동 178-526986.019872133212154<NA>34050290<NA>340<NA>1361026834<NA><NA><NA><NA>1.266.695350조합20072012<NA><NA><NA>2005-09-262007-08-132005-12-05<NA><NA>2006-01-102007-09-212007-12-272011-01-202011-04-282014-02-282014-03-14준공김석조031-8075-3434<NA>
9고양시준공재건축일산Ⅱ-1구역탄현주공경기도 고양시 탄현동 2823728.019891333212154<NA>58939317818571<NA>14139832<NA>18<NA>18<NA>0.952.5393393조합200720122006-12-21<NA><NA>2007-10-22<NA><NA><NA>2003-06-302003-06-302008-12-312009-12-312011-03-042013-05-092013-08-022014-07-11준공김석조031-8075-3434<NA>
시군명사업단계사업유형정비구역명위치구역면적(㎡)(기존주택)준공년도(기존주택)동수(기존주택)세대수(기존주택)면적별세대수(40㎡미만)(기존주택)면적별세대수(40~60㎡)(기존주택)면적별세대수(60~85㎡)(기존주택)면적별세대수(85~135㎡)(기존주택)면적별세대수(135㎡이상)사업시행세대수총계조합원분양세대수일반분양세대수임대세대수(신축주택)분양주택수(신축주택)면적별분양주택수(40㎡미만)(신축주택)면적별분양주택수(40~60㎡)(신축주택)면적별분양주택수(60~85㎡)(신축주택)면적별분양주택수(85~135㎡)(신축주택)면적별분양주택수(135㎡이상)(신축주택)임대주택수(신축주택)면적별임대주택수(40㎡미만)(신축주택)면적별임대주택수(40~60㎡)(신축주택)면적별임대주택수(60~85㎡)(기존)용적률(신축)용적률토지등소유자수(명)조합원수(명)사업시행자사업예정기간(사업시작)사업예정기간(사업완료)정비예정구역고시일자정비구역지정예정일자정비계획승인일자정비구역지정일자(최초지정)정비구역지정일자(변경지정)추진위승인일자예비평가일자안전진단일자조합설립인가일자사업시행인가일자관리처분인가일자착공일자일반분양일자준공일자이전고시일자현추진상황업무담당자담당자전화번호비고
452화성시예정구역재개발정남09경기도 화성시 괘랑리 1163-2211807.01970~1990<NA>94<NA>94<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1.82.2<NA><NA><NA><NA><NA>2022-03-04<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>정비예정구역 고시이준혁031-5189-2406<NA>
453화성시예정구역재개발향남02경기도 화성시 평리 96-622976.01970~1990<NA>154<NA>154<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>180~500%220~800%<NA><NA><NA><NA><NA>2022-03-04<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>정비예정구역 고시이준혁031-5189-2406<NA>
454화성시예정구역재개발향남01경기도 화성시 상신리 62326558.01970~1990<NA>128<NA>128<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>180~200%220~250%<NA><NA><NA><NA><NA>2022-03-04<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>정비예정구역 고시이준혁031-5189-2406<NA>
455화성시예정구역재개발송산01경기도 화성시 사강리 59030275.01970~1990<NA>147<NA>147<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>180~200%220~250%<NA><NA><NA><NA><NA>2022-03-04<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>정비예정구역 고시이준혁031-5189-2406<NA>
456화성시예정구역재개발매송03경기도 화성시 원평리 77-1524506.01970~1990<NA>74<NA>74<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1.82.2<NA><NA><NA><NA><NA>2022-03-04<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>정비예정구역 고시이준혁031-5189-2406<NA>
457화성시예정구역재개발매송02경기도 화성시 천천리 204-166163.01970~1990<NA>258<NA>258<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1.82.2<NA><NA><NA><NA><NA>2022-03-04<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>정비예정구역 고시이준혁031-5189-2406<NA>
458화성시예정구역재개발매송01경기도 화성시 어천리 46932090.01970~1990<NA>161<NA>161<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>180~500%220~800%<NA><NA><NA><NA><NA>2022-03-04<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>정비예정구역 고시이준혁031-5189-2406<NA>
459화성시예정구역재개발우정2-1경기도 화성시 조암리 270-3835944.01970~1990<NA>264<NA>264<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>230~500%270~800%<NA><NA><NA><NA><NA>2022-03-04<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>정비예정구역 고시이준혁031-5189-2406<NA>
460화성시예정구역재개발진안1-2경기도 화성시 진안동 524-711738.01970~1990<NA>42<NA>42<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1.82.2<NA><NA><NA><NA><NA>2022-03-04<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>정비예정구역 고시이준혁031-5189-2406<NA>
461화성시예정구역재개발향남2-3경기도 화성시 발안리133-214726.01970~1990<NA>135<NA>135<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>230~500%270~800%<NA><NA><NA><NA><NA>2022-03-04<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>정비예정구역 고시이준혁031-5189-2406<NA>