Overview

Dataset statistics

Number of variables17
Number of observations246
Missing cells484
Missing cells (%)11.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.4 KiB
Average record size in memory147.5 B

Variable types

Text4
Numeric10
Categorical2
Boolean1

Dataset

Description입주자모집 공고 승인 번호,주택대장 번호,사업명,공급 규모내역,입주예정일,모집공고일,모집공고차수,인가일,건축공사공정현황(%),분양구분,분양세대수,분양기간시작일,분양기간종료일,추첨일,등록일시,분양가상한제여부,분양승인일
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21277/S/1/datasetView.do

Alerts

분양가상한제여부 is highly overall correlated with 모집공고차수High correlation
모집공고차수 is highly overall correlated with 모집공고일 and 8 other fieldsHigh correlation
분양구분 is highly overall correlated with 모집공고차수High correlation
입주예정일 is highly overall correlated with 모집공고일 and 6 other fieldsHigh correlation
모집공고일 is highly overall correlated with 입주예정일 and 7 other fieldsHigh correlation
인가일 is highly overall correlated with 입주예정일 and 2 other fieldsHigh correlation
분양기간시작일 is highly overall correlated with 입주예정일 and 6 other fieldsHigh correlation
분양기간종료일 is highly overall correlated with 입주예정일 and 6 other fieldsHigh correlation
추첨일 is highly overall correlated with 입주예정일 and 6 other fieldsHigh correlation
등록일시 is highly overall correlated with 입주예정일 and 6 other fieldsHigh correlation
분양승인일 is highly overall correlated with 입주예정일 and 6 other fieldsHigh correlation
모집공고차수 is highly imbalanced (66.9%)Imbalance
공급 규모내역 has 15 (6.1%) missing valuesMissing
입주예정일 has 6 (2.4%) missing valuesMissing
모집공고일 has 7 (2.8%) missing valuesMissing
인가일 has 233 (94.7%) missing valuesMissing
분양기간시작일 has 30 (12.2%) missing valuesMissing
분양기간종료일 has 45 (18.3%) missing valuesMissing
추첨일 has 66 (26.8%) missing valuesMissing
등록일시 has 13 (5.3%) missing valuesMissing
분양가상한제여부 has 32 (13.0%) missing valuesMissing
분양승인일 has 37 (15.0%) missing valuesMissing
입주자모집 공고 승인 번호 has unique valuesUnique
건축공사공정현황(%) has 91 (37.0%) zerosZeros
분양세대수 has 72 (29.3%) zerosZeros

Reproduction

Analysis started2024-05-11 05:45:29.621308
Analysis finished2024-05-11 05:45:47.127517
Duration17.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct246
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-05-11T14:45:47.353261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length15.276423
Min length7

Characters and Unicode

Total characters3758
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

Unique246 ?
Unique (%)100.0%

Sample

1st row11230-100023905
2nd row11410-100011784
3rd row11740-100019342
4th row11740-1000000000000000116267
5th row11470-100041691
ValueCountFrequency (%)
11230-100023905 1
 
0.4%
11530-100011627 1
 
0.4%
11560-100008425 1
 
0.4%
11470-100014910 1
 
0.4%
11530-100009088 1
 
0.4%
11305-100006141 1
 
0.4%
11260-100009833 1
 
0.4%
11680-100015362 1
 
0.4%
11530-100010687 1
 
0.4%
11740-100008497 1
 
0.4%
Other values (236) 236
95.9%
2024-05-11T14:45:47.848726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1312
34.9%
1 992
26.4%
- 246
 
6.5%
5 204
 
5.4%
2 196
 
5.2%
4 185
 
4.9%
6 167
 
4.4%
3 157
 
4.2%
7 123
 
3.3%
8 116
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3512
93.5%
Dash Punctuation 246
 
6.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1312
37.4%
1 992
28.2%
5 204
 
5.8%
2 196
 
5.6%
4 185
 
5.3%
6 167
 
4.8%
3 157
 
4.5%
7 123
 
3.5%
8 116
 
3.3%
9 60
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 246
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1312
34.9%
1 992
26.4%
- 246
 
6.5%
5 204
 
5.4%
2 196
 
5.2%
4 185
 
4.9%
6 167
 
4.4%
3 157
 
4.2%
7 123
 
3.3%
8 116
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1312
34.9%
1 992
26.4%
- 246
 
6.5%
5 204
 
5.4%
2 196
 
5.2%
4 185
 
4.9%
6 167
 
4.4%
3 157
 
4.2%
7 123
 
3.3%
8 116
 
3.1%
Distinct219
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-05-11T14:45:48.160490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length14.560976
Min length7

Characters and Unicode

Total characters3582
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

Unique196 ?
Unique (%)79.7%

Sample

1st row11230-100023905
2nd row11410-100011784
3rd row11740-100019342
4th row11740-1000000000000000116267
5th row11470-100041691
ValueCountFrequency (%)
11560-100016986 3
 
1.2%
11560-100008165 3
 
1.2%
11710-100050532 3
 
1.2%
11650-100013831 3
 
1.2%
11215-100006444 2
 
0.8%
11680-100041365 2
 
0.8%
11560-100008166 2
 
0.8%
11290-100016523 2
 
0.8%
11680-100049365 2
 
0.8%
11410-100005936 2
 
0.8%
Other values (209) 222
90.2%
2024-05-11T14:45:48.619513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1095
30.6%
1 965
26.9%
- 246
 
6.9%
5 219
 
6.1%
4 190
 
5.3%
2 188
 
5.2%
6 185
 
5.2%
3 174
 
4.9%
8 130
 
3.6%
7 124
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3336
93.1%
Dash Punctuation 246
 
6.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1095
32.8%
1 965
28.9%
5 219
 
6.6%
4 190
 
5.7%
2 188
 
5.6%
6 185
 
5.5%
3 174
 
5.2%
8 130
 
3.9%
7 124
 
3.7%
9 66
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 246
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3582
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1095
30.6%
1 965
26.9%
- 246
 
6.9%
5 219
 
6.1%
4 190
 
5.3%
2 188
 
5.2%
6 185
 
5.2%
3 174
 
4.9%
8 130
 
3.6%
7 124
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3582
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1095
30.6%
1 965
26.9%
- 246
 
6.9%
5 219
 
6.1%
4 190
 
5.3%
2 188
 
5.2%
6 185
 
5.2%
3 174
 
4.9%
8 130
 
3.6%
7 124
 
3.5%
Distinct221
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-05-11T14:45:48.954613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length30
Mean length16.837398
Min length4

Characters and Unicode

Total characters4142
Distinct characters340
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique201 ?
Unique (%)81.7%

Sample

1st row이문동 한원힐트리움 도시형생활주택 신축공사
2nd row이노와이즈신촌 입주자모집공고
3rd row둔촌 현대수린나 아파트
4th row천호역 마에스트로
5th row어반클라쎄목동아파트
ValueCountFrequency (%)
신축공사 60
 
7.9%
도시형생활주택 48
 
6.3%
입주자모집공고 10
 
1.3%
아파트 10
 
1.3%
도시형 9
 
1.2%
생활주택 9
 
1.2%
송파 8
 
1.1%
공동주택 8
 
1.1%
신축사업 7
 
0.9%
단지형다세대주택 6
 
0.8%
Other values (418) 583
76.9%
2024-05-11T14:45:49.584079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
512
 
12.4%
144
 
3.5%
117
 
2.8%
117
 
2.8%
103
 
2.5%
102
 
2.5%
101
 
2.4%
94
 
2.3%
93
 
2.2%
86
 
2.1%
Other values (330) 2673
64.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3312
80.0%
Space Separator 512
 
12.4%
Decimal Number 133
 
3.2%
Uppercase Letter 48
 
1.2%
Close Punctuation 40
 
1.0%
Open Punctuation 40
 
1.0%
Lowercase Letter 27
 
0.7%
Dash Punctuation 26
 
0.6%
Other Punctuation 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
144
 
4.3%
117
 
3.5%
117
 
3.5%
103
 
3.1%
102
 
3.1%
101
 
3.0%
94
 
2.8%
93
 
2.8%
86
 
2.6%
86
 
2.6%
Other values (282) 2269
68.5%
Uppercase Letter
ValueCountFrequency (%)
B 7
14.6%
L 7
14.6%
K 5
10.4%
A 4
8.3%
S 4
8.3%
C 4
8.3%
I 3
 
6.2%
G 2
 
4.2%
E 2
 
4.2%
P 1
 
2.1%
Other values (9) 9
18.8%
Lowercase Letter
ValueCountFrequency (%)
e 5
18.5%
l 5
18.5%
s 3
11.1%
r 2
 
7.4%
t 2
 
7.4%
i 2
 
7.4%
a 2
 
7.4%
d 1
 
3.7%
u 1
 
3.7%
o 1
 
3.7%
Other values (3) 3
11.1%
Decimal Number
ValueCountFrequency (%)
1 41
30.8%
2 29
21.8%
3 15
 
11.3%
4 12
 
9.0%
6 12
 
9.0%
5 8
 
6.0%
9 5
 
3.8%
8 5
 
3.8%
7 4
 
3.0%
0 2
 
1.5%
Other Punctuation
ValueCountFrequency (%)
, 3
75.0%
. 1
 
25.0%
Space Separator
ValueCountFrequency (%)
512
100.0%
Close Punctuation
ValueCountFrequency (%)
) 40
100.0%
Open Punctuation
ValueCountFrequency (%)
( 40
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3312
80.0%
Common 755
 
18.2%
Latin 75
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
144
 
4.3%
117
 
3.5%
117
 
3.5%
103
 
3.1%
102
 
3.1%
101
 
3.0%
94
 
2.8%
93
 
2.8%
86
 
2.6%
86
 
2.6%
Other values (282) 2269
68.5%
Latin
ValueCountFrequency (%)
B 7
 
9.3%
L 7
 
9.3%
e 5
 
6.7%
l 5
 
6.7%
K 5
 
6.7%
A 4
 
5.3%
S 4
 
5.3%
C 4
 
5.3%
s 3
 
4.0%
I 3
 
4.0%
Other values (22) 28
37.3%
Common
ValueCountFrequency (%)
512
67.8%
1 41
 
5.4%
) 40
 
5.3%
( 40
 
5.3%
2 29
 
3.8%
- 26
 
3.4%
3 15
 
2.0%
4 12
 
1.6%
6 12
 
1.6%
5 8
 
1.1%
Other values (6) 20
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3312
80.0%
ASCII 830
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
512
61.7%
1 41
 
4.9%
) 40
 
4.8%
( 40
 
4.8%
2 29
 
3.5%
- 26
 
3.1%
3 15
 
1.8%
4 12
 
1.4%
6 12
 
1.4%
5 8
 
1.0%
Other values (38) 95
 
11.4%
Hangul
ValueCountFrequency (%)
144
 
4.3%
117
 
3.5%
117
 
3.5%
103
 
3.1%
102
 
3.1%
101
 
3.0%
94
 
2.8%
93
 
2.8%
86
 
2.6%
86
 
2.6%
Other values (282) 2269
68.5%

공급 규모내역
Text

MISSING 

Distinct210
Distinct (%)90.9%
Missing15
Missing (%)6.1%
Memory size2.1 KiB
2024-05-11T14:45:50.074674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length118
Median length43
Mean length26.445887
Min length1

Characters and Unicode

Total characters6109
Distinct characters127
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique194 ?
Unique (%)84.0%

Sample

1st row지하1층 지상6층 2개동 中 도시형생활주택 65세대
2nd row38세대
3rd row34세대
4th row아파트 지하 1층, 지상 12층 총 77세대
5th row45세대
ValueCountFrequency (%)
도시형생활주택 41
 
4.1%
39
 
3.9%
38
 
3.8%
아파트 38
 
3.8%
36
 
3.6%
부대복리시설 29
 
2.9%
1개동 26
 
2.6%
지상 25
 
2.5%
지상6층 22
 
2.2%
지하 19
 
1.9%
Other values (386) 698
69.0%
2024-05-11T14:45:51.127642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
782
 
12.8%
300
 
4.9%
296
 
4.8%
281
 
4.6%
262
 
4.3%
1 249
 
4.1%
2 187
 
3.1%
163
 
2.7%
, 163
 
2.7%
160
 
2.6%
Other values (117) 3266
53.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3482
57.0%
Decimal Number 1301
 
21.3%
Space Separator 782
 
12.8%
Other Punctuation 222
 
3.6%
Open Punctuation 99
 
1.6%
Close Punctuation 99
 
1.6%
Math Symbol 86
 
1.4%
Other Symbol 16
 
0.3%
Dash Punctuation 14
 
0.2%
Uppercase Letter 4
 
0.1%
Other values (3) 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
300
 
8.6%
296
 
8.5%
281
 
8.1%
262
 
7.5%
163
 
4.7%
160
 
4.6%
159
 
4.6%
156
 
4.5%
153
 
4.4%
125
 
3.6%
Other values (89) 1427
41.0%
Decimal Number
ValueCountFrequency (%)
1 249
19.1%
2 187
14.4%
5 137
10.5%
3 128
9.8%
4 127
9.8%
6 114
8.8%
0 102
7.8%
9 91
 
7.0%
7 86
 
6.6%
8 80
 
6.1%
Other Punctuation
ValueCountFrequency (%)
, 163
73.4%
/ 35
 
15.8%
. 17
 
7.7%
: 7
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
B 2
50.0%
A 1
25.0%
C 1
25.0%
Open Punctuation
ValueCountFrequency (%)
( 95
96.0%
[ 4
 
4.0%
Close Punctuation
ValueCountFrequency (%)
) 95
96.0%
] 4
 
4.0%
Space Separator
ValueCountFrequency (%)
782
100.0%
Math Symbol
ValueCountFrequency (%)
~ 86
100.0%
Other Symbol
ValueCountFrequency (%)
16
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Other Number
ValueCountFrequency (%)
² 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3480
57.0%
Common 2621
42.9%
Latin 6
 
0.1%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
300
 
8.6%
296
 
8.5%
281
 
8.1%
262
 
7.5%
163
 
4.7%
160
 
4.6%
159
 
4.6%
156
 
4.5%
153
 
4.4%
125
 
3.6%
Other values (88) 1425
40.9%
Common
ValueCountFrequency (%)
782
29.8%
1 249
 
9.5%
2 187
 
7.1%
, 163
 
6.2%
5 137
 
5.2%
3 128
 
4.9%
4 127
 
4.8%
6 114
 
4.3%
0 102
 
3.9%
( 95
 
3.6%
Other values (14) 537
20.5%
Latin
ValueCountFrequency (%)
m 2
33.3%
B 2
33.3%
A 1
16.7%
C 1
16.7%
Han
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3480
57.0%
ASCII 2610
42.7%
CJK Compat 16
 
0.3%
CJK 2
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
782
30.0%
1 249
 
9.5%
2 187
 
7.2%
, 163
 
6.2%
5 137
 
5.2%
3 128
 
4.9%
4 127
 
4.9%
6 114
 
4.4%
0 102
 
3.9%
( 95
 
3.6%
Other values (16) 526
20.2%
Hangul
ValueCountFrequency (%)
300
 
8.6%
296
 
8.5%
281
 
8.1%
262
 
7.5%
163
 
4.7%
160
 
4.6%
159
 
4.6%
156
 
4.5%
153
 
4.4%
125
 
3.6%
Other values (88) 1425
40.9%
CJK Compat
ValueCountFrequency (%)
16
100.0%
CJK
ValueCountFrequency (%)
2
100.0%
None
ValueCountFrequency (%)
² 1
100.0%

입주예정일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct191
Distinct (%)79.6%
Missing6
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean20087092
Minimum202303
Maximum20251128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-11T14:45:51.372471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum202303
5-th percentile20100963
Q120137908
median20180120
Q320210430
95-th percentile20240302
Maximum20251128
Range20048825
Interquartile range (IQR)72521.75

Descriptive statistics

Standard deviation1289854.5
Coefficient of variation (CV)0.064213102
Kurtosis239.30261
Mean20087092
Median Absolute Deviation (MAD)30911
Skewness-15.458344
Sum4.8209022 × 109
Variance1.6637247 × 1012
MonotonicityNot monotonic
2024-05-11T14:45:51.612866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20140930 5
 
2.0%
20211031 3
 
1.2%
20131030 3
 
1.2%
20180130 3
 
1.2%
20240131 3
 
1.2%
20110131 3
 
1.2%
20160930 3
 
1.2%
20210831 3
 
1.2%
20181231 3
 
1.2%
20160320 2
 
0.8%
Other values (181) 209
85.0%
(Missing) 6
 
2.4%
ValueCountFrequency (%)
202303 1
0.4%
19990731 1
0.4%
20001001 1
0.4%
20010731 1
0.4%
20021031 2
0.8%
20030531 1
0.4%
20040731 1
0.4%
20050301 1
0.4%
20070531 1
0.4%
20090930 1
0.4%
ValueCountFrequency (%)
20251128 1
0.4%
20250930 1
0.4%
20250731 1
0.4%
20250630 1
0.4%
20250201 1
0.4%
20241130 1
0.4%
20241030 1
0.4%
20240930 1
0.4%
20240521 1
0.4%
20240428 1
0.4%

모집공고일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct211
Distinct (%)88.3%
Missing7
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean20159530
Minimum19970528
Maximum20240430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-11T14:45:51.865883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19970528
5-th percentile20090894
Q120130264
median20170621
Q320190768
95-th percentile20230432
Maximum20240430
Range269902
Interquartile range (IQR)60503.5

Descriptive statistics

Standard deviation48637.958
Coefficient of variation (CV)0.0024126534
Kurtosis2.0286153
Mean20159530
Median Absolute Deviation (MAD)30010
Skewness-1.0960459
Sum4.8181276 × 109
Variance2.3656509 × 109
MonotonicityNot monotonic
2024-05-11T14:45:52.102237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20121019 5
 
2.0%
20000427 3
 
1.2%
20181228 3
 
1.2%
20170714 3
 
1.2%
20191220 2
 
0.8%
20200520 2
 
0.8%
20130610 2
 
0.8%
20160312 2
 
0.8%
20131114 2
 
0.8%
20200619 2
 
0.8%
Other values (201) 213
86.6%
(Missing) 7
 
2.8%
ValueCountFrequency (%)
19970528 1
 
0.4%
19990823 1
 
0.4%
20000427 3
1.2%
20011128 2
0.8%
20021229 1
 
0.4%
20050325 1
 
0.4%
20070831 1
 
0.4%
20090209 1
 
0.4%
20090805 1
 
0.4%
20090904 1
 
0.4%
ValueCountFrequency (%)
20240430 1
0.4%
20240422 1
0.4%
20240229 1
0.4%
20240223 1
0.4%
20240112 1
0.4%
20231219 1
0.4%
20231103 1
0.4%
20231025 1
0.4%
20230927 1
0.4%
20230816 1
0.4%

모집공고차수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
0
231 
1
 
15

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 (%)
0 231
93.9%
1 15
 
6.1%

Length

2024-05-11T14:45:52.346995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:45:52.548538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 231
93.9%
1 15
 
6.1%

인가일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)76.9%
Missing233
Missing (%)94.7%
Infinite0
Infinite (%)0.0%
Mean20006895
Minimum19970523
Maximum20050531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-11T14:45:52.721459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19970523
5-th percentile19976825
Q120000425
median20000526
Q320021127
95-th percentile20038948
Maximum20050531
Range80008
Interquartile range (IQR)20702

Descriptive statistics

Standard deviation21109.715
Coefficient of variation (CV)0.001055122
Kurtosis0.46797283
Mean20006895
Median Absolute Deviation (MAD)10600
Skewness0.34258508
Sum2.6008963 × 108
Variance4.4562006 × 108
MonotonicityNot monotonic
2024-05-11T14:45:52.930545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
20000425 3
 
1.2%
20021127 2
 
0.8%
19981026 1
 
0.4%
19990823 1
 
0.4%
20011126 1
 
0.4%
20031226 1
 
0.4%
20010324 1
 
0.4%
20050531 1
 
0.4%
19970523 1
 
0.4%
20000526 1
 
0.4%
(Missing) 233
94.7%
ValueCountFrequency (%)
19970523 1
 
0.4%
19981026 1
 
0.4%
19990823 1
 
0.4%
20000425 3
1.2%
20000526 1
 
0.4%
20010324 1
 
0.4%
20011126 1
 
0.4%
20021127 2
0.8%
20031226 1
 
0.4%
20050531 1
 
0.4%
ValueCountFrequency (%)
20050531 1
 
0.4%
20031226 1
 
0.4%
20021127 2
0.8%
20011126 1
 
0.4%
20010324 1
 
0.4%
20000526 1
 
0.4%
20000425 3
1.2%
19990823 1
 
0.4%
19981026 1
 
0.4%
19970523 1
 
0.4%

건축공사공정현황(%)
Real number (ℝ)

ZEROS 

Distinct57
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.652866
Minimum0
Maximum100
Zeros91
Zeros (%)37.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-11T14:45:53.267881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7.185
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)100

Descriptive statistics

Standard deviation46.952096
Coefficient of variation (CV)1.1007958
Kurtosis-1.8418642
Mean42.652866
Median Absolute Deviation (MAD)7.185
Skewness0.30785267
Sum10492.605
Variance2204.4993
MonotonicityNot monotonic
2024-05-11T14:45:53.520382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 91
37.0%
100.0 86
35.0%
0.1 7
 
2.8%
1.0 3
 
1.2%
0.14 2
 
0.8%
11.99 2
 
0.8%
92.0 2
 
0.8%
0.001 2
 
0.8%
72.14 2
 
0.8%
2.0 2
 
0.8%
Other values (47) 47
19.1%
ValueCountFrequency (%)
0.0 91
37.0%
0.001 2
 
0.8%
0.01 1
 
0.4%
0.02 1
 
0.4%
0.1 7
 
2.8%
0.13 1
 
0.4%
0.14 2
 
0.8%
0.17 1
 
0.4%
0.2 1
 
0.4%
0.3 1
 
0.4%
ValueCountFrequency (%)
100.0 86
35.0%
99.0 1
 
0.4%
98.0 1
 
0.4%
92.0 2
 
0.8%
90.0 1
 
0.4%
85.0 1
 
0.4%
84.0 1
 
0.4%
81.89 1
 
0.4%
80.62 1
 
0.4%
80.26 1
 
0.4%

분양구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
분양
201 
임대
28 
<NA>
 
17

Length

Max length4
Median length2
Mean length2.1382114
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row분양
2nd row임대
3rd row분양
4th row분양
5th row분양

Common Values

ValueCountFrequency (%)
분양 201
81.7%
임대 28
 
11.4%
<NA> 17
 
6.9%

Length

2024-05-11T14:45:53.732095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:45:53.899473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
분양 201
81.7%
임대 28
 
11.4%
na 17
 
6.9%

분양세대수
Real number (ℝ)

ZEROS 

Distinct100
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.06911
Minimum0
Maximum1690
Zeros72
Zeros (%)29.3%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-11T14:45:54.097020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median46
Q3124.75
95-th percentile472.25
Maximum1690
Range1690
Interquartile range (IQR)124.75

Descriptive statistics

Standard deviation193.07799
Coefficient of variation (CV)1.8552863
Kurtosis22.753609
Mean104.06911
Median Absolute Deviation (MAD)46
Skewness4.0432106
Sum25601
Variance37279.109
MonotonicityNot monotonic
2024-05-11T14:45:54.340189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 72
29.3%
1 26
 
10.6%
149 7
 
2.8%
35 6
 
2.4%
60 5
 
2.0%
146 5
 
2.0%
100 4
 
1.6%
123 3
 
1.2%
70 3
 
1.2%
130 3
 
1.2%
Other values (90) 112
45.5%
ValueCountFrequency (%)
0 72
29.3%
1 26
 
10.6%
4 2
 
0.8%
5 3
 
1.2%
7 1
 
0.4%
8 1
 
0.4%
19 1
 
0.4%
27 1
 
0.4%
30 1
 
0.4%
32 1
 
0.4%
ValueCountFrequency (%)
1690 1
0.4%
999 2
0.8%
771 1
0.4%
730 1
0.4%
709 1
0.4%
700 1
0.4%
689 1
0.4%
593 1
0.4%
509 1
0.4%
495 1
0.4%

분양기간시작일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct189
Distinct (%)87.5%
Missing30
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean20164864
Minimum20070905
Maximum20240430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-11T14:45:54.632327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20070905
5-th percentile20101107
Q120130690
median20170720
Q320190847
95-th percentile20230675
Maximum20240430
Range169525
Interquartile range (IQR)60156.25

Descriptive statistics

Standard deviation38876.02
Coefficient of variation (CV)0.0019279088
Kurtosis-0.89559511
Mean20164864
Median Absolute Deviation (MAD)29991.5
Skewness-0.09337564
Sum4.3556107 × 109
Variance1.5113449 × 109
MonotonicityNot monotonic
2024-05-11T14:45:54.924644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20121019 5
 
2.0%
20160902 3
 
1.2%
20110824 2
 
0.8%
20190605 2
 
0.8%
20190709 2
 
0.8%
20181228 2
 
0.8%
20191226 2
 
0.8%
20130611 2
 
0.8%
20200520 2
 
0.8%
20200629 2
 
0.8%
Other values (179) 192
78.0%
(Missing) 30
 
12.2%
ValueCountFrequency (%)
20070905 1
0.4%
20090216 1
0.4%
20090805 1
0.4%
20090915 1
0.4%
20091027 1
0.4%
20100910 2
0.8%
20101102 2
0.8%
20101104 2
0.8%
20101108 2
0.8%
20110310 1
0.4%
ValueCountFrequency (%)
20240430 1
0.4%
20240422 1
0.4%
20240311 1
0.4%
20240304 1
0.4%
20240112 1
0.4%
20231219 1
0.4%
20231113 1
0.4%
20231025 1
0.4%
20230927 1
0.4%
20230816 1
0.4%

분양기간종료일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct173
Distinct (%)86.1%
Missing45
Missing (%)18.3%
Infinite0
Infinite (%)0.0%
Mean20171425
Minimum20070907
Maximum20250814
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-11T14:45:55.177964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20070907
5-th percentile20101110
Q120140930
median20180202
Q320200612
95-th percentile20231113
Maximum20250814
Range179907
Interquartile range (IQR)59682

Descriptive statistics

Standard deviation39698.608
Coefficient of variation (CV)0.0019680617
Kurtosis-0.71787797
Mean20171425
Median Absolute Deviation (MAD)29979
Skewness-0.17604406
Sum4.0544564 × 109
Variance1.5759795 × 109
MonotonicityNot monotonic
2024-05-11T14:45:55.454311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20140930 5
 
2.0%
20180331 3
 
1.2%
20131231 3
 
1.2%
20181231 3
 
1.2%
20190630 3
 
1.2%
20211031 2
 
0.8%
20230131 2
 
0.8%
20200529 2
 
0.8%
20191230 2
 
0.8%
20191228 2
 
0.8%
Other values (163) 174
70.7%
(Missing) 45
 
18.3%
ValueCountFrequency (%)
20070907 1
0.4%
20090217 1
0.4%
20090831 1
0.4%
20090930 1
0.4%
20091029 1
0.4%
20100910 2
0.8%
20101102 1
0.4%
20101104 1
0.4%
20101110 2
0.8%
20110310 1
0.4%
ValueCountFrequency (%)
20250814 1
0.4%
20250429 1
0.4%
20241231 2
0.8%
20240930 1
0.4%
20240313 1
0.4%
20240306 1
0.4%
20240228 1
0.4%
20240131 1
0.4%
20240130 1
0.4%
20231113 1
0.4%

추첨일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct170
Distinct (%)94.4%
Missing66
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean20169183
Minimum20070914
Maximum20240510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-11T14:45:55.686361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20070914
5-th percentile20101112
Q120140690
median20171176
Q320200132
95-th percentile20230911
Maximum20240510
Range169596
Interquartile range (IQR)59441.75

Descriptive statistics

Standard deviation38725.692
Coefficient of variation (CV)0.0019200427
Kurtosis-0.71378557
Mean20169183
Median Absolute Deviation (MAD)29447.5
Skewness-0.22713549
Sum3.6304529 × 109
Variance1.4996792 × 109
MonotonicityNot monotonic
2024-05-11T14:45:55.920608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20180329 2
 
0.8%
20200103 2
 
0.8%
20200623 2
 
0.8%
20210106 2
 
0.8%
20151007 2
 
0.8%
20121019 2
 
0.8%
20190110 2
 
0.8%
20160319 2
 
0.8%
20100910 2
 
0.8%
20101112 2
 
0.8%
Other values (160) 160
65.0%
(Missing) 66
26.8%
ValueCountFrequency (%)
20070914 1
0.4%
20090220 1
0.4%
20090820 1
0.4%
20090930 1
0.4%
20091027 1
0.4%
20100910 2
0.8%
20101101 1
0.4%
20101102 1
0.4%
20101112 2
0.8%
20110311 1
0.4%
ValueCountFrequency (%)
20240510 1
0.4%
20240422 1
0.4%
20240320 1
0.4%
20240313 1
0.4%
20240131 1
0.4%
20240108 1
0.4%
20231113 1
0.4%
20231106 1
0.4%
20231018 1
0.4%
20230905 1
0.4%

등록일시
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct224
Distinct (%)96.1%
Missing13
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean2.015613 × 1013
Minimum2.0080314 × 1013
Maximum2.0220711 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-11T14:45:56.152406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0080314 × 1013
5-th percentile2.0080411 × 1013
Q12.0121019 × 1013
median2.0160822 × 1013
Q32.0190322 × 1013
95-th percentile2.0210858 × 1013
Maximum2.0220711 × 1013
Range1.4039694 × 1011
Interquartile range (IQR)6.930296 × 1010

Descriptive statistics

Standard deviation3.9432962 × 1010
Coefficient of variation (CV)0.0019563757
Kurtosis-0.99254205
Mean2.015613 × 1013
Median Absolute Deviation (MAD)3.0083943 × 1010
Skewness-0.32005132
Sum4.6963782 × 1015
Variance1.5549585 × 1021
MonotonicityDecreasing
2024-05-11T14:45:56.428385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20080314233140 6
 
2.4%
20080314200638 4
 
1.6%
20080412014203 2
 
0.8%
20131119134421 1
 
0.4%
20150115175848 1
 
0.4%
20141128141202 1
 
0.4%
20141124173858 1
 
0.4%
20141121104429 1
 
0.4%
20140724144943 1
 
0.4%
20140709175935 1
 
0.4%
Other values (214) 214
87.0%
(Missing) 13
 
5.3%
ValueCountFrequency (%)
20080314200638 4
1.6%
20080314233140 6
2.4%
20080315003304 1
 
0.4%
20080411214634 1
 
0.4%
20080411215838 1
 
0.4%
20080412014203 2
 
0.8%
20090206165144 1
 
0.4%
20090731105158 1
 
0.4%
20090904090115 1
 
0.4%
20091201173711 1
 
0.4%
ValueCountFrequency (%)
20220711140719 1
0.4%
20220622175108 1
0.4%
20220415093333 1
0.4%
20220322160536 1
0.4%
20220211174911 1
0.4%
20220124173505 1
0.4%
20211229145810 1
0.4%
20211209132901 1
0.4%
20211207184424 1
0.4%
20211021180840 1
0.4%

분양가상한제여부
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.9%
Missing32
Missing (%)13.0%
Memory size624.0 B
False
179 
True
35 
(Missing)
32 
ValueCountFrequency (%)
False 179
72.8%
True 35
 
14.2%
(Missing) 32
 
13.0%
2024-05-11T14:45:56.696930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

분양승인일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct183
Distinct (%)87.6%
Missing37
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean20169593
Minimum20110525
Maximum20240429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-11T14:45:56.931839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20110525
5-th percentile20111121
Q120140610
median20171120
Q320191107
95-th percentile20230507
Maximum20240429
Range129904
Interquartile range (IQR)50497

Descriptive statistics

Standard deviation34647.012
Coefficient of variation (CV)0.0017177844
Kurtosis-0.91857508
Mean20169593
Median Absolute Deviation (MAD)29307
Skewness-0.024404737
Sum4.2154449 × 109
Variance1.2004154 × 109
MonotonicityNot monotonic
2024-05-11T14:45:57.237506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20121019 5
 
2.0%
20180112 3
 
1.2%
20151001 2
 
0.8%
20130607 2
 
0.8%
20200619 2
 
0.8%
20131114 2
 
0.8%
20200427 2
 
0.8%
20141124 2
 
0.8%
20190605 2
 
0.8%
20151221 2
 
0.8%
Other values (173) 185
75.2%
(Missing) 37
 
15.0%
ValueCountFrequency (%)
20110525 2
0.8%
20110617 1
0.4%
20110810 1
0.4%
20110817 2
0.8%
20110826 1
0.4%
20110930 1
0.4%
20111004 1
0.4%
20111111 1
0.4%
20111116 1
0.4%
20111128 1
0.4%
ValueCountFrequency (%)
20240429 1
0.4%
20240422 1
0.4%
20240228 1
0.4%
20240223 1
0.4%
20231227 1
0.4%
20231219 1
0.4%
20231103 1
0.4%
20231017 1
0.4%
20230926 1
0.4%
20230807 1
0.4%

Interactions

2024-05-11T14:45:44.874622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:30.597616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:32.440369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:34.296526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:35.602439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:37.064480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:38.594900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:40.026695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:41.947749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:43.436154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:45.027353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:30.756712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:32.618977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:34.456586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:35.751778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:37.239249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:38.763299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:40.186922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:42.094301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:43.595881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:45.160370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:30.893700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:32.785010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:34.602206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:35.919589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:37.381788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:38.873528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:40.693772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:42.218372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:43.728308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:45.294369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:31.033951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:32.929005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:34.756565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:36.105163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:37.549355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:38.990805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:40.827759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:42.336077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:43.870244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:45.442419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:31.505098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:33.083540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:34.888328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:36.281318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:37.686217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:39.126283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:40.987671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:42.478153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:44.002629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:45.621095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:31.674430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:33.245534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:34.987445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:36.431541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:37.824933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:39.285247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:41.151790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:42.619644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:44.143955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:45.755924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:31.834449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:33.415481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:35.089263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:36.563967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:37.958254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:39.442243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:41.328956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:42.760214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:44.303922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:45.865309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:31.994775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:33.673301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:35.208061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:36.680846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:38.106143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:39.637280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:41.497515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:42.946575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:44.439315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:45.976126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:32.147325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:33.962600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:35.327026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:36.801969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:38.259602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:39.761559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:41.666117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:43.134343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:44.598405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:46.097757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:32.297926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:34.137152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:35.486378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:36.933980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:38.428797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:39.884336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:41.812537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:43.294488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:45:44.736722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T14:45:57.769529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
입주예정일모집공고일모집공고차수인가일건축공사공정현황(%)분양구분분양세대수분양기간시작일분양기간종료일추첨일등록일시분양가상한제여부분양승인일
입주예정일1.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
모집공고일NaN1.0001.0001.0000.0900.1470.0000.9970.9490.9950.9510.1270.996
모집공고차수NaN1.0001.000NaN0.177NaN0.000NaNNaNNaN0.966NaNNaN
인가일NaN1.000NaN1.000NaNNaNNaNNaNNaNNaNNaNNaNNaN
건축공사공정현황(%)NaN0.0900.177NaN1.0000.2570.2290.4480.3030.4280.0280.2810.275
분양구분NaN0.147NaNNaN0.2571.0000.0740.1650.1950.1260.2800.0000.231
분양세대수NaN0.0000.000NaN0.2290.0741.0000.2940.1720.3530.2670.3480.327
분양기간시작일NaN0.997NaNNaN0.4480.1650.2941.0000.9711.0000.9610.2710.979
분양기간종료일NaN0.949NaNNaN0.3030.1950.1720.9711.0000.9720.8880.0000.894
추첨일NaN0.995NaNNaN0.4280.1260.3531.0000.9721.0000.9540.3520.972
등록일시NaN0.9510.966NaN0.0280.2800.2670.9610.8880.9541.0000.1970.964
분양가상한제여부NaN0.127NaNNaN0.2810.0000.3480.2710.0000.3520.1971.0000.324
분양승인일NaN0.996NaNNaN0.2750.2310.3270.9790.8940.9720.9640.3241.000
2024-05-11T14:45:58.003320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분양가상한제여부모집공고차수분양구분
분양가상한제여부1.0001.0000.000
모집공고차수1.0001.0001.000
분양구분0.0001.0001.000
2024-05-11T14:45:58.164162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
입주예정일모집공고일인가일건축공사공정현황(%)분양세대수분양기간시작일분양기간종료일추첨일등록일시분양승인일모집공고차수분양구분분양가상한제여부
입주예정일1.0000.9490.748-0.0520.0310.9430.9420.9350.9420.9360.0000.0000.000
모집공고일0.9491.0001.0000.095-0.0341.0000.9841.0000.9990.9960.9830.1550.154
인가일0.7481.0001.000NaNNaNNaNNaNNaN-0.074NaN1.0000.0000.000
건축공사공정현황(%)-0.0520.095NaN1.000-0.1240.047-0.0020.0120.102-0.0280.1740.2430.306
분양세대수0.031-0.034NaN-0.1241.000-0.125-0.143-0.1450.031-0.1270.0000.0780.368
분양기간시작일0.9431.000NaN0.047-0.1251.0000.9841.0000.9990.9961.0000.1480.200
분양기간종료일0.9420.984NaN-0.002-0.1430.9841.0000.9830.9800.9771.0000.1790.000
추첨일0.9351.000NaN0.012-0.1451.0000.9831.0001.0000.9951.0000.1000.259
등록일시0.9420.999-0.0740.1020.0310.9990.9801.0001.0000.9950.8600.2100.145
분양승인일0.9360.996NaN-0.028-0.1270.9960.9770.9950.9951.0001.0000.1780.237
모집공고차수0.0000.9831.0000.1740.0001.0001.0001.0000.8601.0001.0001.0001.000
분양구분0.0000.1550.0000.2430.0780.1480.1790.1000.2100.1781.0001.0000.000
분양가상한제여부0.0000.1540.0000.3060.3680.2000.0000.2590.1450.2371.0000.0001.000

Missing values

2024-05-11T14:45:46.318423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T14:45:46.641319image/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.
2024-05-11T14:45:46.932694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

입주자모집 공고 승인 번호주택대장 번호사업명공급 규모내역입주예정일모집공고일모집공고차수인가일건축공사공정현황(%)분양구분분양세대수분양기간시작일분양기간종료일추첨일등록일시분양가상한제여부분양승인일
011230-10002390511230-100023905이문동 한원힐트리움 도시형생활주택 신축공사지하1층 지상6층 2개동 中 도시형생활주택 65세대20231231202311030<NA>80.26분양65202311132023111320231113<NA>N20231103
111410-10001178411410-100011784이노와이즈신촌 입주자모집공고38세대20240428202304100<NA>51.0임대0202304202023042120230426<NA>N20230324
211740-10001934211740-100019342둔촌 현대수린나 아파트34세대20231031202306300<NA>100.0분양0202306302023080220230717<NA>N20230629
311740-100000000000000011626711740-1000000000000000116267천호역 마에스트로아파트 지하 1층, 지상 12층 총 77세대20250201202309270<NA>1.0분양1202309272024123120231018<NA>N20230926
411470-10004169111470-100041691어반클라쎄목동아파트45세대20240130202312190<NA>100.0분양5202312192024013020240108<NA>N20231219
511200-10003981411200-100039814성동구 용답동 역세권 장기전세주택 신축공사(청계 SK VIEW)지하5층~지상29~34층, 총 396세대(일반분양 108세대)20250731202308090<NA>29.3분양1202308092023091320230828<NA>N20230807
611740-10002748311740-100027483에스아이팰리스 올림픽공원공동주택(아파트 58세대)20240325202402230<NA>100.0임대58202403042024030620240313<NA>N20240223
711215-10002416811215-100024168포제스한강B3~15층, 3개동, 128세대20250930202401120<NA>24.82분양128202401122025081420240131<NA>N20231227
811230-100000010000000000490111230-100023905이문동 한원힐트리움 도시형생활주택 신축공사지하1층 지상6층 2개동 中 근린생활시설 (5개실)20240422202404220<NA>100.0<NA>5202404222024123120240422<NA>N20240422
911740-10002270311740-100022703더샵 둔촌포레 공동주택(둔촌현대1차 아파트 리모델링)공동주택(아파트) 572세대 중 조합 498세대 제외 한 74세대20241130202402290<NA>66.3임대74202403112024031320240320<NA>N20240228
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23611320-9511320-95청한파크재건축조합<NA><NA><NA>1200312260.0<NA>0<NA><NA><NA>20080314233140<NA><NA>
23711320-2111320-21방학동 삼성아파트(B단지)<NA>20021031200004271200004250.0<NA>0<NA><NA><NA>20080314233140<NA><NA>
23811320-2211320-22방학동 삼성아파트(A단지)<NA>20021031200004271200004250.0<NA>0<NA><NA><NA>20080314233140<NA><NA>
23911320-411320-4방학동 효성아파트<NA><NA><NA>1200103240.0<NA>0<NA><NA><NA>20080314233140<NA><NA>
24011320-811320-8창동북한산아이파크<NA>20040731200111281<NA>0.0<NA>0<NA><NA><NA>20080314233140<NA><NA>
24111320-1811320-18쌍문동 미화아파트<NA>20070531200503251<NA>0.0<NA>0<NA><NA><NA>20080314233140<NA><NA>
24211305-1311305-13우이동포시즌빌리지<NA><NA><NA>1200505310.0<NA>0<NA><NA><NA>20080314200638<NA><NA>
24311305-311305-3미아동 신구아파트 신축공사<NA>19990731199705281199705230.0<NA>0<NA><NA><NA>20080314200638<NA><NA>
24411305-911305-9미아타운재건축주택조합<NA><NA><NA>1200005260.0<NA>0<NA><NA><NA>20080314200638<NA><NA>
24511305-1111305-11미아아파트재건축조합<NA>20030531200004271200004250.0<NA>0<NA><NA><NA>20080314200638<NA><NA>