Overview

Dataset statistics

Number of variables47
Number of observations2788
Missing cells11415
Missing cells (%)8.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory390.0 B

Variable types

Numeric13
Text15
Categorical12
DateTime5
Boolean2

Dataset

Description번호,k-아파트코드,k-아파트명,k-단지분류(아파트,주상복합등등),kapt도로명주소,주소(시도)k-apt주소split,주소(시군구),주소(읍면동),나머지주소,주소(도로명),주소(도로상세주소),k-전화번호,k-팩스번호,단지소개기존clob,단지첨부파일,k-세대타입(분양형태),k-관리방식,k-복도유형,k-난방방식,k-전체동수,k-전체세대수,k-건설사(시공사),k-시행사,k-사용검사일-사용승인일,k-연면적,k-주거전용면적,k-관리비부과면적,k-전용면적별세대현황(60㎡이하),k-전용면적별세대현황(60㎡~85㎡이하),k-85㎡~135㎡이하,k-135㎡초과,k-홈페이지,k-등록일자,k-수정일자,고용보험관리번호,경비비관리형태,세대전기계약방법,청소비관리형태,건축면적,주차대수,기타/의무/임대/임의=1/2/3/4,단지승인일,사용허가여부,관리비 업로드,좌표X,좌표Y,단지신청일
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15818/S/1/datasetView.do

Alerts

사용허가여부 has constant value ""Constant
k-단지분류(아파트,주상복합등등) is highly imbalanced (77.8%)Imbalance
주소(시도)k-apt주소split is highly imbalanced (89.8%)Imbalance
k-세대타입(분양형태) is highly imbalanced (59.0%)Imbalance
k-관리방식 is highly imbalanced (71.3%)Imbalance
k-135㎡초과 is highly imbalanced (98.9%)Imbalance
경비비관리형태 is highly imbalanced (53.5%)Imbalance
청소비관리형태 is highly imbalanced (63.2%)Imbalance
기타/의무/임대/임의=1/2/3/4 is highly imbalanced (56.0%)Imbalance
관리비 업로드 is highly imbalanced (88.5%)Imbalance
kapt도로명주소 has 131 (4.7%) missing valuesMissing
나머지주소 has 656 (23.5%) missing valuesMissing
주소(도로명) has 113 (4.1%) missing valuesMissing
주소(도로상세주소) has 120 (4.3%) missing valuesMissing
k-팩스번호 has 74 (2.7%) missing valuesMissing
단지소개기존clob has 2222 (79.7%) missing valuesMissing
단지첨부파일 has 2604 (93.4%) missing valuesMissing
k-건설사(시공사) has 41 (1.5%) missing valuesMissing
k-시행사 has 55 (2.0%) missing valuesMissing
k-주거전용면적 has 31 (1.1%) missing valuesMissing
k-전용면적별세대현황(60㎡이하) has 29 (1.0%) missing valuesMissing
k-전용면적별세대현황(60㎡~85㎡이하) has 29 (1.0%) missing valuesMissing
k-85㎡~135㎡이하 has 29 (1.0%) missing valuesMissing
k-홈페이지 has 1970 (70.7%) missing valuesMissing
k-등록일자 has 2302 (82.6%) missing valuesMissing
고용보험관리번호 has 685 (24.6%) missing valuesMissing
건축면적 has 47 (1.7%) missing valuesMissing
주차대수 has 37 (1.3%) missing valuesMissing
단지승인일 has 56 (2.0%) missing valuesMissing
좌표X has 40 (1.4%) missing valuesMissing
좌표Y has 40 (1.4%) missing valuesMissing
단지신청일 has 29 (1.0%) missing valuesMissing
k-연면적 is highly skewed (γ1 = 36.86600386)Skewed
건축면적 is highly skewed (γ1 = 51.38743408)Skewed
번호 has unique valuesUnique
k-아파트코드 has unique valuesUnique
k-전용면적별세대현황(60㎡이하) has 796 (28.6%) zerosZeros
k-전용면적별세대현황(60㎡~85㎡이하) has 609 (21.8%) zerosZeros
k-85㎡~135㎡이하 has 1324 (47.5%) zerosZeros
건축면적 has 1155 (41.4%) zerosZeros
주차대수 has 222 (8.0%) zerosZeros

Reproduction

Analysis started2024-05-11 06:41:13.595594
Analysis finished2024-05-11 06:41:16.970994
Duration3.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct2788
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6388.1596
Minimum1
Maximum40407
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.6 KiB
2024-05-11T15:41:17.066978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile144.35
Q1711.75
median1433.5
Q32175.25
95-th percentile40179.65
Maximum40407
Range40406
Interquartile range (IQR)1463.5

Descriptive statistics

Standard deviation11924.144
Coefficient of variation (CV)1.8666008
Kurtosis3.3127459
Mean6388.1596
Median Absolute Deviation (MAD)732
Skewness2.1962163
Sum17810189
Variance1.4218521 × 108
MonotonicityStrictly increasing
2024-05-11T15:41:17.294034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
1934 1
 
< 0.1%
1926 1
 
< 0.1%
1927 1
 
< 0.1%
1928 1
 
< 0.1%
1929 1
 
< 0.1%
1930 1
 
< 0.1%
1931 1
 
< 0.1%
1932 1
 
< 0.1%
1933 1
 
< 0.1%
Other values (2778) 2778
99.6%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
40407 1
< 0.1%
40399 1
< 0.1%
40397 1
< 0.1%
40396 1
< 0.1%
40394 1
< 0.1%
40390 1
< 0.1%
40389 1
< 0.1%
40387 1
< 0.1%
40386 1
< 0.1%
40380 1
< 0.1%

k-아파트코드
Text

UNIQUE 

Distinct2788
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size21.9 KiB
2024-05-11T15:41:17.791776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters25092
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2788 ?
Unique (%)100.0%

Sample

1st rowA15679103
2nd rowA13876112
3rd rowA13873701
4th rowA15275101
5th rowA13991016
ValueCountFrequency (%)
a15679103 1
 
< 0.1%
a11077101 1
 
< 0.1%
a41279909 1
 
< 0.1%
a13585402 1
 
< 0.1%
a13170401 1
 
< 0.1%
a13410009 1
 
< 0.1%
a13410010 1
 
< 0.1%
a13527018 1
 
< 0.1%
a13677101 1
 
< 0.1%
a13276417 1
 
< 0.1%
Other values (2778) 2778
99.6%
2024-05-11T15:41:18.402776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5252
20.9%
1 4799
19.1%
A 2759
11.0%
2 2403
9.6%
3 2368
9.4%
5 1682
 
6.7%
8 1482
 
5.9%
7 1352
 
5.4%
4 1161
 
4.6%
6 958
 
3.8%
Other values (2) 876
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22304
88.9%
Uppercase Letter 2788
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5252
23.5%
1 4799
21.5%
2 2403
10.8%
3 2368
10.6%
5 1682
 
7.5%
8 1482
 
6.6%
7 1352
 
6.1%
4 1161
 
5.2%
6 958
 
4.3%
9 847
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
A 2759
99.0%
B 29
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22304
88.9%
Latin 2788
 
11.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5252
23.5%
1 4799
21.5%
2 2403
10.8%
3 2368
10.6%
5 1682
 
7.5%
8 1482
 
6.6%
7 1352
 
6.1%
4 1161
 
5.2%
6 958
 
4.3%
9 847
 
3.8%
Latin
ValueCountFrequency (%)
A 2759
99.0%
B 29
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25092
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5252
20.9%
1 4799
19.1%
A 2759
11.0%
2 2403
9.6%
3 2368
9.4%
5 1682
 
6.7%
8 1482
 
5.9%
7 1352
 
5.4%
4 1161
 
4.6%
6 958
 
3.8%
Other values (2) 876
 
3.5%
Distinct2779
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size21.9 KiB
2024-05-11T15:41:18.761514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length20
Mean length7.5312052
Min length2

Characters and Unicode

Total characters20997
Distinct characters463
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2770 ?
Unique (%)99.4%

Sample

1st row우리유앤미
2nd row송파파인타운13단지
3rd row오금현대백조(임대)
4th row개봉건영
5th row월계동원베네스트
ValueCountFrequency (%)
아파트 57
 
1.9%
래미안 12
 
0.4%
e편한세상 8
 
0.3%
아이파크 6
 
0.2%
힐스테이트 5
 
0.2%
청년주택 5
 
0.2%
sk뷰 4
 
0.1%
고덕 4
 
0.1%
롯데캐슬 4
 
0.1%
이편한세상 4
 
0.1%
Other values (2895) 2969
96.5%
2024-05-11T15:41:19.386430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
743
 
3.5%
724
 
3.4%
696
 
3.3%
521
 
2.5%
501
 
2.4%
449
 
2.1%
401
 
1.9%
401
 
1.9%
387
 
1.8%
387
 
1.8%
Other values (453) 15787
75.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19085
90.9%
Decimal Number 1049
 
5.0%
Space Separator 317
 
1.5%
Uppercase Letter 286
 
1.4%
Lowercase Letter 100
 
0.5%
Open Punctuation 46
 
0.2%
Close Punctuation 46
 
0.2%
Dash Punctuation 40
 
0.2%
Other Punctuation 26
 
0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
743
 
3.9%
724
 
3.8%
696
 
3.6%
521
 
2.7%
501
 
2.6%
449
 
2.4%
401
 
2.1%
401
 
2.1%
387
 
2.0%
387
 
2.0%
Other values (403) 13875
72.7%
Uppercase Letter
ValueCountFrequency (%)
S 49
17.1%
C 35
12.2%
K 29
10.1%
D 28
9.8%
M 27
9.4%
H 21
7.3%
L 19
 
6.6%
E 17
 
5.9%
I 14
 
4.9%
V 12
 
4.2%
Other values (9) 35
12.2%
Lowercase Letter
ValueCountFrequency (%)
e 54
54.0%
l 10
 
10.0%
i 8
 
8.0%
k 6
 
6.0%
v 6
 
6.0%
s 5
 
5.0%
c 2
 
2.0%
a 2
 
2.0%
w 2
 
2.0%
h 2
 
2.0%
Other values (3) 3
 
3.0%
Decimal Number
ValueCountFrequency (%)
1 309
29.5%
2 309
29.5%
3 130
12.4%
4 76
 
7.2%
5 57
 
5.4%
6 47
 
4.5%
7 34
 
3.2%
8 29
 
2.8%
9 29
 
2.8%
0 29
 
2.8%
Other Punctuation
ValueCountFrequency (%)
, 21
80.8%
. 5
 
19.2%
Space Separator
ValueCountFrequency (%)
317
100.0%
Open Punctuation
ValueCountFrequency (%)
( 46
100.0%
Close Punctuation
ValueCountFrequency (%)
) 46
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19085
90.9%
Common 1525
 
7.3%
Latin 387
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
743
 
3.9%
724
 
3.8%
696
 
3.6%
521
 
2.7%
501
 
2.6%
449
 
2.4%
401
 
2.1%
401
 
2.1%
387
 
2.0%
387
 
2.0%
Other values (403) 13875
72.7%
Latin
ValueCountFrequency (%)
e 54
14.0%
S 49
12.7%
C 35
 
9.0%
K 29
 
7.5%
D 28
 
7.2%
M 27
 
7.0%
H 21
 
5.4%
L 19
 
4.9%
E 17
 
4.4%
I 14
 
3.6%
Other values (23) 94
24.3%
Common
ValueCountFrequency (%)
317
20.8%
1 309
20.3%
2 309
20.3%
3 130
8.5%
4 76
 
5.0%
5 57
 
3.7%
6 47
 
3.1%
( 46
 
3.0%
) 46
 
3.0%
- 40
 
2.6%
Other values (7) 148
9.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 19085
90.9%
ASCII 1911
 
9.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
743
 
3.9%
724
 
3.8%
696
 
3.6%
521
 
2.7%
501
 
2.6%
449
 
2.4%
401
 
2.1%
401
 
2.1%
387
 
2.0%
387
 
2.0%
Other values (403) 13875
72.7%
ASCII
ValueCountFrequency (%)
317
16.6%
1 309
16.2%
2 309
16.2%
3 130
 
6.8%
4 76
 
4.0%
5 57
 
3.0%
e 54
 
2.8%
S 49
 
2.6%
6 47
 
2.5%
( 46
 
2.4%
Other values (39) 517
27.1%
Number Forms
ValueCountFrequency (%)
1
100.0%
Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size21.9 KiB
아파트
2519 
주상복합
 
151
<NA>
 
69
도시형 생활주택(주상복합)
 
26
연립주택
 
11
Other values (2)
 
12

Length

Max length14
Median length3
Mean length3.2288379
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
아파트 2519
90.4%
주상복합 151
 
5.4%
<NA> 69
 
2.5%
도시형 생활주택(주상복합) 26
 
0.9%
연립주택 11
 
0.4%
도시형 생활주택(아파트) 11
 
0.4%
도시형 생활주택(연립주택) 1
 
< 0.1%

Length

2024-05-11T15:41:19.619284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:41:19.857998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아파트 2519
89.1%
주상복합 151
 
5.3%
na 69
 
2.4%
도시형 38
 
1.3%
생활주택(주상복합 26
 
0.9%
연립주택 11
 
0.4%
생활주택(아파트 11
 
0.4%
생활주택(연립주택 1
 
< 0.1%

kapt도로명주소
Text

MISSING 

Distinct2579
Distinct (%)97.1%
Missing131
Missing (%)4.7%
Memory size21.9 KiB
2024-05-11T15:41:20.366750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length23
Mean length18.584494
Min length15

Characters and Unicode

Total characters49379
Distinct characters271
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2502 ?
Unique (%)94.2%

Sample

1st row서울특별시 동작구 서달로 83
2nd row서울특별시 송파구 송파대로8길 10
3rd row서울특별시 송파구 양재대로72길 20
4th row서울특별시 구로구 고척로21나길 85-6
5th row서울특별시 노원구 월계로53길 21
ValueCountFrequency (%)
서울특별시 2657
25.0%
노원구 210
 
2.0%
강남구 198
 
1.9%
강서구 164
 
1.5%
영등포구 153
 
1.4%
서초구 147
 
1.4%
구로구 136
 
1.3%
송파구 135
 
1.3%
성북구 122
 
1.1%
은평구 118
 
1.1%
Other values (2008) 6589
62.0%
2024-05-11T15:41:21.201921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7972
16.1%
3186
 
6.5%
2837
 
5.7%
2739
 
5.5%
2686
 
5.4%
2667
 
5.4%
2657
 
5.4%
2657
 
5.4%
1 1769
 
3.6%
1455
 
2.9%
Other values (261) 18754
38.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32245
65.3%
Decimal Number 8983
 
18.2%
Space Separator 7972
 
16.1%
Dash Punctuation 179
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3186
 
9.9%
2837
 
8.8%
2739
 
8.5%
2686
 
8.3%
2667
 
8.3%
2657
 
8.2%
2657
 
8.2%
1455
 
4.5%
665
 
2.1%
560
 
1.7%
Other values (249) 10136
31.4%
Decimal Number
ValueCountFrequency (%)
1 1769
19.7%
2 1233
13.7%
3 1058
11.8%
4 834
9.3%
5 829
9.2%
0 728
8.1%
6 715
8.0%
7 702
 
7.8%
9 559
 
6.2%
8 556
 
6.2%
Space Separator
ValueCountFrequency (%)
7972
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32245
65.3%
Common 17134
34.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3186
 
9.9%
2837
 
8.8%
2739
 
8.5%
2686
 
8.3%
2667
 
8.3%
2657
 
8.2%
2657
 
8.2%
1455
 
4.5%
665
 
2.1%
560
 
1.7%
Other values (249) 10136
31.4%
Common
ValueCountFrequency (%)
7972
46.5%
1 1769
 
10.3%
2 1233
 
7.2%
3 1058
 
6.2%
4 834
 
4.9%
5 829
 
4.8%
0 728
 
4.2%
6 715
 
4.2%
7 702
 
4.1%
9 559
 
3.3%
Other values (2) 735
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32245
65.3%
ASCII 17134
34.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7972
46.5%
1 1769
 
10.3%
2 1233
 
7.2%
3 1058
 
6.2%
4 834
 
4.9%
5 829
 
4.8%
0 728
 
4.2%
6 715
 
4.2%
7 702
 
4.1%
9 559
 
3.3%
Other values (2) 735
 
4.3%
Hangul
ValueCountFrequency (%)
3186
 
9.9%
2837
 
8.8%
2739
 
8.5%
2686
 
8.3%
2667
 
8.3%
2657
 
8.2%
2657
 
8.2%
1455
 
4.5%
665
 
2.1%
560
 
1.7%
Other values (249) 10136
31.4%

주소(시도)k-apt주소split
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.9 KiB
서울
2751 
서울특별시
 
37

Length

Max length5
Median length2
Mean length2.0398135
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 2751
98.7%
서울특별시 37
 
1.3%

Length

2024-05-11T15:41:21.489767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:41:21.691883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 2751
98.7%
서울특별시 37
 
1.3%
Distinct25
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size21.9 KiB
강남구
223 
노원구
214 
강서구
 
168
영등포구
 
157
서초구
 
154
Other values (20)
1872 

Length

Max length4
Median length3
Mean length3.1072453
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동작구
2nd row송파구
3rd row송파구
4th row구로구
5th row노원구

Common Values

ValueCountFrequency (%)
강남구 223
 
8.0%
노원구 214
 
7.7%
강서구 168
 
6.0%
영등포구 157
 
5.6%
서초구 154
 
5.5%
구로구 141
 
5.1%
송파구 139
 
5.0%
성북구 131
 
4.7%
은평구 124
 
4.4%
마포구 121
 
4.3%
Other values (15) 1216
43.6%

Length

2024-05-11T15:41:21.875927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 223
 
8.0%
노원구 214
 
7.7%
강서구 168
 
6.0%
영등포구 157
 
5.6%
서초구 154
 
5.5%
구로구 141
 
5.1%
송파구 139
 
5.0%
성북구 131
 
4.7%
은평구 124
 
4.4%
마포구 121
 
4.3%
Other values (15) 1216
43.6%
Distinct274
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size21.9 KiB
2024-05-11T15:41:22.378766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.1459828
Min length2

Characters and Unicode

Total characters8771
Distinct characters179
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)1.9%

Sample

1st row흑석동
2nd row장지동
3rd row오금동
4th row개봉동
5th row월계동
ValueCountFrequency (%)
상계동 73
 
2.6%
신정동 57
 
2.0%
중계동 47
 
1.7%
서초동 43
 
1.5%
진관동 41
 
1.5%
공릉동 40
 
1.4%
목동 39
 
1.4%
구로동 38
 
1.4%
봉천동 36
 
1.3%
월계동 36
 
1.3%
Other values (264) 2338
83.9%
2024-05-11T15:41:23.059564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2776
31.6%
278
 
3.2%
229
 
2.6%
175
 
2.0%
148
 
1.7%
130
 
1.5%
123
 
1.4%
114
 
1.3%
105
 
1.2%
94
 
1.1%
Other values (169) 4599
52.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8600
98.1%
Decimal Number 171
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2776
32.3%
278
 
3.2%
229
 
2.7%
175
 
2.0%
148
 
1.7%
130
 
1.5%
123
 
1.4%
114
 
1.3%
105
 
1.2%
94
 
1.1%
Other values (161) 4428
51.5%
Decimal Number
ValueCountFrequency (%)
1 38
22.2%
3 33
19.3%
2 32
18.7%
4 30
17.5%
5 19
11.1%
6 13
 
7.6%
7 4
 
2.3%
8 2
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8600
98.1%
Common 171
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2776
32.3%
278
 
3.2%
229
 
2.7%
175
 
2.0%
148
 
1.7%
130
 
1.5%
123
 
1.4%
114
 
1.3%
105
 
1.2%
94
 
1.1%
Other values (161) 4428
51.5%
Common
ValueCountFrequency (%)
1 38
22.2%
3 33
19.3%
2 32
18.7%
4 30
17.5%
5 19
11.1%
6 13
 
7.6%
7 4
 
2.3%
8 2
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8600
98.1%
ASCII 171
 
1.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
2776
32.3%
278
 
3.2%
229
 
2.7%
175
 
2.0%
148
 
1.7%
130
 
1.5%
123
 
1.4%
114
 
1.3%
105
 
1.2%
94
 
1.1%
Other values (161) 4428
51.5%
ASCII
ValueCountFrequency (%)
1 38
22.2%
3 33
19.3%
2 32
18.7%
4 30
17.5%
5 19
11.1%
6 13
 
7.6%
7 4
 
2.3%
8 2
 
1.2%

나머지주소
Text

MISSING 

Distinct1882
Distinct (%)88.3%
Missing656
Missing (%)23.5%
Memory size21.9 KiB
2024-05-11T15:41:23.685508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length30
Mean length7.1219512
Min length1

Characters and Unicode

Total characters15184
Distinct characters391
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1719 ?
Unique (%)80.6%

Sample

1st row우리유앤미아파트
2nd row857
3rd row20-2
4th row47-1
5th row서울시 노원구 월계2동 940번지
ValueCountFrequency (%)
관리사무소 103
 
3.7%
서울시 31
 
1.1%
서울 31
 
1.1%
아파트 24
 
0.9%
현대아파트 12
 
0.4%
노원구 10
 
0.4%
서울특별시 10
 
0.4%
생활지원센터 10
 
0.4%
동대문구 9
 
0.3%
삼성아파트 9
 
0.3%
Other values (2164) 2525
91.0%
2024-05-11T15:41:24.619137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1020
 
6.7%
2 669
 
4.4%
653
 
4.3%
627
 
4.1%
3 561
 
3.7%
551
 
3.6%
544
 
3.6%
4 543
 
3.6%
535
 
3.5%
5 523
 
3.4%
Other values (381) 8958
59.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8417
55.4%
Decimal Number 5353
35.3%
Space Separator 653
 
4.3%
Dash Punctuation 408
 
2.7%
Close Punctuation 93
 
0.6%
Open Punctuation 89
 
0.6%
Other Punctuation 76
 
0.5%
Uppercase Letter 57
 
0.4%
Lowercase Letter 34
 
0.2%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
627
 
7.4%
551
 
6.5%
544
 
6.5%
535
 
6.4%
471
 
5.6%
294
 
3.5%
222
 
2.6%
155
 
1.8%
150
 
1.8%
146
 
1.7%
Other values (331) 4722
56.1%
Uppercase Letter
ValueCountFrequency (%)
S 9
15.8%
C 7
12.3%
K 7
12.3%
I 4
 
7.0%
H 4
 
7.0%
L 4
 
7.0%
V 3
 
5.3%
E 3
 
5.3%
D 2
 
3.5%
M 2
 
3.5%
Other values (7) 12
21.1%
Decimal Number
ValueCountFrequency (%)
1 1020
19.1%
2 669
12.5%
3 561
10.5%
4 543
10.1%
5 523
9.8%
7 481
9.0%
6 465
8.7%
0 431
8.1%
8 340
 
6.4%
9 320
 
6.0%
Lowercase Letter
ValueCountFrequency (%)
e 10
29.4%
s 6
17.6%
l 4
 
11.8%
k 4
 
11.8%
w 2
 
5.9%
c 2
 
5.9%
j 2
 
5.9%
q 2
 
5.9%
h 1
 
2.9%
i 1
 
2.9%
Other Punctuation
ValueCountFrequency (%)
, 60
78.9%
. 14
 
18.4%
; 1
 
1.3%
@ 1
 
1.3%
Close Punctuation
ValueCountFrequency (%)
) 92
98.9%
] 1
 
1.1%
Open Punctuation
ValueCountFrequency (%)
( 88
98.9%
[ 1
 
1.1%
Letter Number
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
653
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 408
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8417
55.4%
Common 6674
44.0%
Latin 93
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
627
 
7.4%
551
 
6.5%
544
 
6.5%
535
 
6.4%
471
 
5.6%
294
 
3.5%
222
 
2.6%
155
 
1.8%
150
 
1.8%
146
 
1.7%
Other values (331) 4722
56.1%
Latin
ValueCountFrequency (%)
e 10
 
10.8%
S 9
 
9.7%
C 7
 
7.5%
K 7
 
7.5%
s 6
 
6.5%
I 4
 
4.3%
l 4
 
4.3%
k 4
 
4.3%
H 4
 
4.3%
L 4
 
4.3%
Other values (19) 34
36.6%
Common
ValueCountFrequency (%)
1 1020
15.3%
2 669
10.0%
653
9.8%
3 561
8.4%
4 543
8.1%
5 523
7.8%
7 481
7.2%
6 465
7.0%
0 431
6.5%
- 408
 
6.1%
Other values (11) 920
13.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8416
55.4%
ASCII 6765
44.6%
Number Forms 2
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1020
15.1%
2 669
9.9%
653
9.7%
3 561
8.3%
4 543
8.0%
5 523
7.7%
7 481
7.1%
6 465
6.9%
0 431
6.4%
- 408
 
6.0%
Other values (38) 1011
14.9%
Hangul
ValueCountFrequency (%)
627
 
7.5%
551
 
6.5%
544
 
6.5%
535
 
6.4%
471
 
5.6%
294
 
3.5%
222
 
2.6%
155
 
1.8%
150
 
1.8%
146
 
1.7%
Other values (330) 4721
56.1%
Number Forms
ValueCountFrequency (%)
1
50.0%
1
50.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%

주소(도로명)
Text

MISSING 

Distinct1343
Distinct (%)50.2%
Missing113
Missing (%)4.1%
Memory size21.9 KiB
2024-05-11T15:41:25.071918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.0183178
Min length2

Characters and Unicode

Total characters13424
Distinct characters267
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique816 ?
Unique (%)30.5%

Sample

1st row서달로
2nd row송파대로8길
3rd row양재대로72길
4th row고척로21나길
5th row월계로53길
ValueCountFrequency (%)
통일로 26
 
1.0%
목동동로 17
 
0.6%
독서당로 15
 
0.6%
마들로 14
 
0.5%
동일로 14
 
0.5%
천호대로 13
 
0.5%
아차산로 13
 
0.5%
경인로 13
 
0.5%
해등로 13
 
0.5%
진관4로 13
 
0.5%
Other values (1333) 2524
94.4%
2024-05-11T15:41:25.754257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2597
 
19.3%
1470
 
11.0%
1 500
 
3.7%
2 358
 
2.7%
3 342
 
2.5%
4 286
 
2.1%
254
 
1.9%
242
 
1.8%
5 238
 
1.8%
7 202
 
1.5%
Other values (257) 6935
51.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10777
80.3%
Decimal Number 2647
 
19.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2597
24.1%
1470
 
13.6%
254
 
2.4%
242
 
2.2%
180
 
1.7%
162
 
1.5%
141
 
1.3%
124
 
1.2%
122
 
1.1%
120
 
1.1%
Other values (247) 5365
49.8%
Decimal Number
ValueCountFrequency (%)
1 500
18.9%
2 358
13.5%
3 342
12.9%
4 286
10.8%
5 238
9.0%
7 202
7.6%
6 196
 
7.4%
9 190
 
7.2%
8 181
 
6.8%
0 154
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10777
80.3%
Common 2647
 
19.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2597
24.1%
1470
 
13.6%
254
 
2.4%
242
 
2.2%
180
 
1.7%
162
 
1.5%
141
 
1.3%
124
 
1.2%
122
 
1.1%
120
 
1.1%
Other values (247) 5365
49.8%
Common
ValueCountFrequency (%)
1 500
18.9%
2 358
13.5%
3 342
12.9%
4 286
10.8%
5 238
9.0%
7 202
7.6%
6 196
 
7.4%
9 190
 
7.2%
8 181
 
6.8%
0 154
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10777
80.3%
ASCII 2647
 
19.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
2597
24.1%
1470
 
13.6%
254
 
2.4%
242
 
2.2%
180
 
1.7%
162
 
1.5%
141
 
1.3%
124
 
1.2%
122
 
1.1%
120
 
1.1%
Other values (247) 5365
49.8%
ASCII
ValueCountFrequency (%)
1 500
18.9%
2 358
13.5%
3 342
12.9%
4 286
10.8%
5 238
9.0%
7 202
7.6%
6 196
 
7.4%
9 190
 
7.2%
8 181
 
6.8%
0 154
 
5.8%
Distinct662
Distinct (%)24.8%
Missing120
Missing (%)4.3%
Memory size21.9 KiB
2024-05-11T15:41:26.389676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.4651424
Min length1

Characters and Unicode

Total characters6577
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

Unique364 ?
Unique (%)13.6%

Sample

1st row83
2nd row10
3rd row20
4th row85-6
5th row21
ValueCountFrequency (%)
11 41
 
1.5%
16 39
 
1.5%
19 38
 
1.4%
15 38
 
1.4%
10 36
 
1.3%
30 36
 
1.3%
7 36
 
1.3%
20 35
 
1.3%
25 35
 
1.3%
17 34
 
1.3%
Other values (652) 2300
86.2%
2024-05-11T15:41:27.249332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1288
19.6%
2 882
13.4%
3 724
11.0%
5 599
9.1%
0 579
8.8%
4 551
8.4%
6 520
7.9%
7 503
 
7.6%
8 376
 
5.7%
9 374
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6396
97.2%
Dash Punctuation 181
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1288
20.1%
2 882
13.8%
3 724
11.3%
5 599
9.4%
0 579
9.1%
4 551
8.6%
6 520
8.1%
7 503
 
7.9%
8 376
 
5.9%
9 374
 
5.8%
Dash Punctuation
ValueCountFrequency (%)
- 181
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6577
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1288
19.6%
2 882
13.4%
3 724
11.0%
5 599
9.1%
0 579
8.8%
4 551
8.4%
6 520
7.9%
7 503
 
7.6%
8 376
 
5.7%
9 374
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6577
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1288
19.6%
2 882
13.4%
3 724
11.0%
5 599
9.1%
0 579
8.8%
4 551
8.4%
6 520
7.9%
7 503
 
7.6%
8 376
 
5.7%
9 374
 
5.7%
Distinct2774
Distinct (%)99.7%
Missing7
Missing (%)0.3%
Memory size21.9 KiB
2024-05-11T15:41:27.737209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length9.4016541
Min length9

Characters and Unicode

Total characters26146
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

Unique2767 ?
Unique (%)99.5%

Sample

1st row028127541
2nd row024002658
3rd row024000298
4th row0220878606
5th row029029567
ValueCountFrequency (%)
025450542 2
 
0.1%
025324861 2
 
0.1%
0236625709 2
 
0.1%
024300046 2
 
0.1%
025216109 2
 
0.1%
028829891 2
 
0.1%
029123927 2
 
0.1%
024939048 1
 
< 0.1%
029599905 1
 
< 0.1%
025996159 1
 
< 0.1%
Other values (2764) 2764
99.4%
2024-05-11T15:41:28.451939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 5328
20.4%
0 4687
17.9%
3 2277
8.7%
4 2111
 
8.1%
6 2098
 
8.0%
9 2086
 
8.0%
5 1962
 
7.5%
1 1936
 
7.4%
8 1787
 
6.8%
7 1761
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26033
99.6%
Dash Punctuation 113
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 5328
20.5%
0 4687
18.0%
3 2277
8.7%
4 2111
 
8.1%
6 2098
 
8.1%
9 2086
 
8.0%
5 1962
 
7.5%
1 1936
 
7.4%
8 1787
 
6.9%
7 1761
 
6.8%
Dash Punctuation
ValueCountFrequency (%)
- 113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26146
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 5328
20.4%
0 4687
17.9%
3 2277
8.7%
4 2111
 
8.1%
6 2098
 
8.0%
9 2086
 
8.0%
5 1962
 
7.5%
1 1936
 
7.4%
8 1787
 
6.8%
7 1761
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26146
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 5328
20.4%
0 4687
17.9%
3 2277
8.7%
4 2111
 
8.1%
6 2098
 
8.0%
9 2086
 
8.0%
5 1962
 
7.5%
1 1936
 
7.4%
8 1787
 
6.8%
7 1761
 
6.7%

k-팩스번호
Text

MISSING 

Distinct2706
Distinct (%)99.7%
Missing74
Missing (%)2.7%
Memory size21.9 KiB
2024-05-11T15:41:28.875113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length9.4502579
Min length3

Characters and Unicode

Total characters25648
Distinct characters12
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

Unique2699 ?
Unique (%)99.4%

Sample

1st row028127542
2nd row024002668
3rd row024000497
4th row0226878616
5th row029909567
ValueCountFrequency (%)
029123928 3
 
0.1%
024300048 2
 
0.1%
0260925709 2
 
0.1%
025450543 2
 
0.1%
023556303 2
 
0.1%
025216120 2
 
0.1%
0262844861 2
 
0.1%
0222824207 1
 
< 0.1%
025845628 1
 
< 0.1%
028179469 1
 
< 0.1%
Other values (2696) 2696
99.3%
2024-05-11T15:41:29.579709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 5286
20.6%
0 4369
17.0%
6 2309
9.0%
3 2305
9.0%
9 2091
 
8.2%
4 2086
 
8.1%
5 1849
 
7.2%
1 1807
 
7.0%
8 1750
 
6.8%
7 1721
 
6.7%
Other values (2) 75
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25573
99.7%
Dash Punctuation 74
 
0.3%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 5286
20.7%
0 4369
17.1%
6 2309
9.0%
3 2305
9.0%
9 2091
 
8.2%
4 2086
 
8.2%
5 1849
 
7.2%
1 1807
 
7.1%
8 1750
 
6.8%
7 1721
 
6.7%
Dash Punctuation
ValueCountFrequency (%)
- 74
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25648
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 5286
20.6%
0 4369
17.0%
6 2309
9.0%
3 2305
9.0%
9 2091
 
8.2%
4 2086
 
8.1%
5 1849
 
7.2%
1 1807
 
7.0%
8 1750
 
6.8%
7 1721
 
6.7%
Other values (2) 75
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25648
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 5286
20.6%
0 4369
17.0%
6 2309
9.0%
3 2305
9.0%
9 2091
 
8.2%
4 2086
 
8.1%
5 1849
 
7.2%
1 1807
 
7.0%
8 1750
 
6.8%
7 1721
 
6.7%
Other values (2) 75
 
0.3%

단지소개기존clob
Real number (ℝ)

MISSING 

Distinct298
Distinct (%)52.7%
Missing2222
Missing (%)79.7%
Infinite0
Infinite (%)0.0%
Mean418.9682
Minimum1
Maximum2888
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.6 KiB
2024-05-11T15:41:29.827594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14
median94
Q3463.75
95-th percentile2163.5
Maximum2888
Range2887
Interquartile range (IQR)459.75

Descriptive statistics

Standard deviation683.36765
Coefficient of variation (CV)1.6310728
Kurtosis2.7332987
Mean418.9682
Median Absolute Deviation (MAD)90
Skewness1.9571505
Sum237136
Variance466991.34
MonotonicityNot monotonic
2024-05-11T15:41:30.042732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 192
 
6.9%
5 21
 
0.8%
93 5
 
0.2%
2000 5
 
0.2%
70 3
 
0.1%
124 3
 
0.1%
2 3
 
0.1%
236 2
 
0.1%
63 2
 
0.1%
297 2
 
0.1%
Other values (288) 328
 
11.8%
(Missing) 2222
79.7%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 3
 
0.1%
4 192
6.9%
5 21
 
0.8%
8 2
 
0.1%
9 2
 
0.1%
10 2
 
0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
18 1
 
< 0.1%
ValueCountFrequency (%)
2888 1
< 0.1%
2806 1
< 0.1%
2682 1
< 0.1%
2631 1
< 0.1%
2626 1
< 0.1%
2596 1
< 0.1%
2588 1
< 0.1%
2576 1
< 0.1%
2556 1
< 0.1%
2521 1
< 0.1%

단지첨부파일
Text

MISSING 

Distinct170
Distinct (%)92.4%
Missing2604
Missing (%)93.4%
Memory size21.9 KiB
2024-05-11T15:41:30.455254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters3680
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique169 ?
Unique (%)91.8%

Sample

1st rowFILE_000000000084906
2nd rowFILE_000000000336219
3rd rowFILE_000000000038808
4th rowFILE_000000000165416
5th rowFILE_000000000123878
ValueCountFrequency (%)
file_0000000000basic 15
 
8.2%
file_000000000020837 1
 
0.5%
file_000000000022014 1
 
0.5%
file_000000000090952 1
 
0.5%
file_000000000083696 1
 
0.5%
file_000000000158878 1
 
0.5%
file_000000000002723 1
 
0.5%
file_000000000013337 1
 
0.5%
file_000000000004079 1
 
0.5%
file_000000000001831 1
 
0.5%
Other values (160) 160
87.0%
2024-05-11T15:41:31.463735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1930
52.4%
F 184
 
5.0%
L 184
 
5.0%
E 184
 
5.0%
_ 184
 
5.0%
I 184
 
5.0%
1 114
 
3.1%
8 100
 
2.7%
2 97
 
2.6%
6 88
 
2.4%
Other values (10) 431
 
11.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2685
73.0%
Uppercase Letter 736
 
20.0%
Connector Punctuation 184
 
5.0%
Lowercase Letter 75
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1930
71.9%
1 114
 
4.2%
8 100
 
3.7%
2 97
 
3.6%
6 88
 
3.3%
5 79
 
2.9%
7 74
 
2.8%
4 73
 
2.7%
3 72
 
2.7%
9 58
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
i 15
20.0%
s 15
20.0%
a 15
20.0%
b 15
20.0%
c 15
20.0%
Uppercase Letter
ValueCountFrequency (%)
F 184
25.0%
L 184
25.0%
E 184
25.0%
I 184
25.0%
Connector Punctuation
ValueCountFrequency (%)
_ 184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2869
78.0%
Latin 811
 
22.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1930
67.3%
_ 184
 
6.4%
1 114
 
4.0%
8 100
 
3.5%
2 97
 
3.4%
6 88
 
3.1%
5 79
 
2.8%
7 74
 
2.6%
4 73
 
2.5%
3 72
 
2.5%
Latin
ValueCountFrequency (%)
F 184
22.7%
L 184
22.7%
E 184
22.7%
I 184
22.7%
i 15
 
1.8%
s 15
 
1.8%
a 15
 
1.8%
b 15
 
1.8%
c 15
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3680
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1930
52.4%
F 184
 
5.0%
L 184
 
5.0%
E 184
 
5.0%
_ 184
 
5.0%
I 184
 
5.0%
1 114
 
3.1%
8 100
 
2.7%
2 97
 
2.6%
6 88
 
2.4%
Other values (10) 431
 
11.7%

k-세대타입(분양형태)
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size21.9 KiB
분양
2120 
기타
390 
임대
265 
<NA>
 
10
혼합
 
2

Length

Max length5
Median length2
Mean length2.0082496
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
분양 2120
76.0%
기타 390
 
14.0%
임대 265
 
9.5%
<NA> 10
 
0.4%
혼합 2
 
0.1%
임대+분양 1
 
< 0.1%

Length

2024-05-11T15:41:31.715120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:41:31.932780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
분양 2120
76.0%
기타 390
 
14.0%
임대 265
 
9.5%
na 10
 
0.4%
혼합 2
 
0.1%
임대+분양 1
 
< 0.1%

k-관리방식
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size21.9 KiB
위탁관리
2433 
자치관리
280 
직영
 
61
<NA>
 
13
직영+위탁
 
1

Length

Max length5
Median length4
Mean length3.9565997
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
위탁관리 2433
87.3%
자치관리 280
 
10.0%
직영 61
 
2.2%
<NA> 13
 
0.5%
직영+위탁 1
 
< 0.1%

Length

2024-05-11T15:41:32.146654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:41:32.331365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
위탁관리 2433
87.3%
자치관리 280
 
10.0%
직영 61
 
2.2%
na 13
 
0.5%
직영+위탁 1
 
< 0.1%

k-복도유형
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size21.9 KiB
계단식
1276 
혼합식
915 
복도식
532 
타워형
 
26
기타
 
21

Length

Max length4
Median length3
Mean length2.998924
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row혼합식
2nd row계단식
3rd row복도식
4th row계단식
5th row계단식

Common Values

ValueCountFrequency (%)
계단식 1276
45.8%
혼합식 915
32.8%
복도식 532
19.1%
타워형 26
 
0.9%
기타 21
 
0.8%
<NA> 18
 
0.6%

Length

2024-05-11T15:41:32.549916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:41:32.721846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
계단식 1276
45.8%
혼합식 915
32.8%
복도식 532
19.1%
타워형 26
 
0.9%
기타 21
 
0.8%
na 18
 
0.6%

k-난방방식
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size21.9 KiB
개별난방
1892 
지역난방
740 
중앙난방
 
123
기타
 
26
<NA>
 
7

Length

Max length4
Median length4
Mean length3.9813486
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개별난방 1892
67.9%
지역난방 740
 
26.5%
중앙난방 123
 
4.4%
기타 26
 
0.9%
<NA> 7
 
0.3%

Length

2024-05-11T15:41:32.931627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:41:33.106839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개별난방 1892
67.9%
지역난방 740
 
26.5%
중앙난방 123
 
4.4%
기타 26
 
0.9%
na 7
 
0.3%

k-전체동수
Real number (ℝ)

Distinct58
Distinct (%)2.1%
Missing25
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean6.9811799
Minimum1
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.6 KiB
2024-05-11T15:41:33.320915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q39
95-th percentile20.9
Maximum124
Range123
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.4525558
Coefficient of variation (CV)1.2107632
Kurtosis46.241973
Mean6.9811799
Median Absolute Deviation (MAD)3
Skewness5.1273347
Sum19289
Variance71.445699
MonotonicityNot monotonic
2024-05-11T15:41:33.577353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 411
14.7%
1 356
12.8%
3 339
12.2%
4 270
9.7%
5 217
7.8%
6 189
 
6.8%
7 150
 
5.4%
8 127
 
4.6%
9 104
 
3.7%
10 86
 
3.1%
Other values (48) 514
18.4%
ValueCountFrequency (%)
1 356
12.8%
2 411
14.7%
3 339
12.2%
4 270
9.7%
5 217
7.8%
6 189
6.8%
7 150
 
5.4%
8 127
 
4.6%
9 104
 
3.7%
10 86
 
3.1%
ValueCountFrequency (%)
124 1
< 0.1%
122 1
< 0.1%
100 2
0.1%
84 1
< 0.1%
74 1
< 0.1%
72 1
< 0.1%
66 1
< 0.1%
65 1
< 0.1%
61 1
< 0.1%
58 1
< 0.1%

k-전체세대수
Real number (ℝ)

Distinct1101
Distinct (%)39.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean597.3411
Minimum52
Maximum9510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.6 KiB
2024-05-11T15:41:33.838525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile146.35
Q1222
median387
Q3713
95-th percentile1765.3
Maximum9510
Range9458
Interquartile range (IQR)491

Descriptive statistics

Standard deviation649.38995
Coefficient of variation (CV)1.0871342
Kurtosis28.090482
Mean597.3411
Median Absolute Deviation (MAD)190
Skewness4.0085243
Sum1665387
Variance421707.31
MonotonicityNot monotonic
2024-05-11T15:41:34.036056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
299 24
 
0.9%
160 19
 
0.7%
206 18
 
0.6%
200 15
 
0.5%
150 14
 
0.5%
264 13
 
0.5%
198 13
 
0.5%
170 13
 
0.5%
298 12
 
0.4%
154 11
 
0.4%
Other values (1091) 2636
94.5%
ValueCountFrequency (%)
52 1
< 0.1%
54 1
< 0.1%
58 2
0.1%
59 1
< 0.1%
63 1
< 0.1%
64 2
0.1%
65 2
0.1%
67 1
< 0.1%
68 1
< 0.1%
69 1
< 0.1%
ValueCountFrequency (%)
9510 1
< 0.1%
6864 1
< 0.1%
6702 1
< 0.1%
5678 1
< 0.1%
5563 1
< 0.1%
5540 1
< 0.1%
5040 1
< 0.1%
4932 1
< 0.1%
4494 1
< 0.1%
4424 1
< 0.1%
Distinct954
Distinct (%)34.7%
Missing41
Missing (%)1.5%
Memory size21.9 KiB
2024-05-11T15:41:34.401745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length70
Median length25
Mean length5.7750273
Min length1

Characters and Unicode

Total characters15864
Distinct characters334
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique667 ?
Unique (%)24.3%

Sample

1st row우리건설
2nd row양우건설(주)
3rd row현대건설
4th row건영종합건설
5th row(주)동원건설
ValueCountFrequency (%)
현대건설 107
 
3.7%
삼성물산 101
 
3.5%
현대산업개발 88
 
3.0%
현대건설(주 51
 
1.8%
한신공영 48
 
1.7%
우성건설 47
 
1.6%
대우건설 47
 
1.6%
롯데건설 41
 
1.4%
gs건설 41
 
1.4%
대림산업 40
 
1.4%
Other values (897) 2276
78.8%
2024-05-11T15:41:34.972608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1639
 
10.3%
1548
 
9.8%
1156
 
7.3%
) 927
 
5.8%
( 922
 
5.8%
687
 
4.3%
600
 
3.8%
448
 
2.8%
419
 
2.6%
358
 
2.3%
Other values (324) 7160
45.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13173
83.0%
Close Punctuation 927
 
5.8%
Open Punctuation 922
 
5.8%
Uppercase Letter 329
 
2.1%
Other Punctuation 162
 
1.0%
Space Separator 153
 
1.0%
Lowercase Letter 119
 
0.8%
Decimal Number 66
 
0.4%
Dash Punctuation 10
 
0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1639
 
12.4%
1548
 
11.8%
1156
 
8.8%
687
 
5.2%
600
 
4.6%
448
 
3.4%
419
 
3.2%
358
 
2.7%
337
 
2.6%
281
 
2.1%
Other values (261) 5700
43.3%
Lowercase Letter
ValueCountFrequency (%)
s 29
24.4%
k 14
11.8%
h 13
10.9%
g 9
 
7.6%
c 8
 
6.7%
t 6
 
5.0%
p 5
 
4.2%
o 5
 
4.2%
w 4
 
3.4%
a 4
 
3.4%
Other values (12) 22
18.5%
Uppercase Letter
ValueCountFrequency (%)
S 102
31.0%
G 71
21.6%
H 35
 
10.6%
K 30
 
9.1%
C 29
 
8.8%
L 25
 
7.6%
D 11
 
3.3%
R 6
 
1.8%
I 5
 
1.5%
T 3
 
0.9%
Other values (8) 12
 
3.6%
Decimal Number
ValueCountFrequency (%)
1 17
25.8%
2 14
21.2%
0 10
15.2%
3 8
12.1%
4 5
 
7.6%
5 5
 
7.6%
8 5
 
7.6%
6 1
 
1.5%
7 1
 
1.5%
Other Punctuation
ValueCountFrequency (%)
, 121
74.7%
. 19
 
11.7%
/ 10
 
6.2%
& 7
 
4.3%
? 2
 
1.2%
: 2
 
1.2%
# 1
 
0.6%
Math Symbol
ValueCountFrequency (%)
= 1
50.0%
> 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 927
100.0%
Open Punctuation
ValueCountFrequency (%)
( 922
100.0%
Space Separator
ValueCountFrequency (%)
153
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13173
83.0%
Common 2243
 
14.1%
Latin 448
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1639
 
12.4%
1548
 
11.8%
1156
 
8.8%
687
 
5.2%
600
 
4.6%
448
 
3.4%
419
 
3.2%
358
 
2.7%
337
 
2.6%
281
 
2.1%
Other values (261) 5700
43.3%
Latin
ValueCountFrequency (%)
S 102
22.8%
G 71
15.8%
H 35
 
7.8%
K 30
 
6.7%
C 29
 
6.5%
s 29
 
6.5%
L 25
 
5.6%
k 14
 
3.1%
h 13
 
2.9%
D 11
 
2.5%
Other values (30) 89
19.9%
Common
ValueCountFrequency (%)
) 927
41.3%
( 922
41.1%
153
 
6.8%
, 121
 
5.4%
. 19
 
0.8%
1 17
 
0.8%
2 14
 
0.6%
/ 10
 
0.4%
- 10
 
0.4%
0 10
 
0.4%
Other values (13) 40
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13172
83.0%
ASCII 2691
 
17.0%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1639
 
12.4%
1548
 
11.8%
1156
 
8.8%
687
 
5.2%
600
 
4.6%
448
 
3.4%
419
 
3.2%
358
 
2.7%
337
 
2.6%
281
 
2.1%
Other values (260) 5699
43.3%
ASCII
ValueCountFrequency (%)
) 927
34.4%
( 922
34.3%
153
 
5.7%
, 121
 
4.5%
S 102
 
3.8%
G 71
 
2.6%
H 35
 
1.3%
K 30
 
1.1%
C 29
 
1.1%
s 29
 
1.1%
Other values (53) 272
 
10.1%
Compat Jamo
ValueCountFrequency (%)
1
100.0%

k-시행사
Text

MISSING 

Distinct1699
Distinct (%)62.2%
Missing55
Missing (%)2.0%
Memory size21.9 KiB
2024-05-11T15:41:35.407003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length7.9231614
Min length1

Characters and Unicode

Total characters21654
Distinct characters458
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1509 ?
Unique (%)55.2%

Sample

1st row경수재건축조합
2nd rowSH공사
3rd row현대건설
4th row철도조합
5th row월계동원베네스트아파트조합
ValueCountFrequency (%)
sh공사 185
 
5.6%
조합 64
 
1.9%
재건축조합 63
 
1.9%
현대건설 48
 
1.4%
주택조합 39
 
1.2%
현대산업개발 38
 
1.1%
재개발조합 37
 
1.1%
대한주택공사 35
 
1.1%
한신공영 30
 
0.9%
우성건설 29
 
0.9%
Other values (1802) 2765
83.0%
2024-05-11T15:41:36.017349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1213
 
5.6%
1033
 
4.8%
984
 
4.5%
908
 
4.2%
673
 
3.1%
661
 
3.1%
658
 
3.0%
618
 
2.9%
606
 
2.8%
505
 
2.3%
Other values (448) 13795
63.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18747
86.6%
Space Separator 606
 
2.8%
Decimal Number 601
 
2.8%
Close Punctuation 478
 
2.2%
Open Punctuation 472
 
2.2%
Uppercase Letter 435
 
2.0%
Lowercase Letter 139
 
0.6%
Other Punctuation 126
 
0.6%
Dash Punctuation 47
 
0.2%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1213
 
6.5%
1033
 
5.5%
984
 
5.2%
908
 
4.8%
673
 
3.6%
661
 
3.5%
658
 
3.5%
618
 
3.3%
505
 
2.7%
493
 
2.6%
Other values (399) 11001
58.7%
Uppercase Letter
ValueCountFrequency (%)
H 164
37.7%
S 164
37.7%
L 25
 
5.7%
G 17
 
3.9%
C 10
 
2.3%
K 7
 
1.6%
B 6
 
1.4%
R 6
 
1.4%
T 6
 
1.4%
A 5
 
1.1%
Other values (10) 25
 
5.7%
Decimal Number
ValueCountFrequency (%)
1 189
31.4%
2 118
19.6%
3 86
14.3%
4 61
 
10.1%
6 39
 
6.5%
5 38
 
6.3%
0 22
 
3.7%
7 21
 
3.5%
9 14
 
2.3%
8 13
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
h 65
46.8%
s 62
44.6%
k 4
 
2.9%
l 3
 
2.2%
t 2
 
1.4%
u 1
 
0.7%
c 1
 
0.7%
n 1
 
0.7%
Other Punctuation
ValueCountFrequency (%)
, 84
66.7%
. 33
 
26.2%
/ 5
 
4.0%
? 3
 
2.4%
1
 
0.8%
Space Separator
ValueCountFrequency (%)
606
100.0%
Close Punctuation
ValueCountFrequency (%)
) 478
100.0%
Open Punctuation
ValueCountFrequency (%)
( 472
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18748
86.6%
Common 2332
 
10.8%
Latin 574
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1213
 
6.5%
1033
 
5.5%
984
 
5.2%
908
 
4.8%
673
 
3.6%
661
 
3.5%
658
 
3.5%
618
 
3.3%
505
 
2.7%
493
 
2.6%
Other values (400) 11002
58.7%
Latin
ValueCountFrequency (%)
H 164
28.6%
S 164
28.6%
h 65
 
11.3%
s 62
 
10.8%
L 25
 
4.4%
G 17
 
3.0%
C 10
 
1.7%
K 7
 
1.2%
B 6
 
1.0%
R 6
 
1.0%
Other values (18) 48
 
8.4%
Common
ValueCountFrequency (%)
606
26.0%
) 478
20.5%
( 472
20.2%
1 189
 
8.1%
2 118
 
5.1%
3 86
 
3.7%
, 84
 
3.6%
4 61
 
2.6%
- 47
 
2.0%
6 39
 
1.7%
Other values (10) 152
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18743
86.6%
ASCII 2905
 
13.4%
Compat Jamo 4
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1213
 
6.5%
1033
 
5.5%
984
 
5.2%
908
 
4.8%
673
 
3.6%
661
 
3.5%
658
 
3.5%
618
 
3.3%
505
 
2.7%
493
 
2.6%
Other values (397) 10997
58.7%
ASCII
ValueCountFrequency (%)
606
20.9%
) 478
16.5%
( 472
16.2%
1 189
 
6.5%
H 164
 
5.6%
S 164
 
5.6%
2 118
 
4.1%
3 86
 
3.0%
, 84
 
2.9%
h 65
 
2.2%
Other values (37) 479
16.5%
Compat Jamo
ValueCountFrequency (%)
3
75.0%
1
 
25.0%
None
ValueCountFrequency (%)
1
50.0%
1
50.0%
Distinct2122
Distinct (%)76.5%
Missing14
Missing (%)0.5%
Memory size21.9 KiB
Minimum1968-05-30 00:00:00
Maximum2024-02-22 00:00:00
2024-05-11T15:41:36.197469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:41:36.391927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

k-연면적
Real number (ℝ)

SKEWED 

Distinct2724
Distinct (%)97.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean211929.84
Minimum0
Maximum1.8015525 × 108
Zeros26
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size24.6 KiB
2024-05-11T15:41:36.600849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13876.1
Q128673.5
median50041
Q392758
95-th percentile236867.8
Maximum1.8015525 × 108
Range1.8015525 × 108
Interquartile range (IQR)64084.5

Descriptive statistics

Standard deviation4454247.3
Coefficient of variation (CV)21.017556
Kurtosis1389.2569
Mean211929.84
Median Absolute Deviation (MAD)26039
Skewness36.866004
Sum5.9064848 × 108
Variance1.9840319 × 1013
MonotonicityNot monotonic
2024-05-11T15:41:36.859693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
 
0.9%
37340 2
 
0.1%
23813 2
 
0.1%
136790 2
 
0.1%
77917 2
 
0.1%
34828 2
 
0.1%
29220 2
 
0.1%
26306 2
 
0.1%
75185 2
 
0.1%
15041 2
 
0.1%
Other values (2714) 2743
98.4%
ValueCountFrequency (%)
0 26
0.9%
11 1
 
< 0.1%
5817 1
 
< 0.1%
5849 1
 
< 0.1%
6019 1
 
< 0.1%
6255 1
 
< 0.1%
6293 1
 
< 0.1%
6418 1
 
< 0.1%
6430 1
 
< 0.1%
6581 1
 
< 0.1%
ValueCountFrequency (%)
180155246 1
< 0.1%
147781069 1
< 0.1%
31057145 1
< 0.1%
9591851 1
< 0.1%
2456978 1
< 0.1%
1306341 1
< 0.1%
1142141 1
< 0.1%
971190 1
< 0.1%
935380 1
< 0.1%
935053 1
< 0.1%

k-주거전용면적
Real number (ℝ)

MISSING 

Distinct2704
Distinct (%)98.1%
Missing31
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean46249.98
Minimum2338
Maximum734781
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.6 KiB
2024-05-11T15:41:37.194067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2338
5-th percentile8035
Q116947
median29806
Q355281
95-th percentile135282.8
Maximum734781
Range732443
Interquartile range (IQR)38334

Descriptive statistics

Standard deviation53761.507
Coefficient of variation (CV)1.1624115
Kurtosis32.303089
Mean46249.98
Median Absolute Deviation (MAD)15303
Skewness4.4541367
Sum1.275112 × 108
Variance2.8902996 × 109
MonotonicityNot monotonic
2024-05-11T15:41:37.499067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14228 3
 
0.1%
15308 3
 
0.1%
27029 2
 
0.1%
24906 2
 
0.1%
21962 2
 
0.1%
21636 2
 
0.1%
19819 2
 
0.1%
18055 2
 
0.1%
18399 2
 
0.1%
16577 2
 
0.1%
Other values (2694) 2735
98.1%
(Missing) 31
 
1.1%
ValueCountFrequency (%)
2338 1
< 0.1%
2341 1
< 0.1%
2420 1
< 0.1%
2434 1
< 0.1%
2478 1
< 0.1%
2566 1
< 0.1%
2614 1
< 0.1%
2688 1
< 0.1%
2705 1
< 0.1%
2770 1
< 0.1%
ValueCountFrequency (%)
734781 1
< 0.1%
597298 1
< 0.1%
589196 1
< 0.1%
570727 1
< 0.1%
537573 1
< 0.1%
470140 1
< 0.1%
456872 1
< 0.1%
447330 1
< 0.1%
405926 1
< 0.1%
387755 1
< 0.1%

k-관리비부과면적
Real number (ℝ)

Distinct2718
Distinct (%)97.6%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean60738.777
Minimum0
Maximum2456978
Zeros26
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size24.6 KiB
2024-05-11T15:41:37.789321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10678.75
Q122140.5
median38522
Q371787.75
95-th percentile179348
Maximum2456978
Range2456978
Interquartile range (IQR)49647.25

Descriptive statistics

Standard deviation82026.388
Coefficient of variation (CV)1.3504781
Kurtosis274.63836
Mean60738.777
Median Absolute Deviation (MAD)19999
Skewness11.368629
Sum1.6921823 × 108
Variance6.7283284 × 109
MonotonicityNot monotonic
2024-05-11T15:41:38.212720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
 
0.9%
25406 2
 
0.1%
18545 2
 
0.1%
21278 2
 
0.1%
14166 2
 
0.1%
24595 2
 
0.1%
43036 2
 
0.1%
21360 2
 
0.1%
26110 2
 
0.1%
18502 2
 
0.1%
Other values (2708) 2742
98.4%
ValueCountFrequency (%)
0 26
0.9%
11 1
 
< 0.1%
294 1
 
< 0.1%
1197 1
 
< 0.1%
3441 1
 
< 0.1%
3778 1
 
< 0.1%
3993 1
 
< 0.1%
4005 1
 
< 0.1%
4109 1
 
< 0.1%
4271 1
 
< 0.1%
ValueCountFrequency (%)
2456978 1
< 0.1%
969877 1
< 0.1%
770030 1
< 0.1%
646257 1
< 0.1%
634679 1
< 0.1%
618666 1
< 0.1%
581999 1
< 0.1%
570727 1
< 0.1%
520773 1
< 0.1%
516492 1
< 0.1%

k-전용면적별세대현황(60㎡이하)
Real number (ℝ)

MISSING  ZEROS 

Distinct739
Distinct (%)26.8%
Missing29
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean243.36607
Minimum0
Maximum4975
Zeros796
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size24.6 KiB
2024-05-11T15:41:38.491776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median117
Q3291
95-th percentile984.2
Maximum4975
Range4975
Interquartile range (IQR)291

Descriptive statistics

Standard deviation393.34815
Coefficient of variation (CV)1.6162818
Kurtosis22.383568
Mean243.36607
Median Absolute Deviation (MAD)117
Skewness3.7922696
Sum671447
Variance154722.77
MonotonicityNot monotonic
2024-05-11T15:41:38.888653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 796
28.6%
120 20
 
0.7%
76 15
 
0.5%
81 14
 
0.5%
206 13
 
0.5%
150 12
 
0.4%
60 12
 
0.4%
75 11
 
0.4%
54 11
 
0.4%
66 11
 
0.4%
Other values (729) 1844
66.1%
(Missing) 29
 
1.0%
ValueCountFrequency (%)
0 796
28.6%
1 3
 
0.1%
2 3
 
0.1%
3 1
 
< 0.1%
4 3
 
0.1%
5 1
 
< 0.1%
6 2
 
0.1%
7 2
 
0.1%
8 2
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
4975 1
< 0.1%
3930 1
< 0.1%
3710 1
< 0.1%
3481 1
< 0.1%
2995 1
< 0.1%
2854 1
< 0.1%
2840 1
< 0.1%
2646 1
< 0.1%
2634 1
< 0.1%
2619 1
< 0.1%

k-전용면적별세대현황(60㎡~85㎡이하)
Real number (ℝ)

MISSING  ZEROS 

Distinct711
Distinct (%)25.8%
Missing29
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean241.83219
Minimum0
Maximum5132
Zeros609
Zeros (%)21.8%
Negative0
Negative (%)0.0%
Memory size24.6 KiB
2024-05-11T15:41:39.170673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q142.5
median152
Q3316.5
95-th percentile782.1
Maximum5132
Range5132
Interquartile range (IQR)274

Descriptive statistics

Standard deviation347.1359
Coefficient of variation (CV)1.4354413
Kurtosis46.867389
Mean241.83219
Median Absolute Deviation (MAD)140
Skewness5.1682449
Sum667215
Variance120503.33
MonotonicityNot monotonic
2024-05-11T15:41:39.455588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 609
 
21.8%
120 18
 
0.6%
160 17
 
0.6%
112 16
 
0.6%
90 15
 
0.5%
140 15
 
0.5%
104 15
 
0.5%
72 14
 
0.5%
144 13
 
0.5%
150 13
 
0.5%
Other values (701) 2014
72.2%
(Missing) 29
 
1.0%
ValueCountFrequency (%)
0 609
21.8%
1 5
 
0.2%
2 6
 
0.2%
3 1
 
< 0.1%
4 4
 
0.1%
5 2
 
0.1%
6 2
 
0.1%
7 2
 
0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
5132 1
< 0.1%
4424 1
< 0.1%
4260 1
< 0.1%
4042 1
< 0.1%
3930 1
< 0.1%
3590 1
< 0.1%
2678 1
< 0.1%
2676 1
< 0.1%
2598 1
< 0.1%
2522 1
< 0.1%

k-85㎡~135㎡이하
Real number (ℝ)

MISSING  ZEROS 

Distinct448
Distinct (%)16.2%
Missing29
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean92.702428
Minimum0
Maximum2728
Zeros1324
Zeros (%)47.5%
Negative0
Negative (%)0.0%
Memory size24.6 KiB
2024-05-11T15:41:39.878732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15
Q3106
95-th percentile430
Maximum2728
Range2728
Interquartile range (IQR)106

Descriptive statistics

Standard deviation182.77184
Coefficient of variation (CV)1.9715971
Kurtosis30.985624
Mean92.702428
Median Absolute Deviation (MAD)15
Skewness4.3040387
Sum255766
Variance33405.546
MonotonicityNot monotonic
2024-05-11T15:41:40.221383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1324
47.5%
38 23
 
0.8%
36 23
 
0.8%
30 21
 
0.8%
56 19
 
0.7%
40 18
 
0.6%
90 17
 
0.6%
44 16
 
0.6%
84 16
 
0.6%
76 16
 
0.6%
Other values (438) 1266
45.4%
(Missing) 29
 
1.0%
ValueCountFrequency (%)
0 1324
47.5%
1 11
 
0.4%
2 6
 
0.2%
3 3
 
0.1%
4 2
 
0.1%
5 2
 
0.1%
6 1
 
< 0.1%
8 6
 
0.2%
9 3
 
0.1%
10 4
 
0.1%
ValueCountFrequency (%)
2728 1
< 0.1%
1924 1
< 0.1%
1500 1
< 0.1%
1488 1
< 0.1%
1444 1
< 0.1%
1402 1
< 0.1%
1344 1
< 0.1%
1340 1
< 0.1%
1244 1
< 0.1%
1230 1
< 0.1%

k-135㎡초과
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.9 KiB
<NA>
2783 
0
 
3
70
 
1
1
 
1

Length

Max length4
Median length4
Mean length3.9949785
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2783
99.8%
0 3
 
0.1%
70 1
 
< 0.1%
1 1
 
< 0.1%

Length

2024-05-11T15:41:40.614148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:41:40.949582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2783
99.8%
0 3
 
0.1%
70 1
 
< 0.1%
1 1
 
< 0.1%

k-홈페이지
Text

MISSING 

Distinct732
Distinct (%)89.5%
Missing1970
Missing (%)70.7%
Memory size21.9 KiB
2024-05-11T15:41:41.815635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length74
Median length44
Mean length18.572127
Min length1

Characters and Unicode

Total characters15192
Distinct characters323
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique706 ?
Unique (%)86.3%

Sample

1st row없음
2nd row없음
3rd row없음
4th rowcafe.daum.net/sd-uneed
5th rowcafe.daum.net/sdaapt5
ValueCountFrequency (%)
없음 33
 
4.0%
happy.i-sh.co.kr 11
 
1.3%
www.kohom.co.kr 10
 
1.2%
www.i-sh.co.kr 9
 
1.1%
openapt.seoul.go.kr 7
 
0.8%
www.kohom.or.kr 6
 
0.7%
v2admin.aptner.com 5
 
0.6%
www.aptner.com 3
 
0.4%
3
 
0.4%
admin.aptner.com 3
 
0.4%
Other values (726) 745
89.2%
2024-05-11T15:41:42.679268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1951
 
12.8%
o 1015
 
6.7%
a 969
 
6.4%
c 799
 
5.3%
t 771
 
5.1%
p 768
 
5.1%
r 753
 
5.0%
i 650
 
4.3%
w 624
 
4.1%
k 579
 
3.8%
Other values (313) 6313
41.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10300
67.8%
Other Punctuation 2430
 
16.0%
Other Letter 1589
 
10.5%
Decimal Number 604
 
4.0%
Dash Punctuation 120
 
0.8%
Uppercase Letter 76
 
0.5%
Math Symbol 40
 
0.3%
Space Separator 23
 
0.2%
Connector Punctuation 4
 
< 0.1%
Close Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
53
 
3.3%
46
 
2.9%
42
 
2.6%
39
 
2.5%
38
 
2.4%
38
 
2.4%
36
 
2.3%
36
 
2.3%
35
 
2.2%
35
 
2.2%
Other values (244) 1191
75.0%
Lowercase Letter
ValueCountFrequency (%)
o 1015
 
9.9%
a 969
 
9.4%
c 799
 
7.8%
t 771
 
7.5%
p 768
 
7.5%
r 753
 
7.3%
i 650
 
6.3%
w 624
 
6.1%
k 579
 
5.6%
m 509
 
4.9%
Other values (16) 2863
27.8%
Uppercase Letter
ValueCountFrequency (%)
H 10
13.2%
A 8
10.5%
W 7
9.2%
P 6
 
7.9%
M 5
 
6.6%
T 5
 
6.6%
C 5
 
6.6%
S 5
 
6.6%
K 4
 
5.3%
G 4
 
5.3%
Other values (9) 17
22.4%
Decimal Number
ValueCountFrequency (%)
1 130
21.5%
2 110
18.2%
0 86
14.2%
7 69
11.4%
3 50
 
8.3%
5 36
 
6.0%
4 34
 
5.6%
6 31
 
5.1%
8 31
 
5.1%
9 27
 
4.5%
Other Punctuation
ValueCountFrequency (%)
. 1951
80.3%
/ 378
 
15.6%
: 56
 
2.3%
? 39
 
1.6%
, 3
 
0.1%
& 1
 
< 0.1%
@ 1
 
< 0.1%
# 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 120
100.0%
Math Symbol
ValueCountFrequency (%)
= 40
100.0%
Space Separator
ValueCountFrequency (%)
23
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10376
68.3%
Common 3227
 
21.2%
Hangul 1589
 
10.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
53
 
3.3%
46
 
2.9%
42
 
2.6%
39
 
2.5%
38
 
2.4%
38
 
2.4%
36
 
2.3%
36
 
2.3%
35
 
2.2%
35
 
2.2%
Other values (244) 1191
75.0%
Latin
ValueCountFrequency (%)
o 1015
 
9.8%
a 969
 
9.3%
c 799
 
7.7%
t 771
 
7.4%
p 768
 
7.4%
r 753
 
7.3%
i 650
 
6.3%
w 624
 
6.0%
k 579
 
5.6%
m 509
 
4.9%
Other values (35) 2939
28.3%
Common
ValueCountFrequency (%)
. 1951
60.5%
/ 378
 
11.7%
1 130
 
4.0%
- 120
 
3.7%
2 110
 
3.4%
0 86
 
2.7%
7 69
 
2.1%
: 56
 
1.7%
3 50
 
1.5%
= 40
 
1.2%
Other values (14) 237
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13603
89.5%
Hangul 1589
 
10.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1951
14.3%
o 1015
 
7.5%
a 969
 
7.1%
c 799
 
5.9%
t 771
 
5.7%
p 768
 
5.6%
r 753
 
5.5%
i 650
 
4.8%
w 624
 
4.6%
k 579
 
4.3%
Other values (59) 4724
34.7%
Hangul
ValueCountFrequency (%)
53
 
3.3%
46
 
2.9%
42
 
2.6%
39
 
2.5%
38
 
2.4%
38
 
2.4%
36
 
2.3%
36
 
2.3%
35
 
2.2%
35
 
2.2%
Other values (244) 1191
75.0%

k-등록일자
Date

MISSING 

Distinct430
Distinct (%)88.5%
Missing2302
Missing (%)82.6%
Memory size21.9 KiB
Minimum2017-02-01 10:49:21
Maximum2024-05-09 17:59:02
2024-05-11T15:41:43.090974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:41:43.568743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2753
Distinct (%)99.7%
Missing26
Missing (%)0.9%
Memory size21.9 KiB
Minimum2020-02-17 04:17:59
Maximum2024-05-11 04:00:23
2024-05-11T15:41:44.008220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:41:44.397185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2069
Distinct (%)98.4%
Missing685
Missing (%)24.6%
Memory size21.9 KiB
2024-05-11T15:41:44.875936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length11
Mean length11.985735
Min length1

Characters and Unicode

Total characters25206
Distinct characters17
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2044 ?
Unique (%)97.2%

Sample

1st row90800610361
2nd row911-00-18063-1
3rd row2158007586
4th row11382604260
5th row91303045251
ValueCountFrequency (%)
90700261231 5
 
0.2%
90700279241 4
 
0.2%
4
 
0.2%
0000 4
 
0.2%
20981344450 3
 
0.1%
90700313911 3
 
0.1%
00 3
 
0.1%
1 3
 
0.1%
90901943467 2
 
0.1%
907-00-27977-1 2
 
0.1%
Other values (2061) 2078
98.4%
2024-05-11T15:41:45.776406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6423
25.5%
1 3789
15.0%
9 2298
 
9.1%
- 2195
 
8.7%
2 2186
 
8.7%
8 1925
 
7.6%
7 1728
 
6.9%
6 1369
 
5.4%
3 1287
 
5.1%
5 997
 
4.0%
Other values (7) 1009
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22997
91.2%
Dash Punctuation 2195
 
8.7%
Space Separator 8
 
< 0.1%
Lowercase Letter 4
 
< 0.1%
Other Punctuation 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6423
27.9%
1 3789
16.5%
9 2298
 
10.0%
2 2186
 
9.5%
8 1925
 
8.4%
7 1728
 
7.5%
6 1369
 
6.0%
3 1287
 
5.6%
5 997
 
4.3%
4 995
 
4.3%
Lowercase Letter
ValueCountFrequency (%)
t 2
50.0%
e 1
25.0%
s 1
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 2195
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25201
> 99.9%
Latin 5
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6423
25.5%
1 3789
15.0%
9 2298
 
9.1%
- 2195
 
8.7%
2 2186
 
8.7%
8 1925
 
7.6%
7 1728
 
6.9%
6 1369
 
5.4%
3 1287
 
5.1%
5 997
 
4.0%
Other values (3) 1004
 
4.0%
Latin
ValueCountFrequency (%)
t 2
40.0%
e 1
20.0%
s 1
20.0%
A 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25206
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6423
25.5%
1 3789
15.0%
9 2298
 
9.1%
- 2195
 
8.7%
2 2186
 
8.7%
8 1925
 
7.6%
7 1728
 
6.9%
6 1369
 
5.4%
3 1287
 
5.1%
5 997
 
4.0%
Other values (7) 1009
 
4.0%

경비비관리형태
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size21.9 KiB
위탁
2140 
직영
467 
<NA>
 
85
위탁+직영
 
58
기타
 
38

Length

Max length5
Median length2
Mean length2.1233859
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
위탁 2140
76.8%
직영 467
 
16.8%
<NA> 85
 
3.0%
위탁+직영 58
 
2.1%
기타 38
 
1.4%

Length

2024-05-11T15:41:46.159606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:41:46.516724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
위탁 2140
76.8%
직영 467
 
16.8%
na 85
 
3.0%
위탁+직영 58
 
2.1%
기타 38
 
1.4%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.9 KiB
종합계약
1344 
단일계약
1241 
<NA>
203 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
종합계약 1344
48.2%
단일계약 1241
44.5%
<NA> 203
 
7.3%

Length

2024-05-11T15:41:46.778299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:41:47.093171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
종합계약 1344
48.2%
단일계약 1241
44.5%
na 203
 
7.3%

청소비관리형태
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size21.9 KiB
위탁
2360 
직영
264 
<NA>
 
83
기타
 
43
위탁+직영
 
38

Length

Max length5
Median length2
Mean length2.1004304
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
위탁 2360
84.6%
직영 264
 
9.5%
<NA> 83
 
3.0%
기타 43
 
1.5%
위탁+직영 38
 
1.4%

Length

2024-05-11T15:41:47.377877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:41:47.643046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
위탁 2360
84.6%
직영 264
 
9.5%
na 83
 
3.0%
기타 43
 
1.5%
위탁+직영 38
 
1.4%

건축면적
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct1577
Distinct (%)57.5%
Missing47
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean448693.6
Minimum0
Maximum9.1359627 × 108
Zeros1155
Zeros (%)41.4%
Negative0
Negative (%)0.0%
Memory size24.6 KiB
2024-05-11T15:41:47.991867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1581.69
Q34886.7
95-th percentile24316.87
Maximum9.1359627 × 108
Range9.1359627 × 108
Interquartile range (IQR)4886.7

Descriptive statistics

Standard deviation17562486
Coefficient of variation (CV)39.141378
Kurtosis2670.3659
Mean448693.6
Median Absolute Deviation (MAD)1581.69
Skewness51.387434
Sum1.2298692 × 109
Variance3.084409 × 1014
MonotonicityNot monotonic
2024-05-11T15:41:48.384146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1155
41.4%
2154.0 2
 
0.1%
1256.64 2
 
0.1%
1397.0 2
 
0.1%
2857.82 2
 
0.1%
1600.0 2
 
0.1%
1809.98 2
 
0.1%
2.0 2
 
0.1%
2882.53 2
 
0.1%
1206.0 2
 
0.1%
Other values (1567) 1568
56.2%
(Missing) 47
 
1.7%
ValueCountFrequency (%)
0.0 1155
41.4%
1.0 1
 
< 0.1%
2.0 2
 
0.1%
2.15 1
 
< 0.1%
2.35 1
 
< 0.1%
2.71 1
 
< 0.1%
100.0 1
 
< 0.1%
114.0 1
 
< 0.1%
123.0 1
 
< 0.1%
330.14 1
 
< 0.1%
ValueCountFrequency (%)
913596273.0 1
< 0.1%
88500459.0 1
< 0.1%
31596200.0 1
< 0.1%
24041400.0 1
< 0.1%
21387643.0 1
< 0.1%
17598789.0 1
< 0.1%
13572540.0 1
< 0.1%
10036874.0 1
< 0.1%
9095559.0 1
< 0.1%
8418385.0 1
< 0.1%

주차대수
Real number (ℝ)

MISSING  ZEROS 

Distinct1102
Distinct (%)40.1%
Missing37
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean566.82297
Minimum0
Maximum13154
Zeros222
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size24.6 KiB
2024-05-11T15:41:49.144421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1192
median339
Q3682
95-th percentile1723
Maximum13154
Range13154
Interquartile range (IQR)490

Descriptive statistics

Standard deviation766.68221
Coefficient of variation (CV)1.3525955
Kurtosis62.000103
Mean566.82297
Median Absolute Deviation (MAD)194
Skewness5.810824
Sum1559330
Variance587801.61
MonotonicityNot monotonic
2024-05-11T15:41:49.968407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 222
 
8.0%
1 14
 
0.5%
200 13
 
0.5%
160 13
 
0.5%
220 12
 
0.4%
206 11
 
0.4%
212 11
 
0.4%
250 11
 
0.4%
168 10
 
0.4%
330 10
 
0.4%
Other values (1092) 2424
86.9%
(Missing) 37
 
1.3%
ValueCountFrequency (%)
0 222
8.0%
1 14
 
0.5%
2 5
 
0.2%
3 2
 
0.1%
4 2
 
0.1%
5 2
 
0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
12 1
 
< 0.1%
20 1
 
< 0.1%
ValueCountFrequency (%)
13154 1
< 0.1%
12096 1
< 0.1%
9486 1
< 0.1%
7451 1
< 0.1%
6405 1
< 0.1%
6075 1
< 0.1%
5881 1
< 0.1%
5823 1
< 0.1%
5460 1
< 0.1%
5404 1
< 0.1%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.9 KiB
의무
2329 
임대
255 
임의
 
122
기타
 
82

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row의무
2nd row의무
3rd row임의
4th row의무
5th row의무

Common Values

ValueCountFrequency (%)
의무 2329
83.5%
임대 255
 
9.1%
임의 122
 
4.4%
기타 82
 
2.9%

Length

2024-05-11T15:41:50.436588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:41:50.737837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의무 2329
83.5%
임대 255
 
9.1%
임의 122
 
4.4%
기타 82
 
2.9%

단지승인일
Date

MISSING 

Distinct2712
Distinct (%)99.3%
Missing56
Missing (%)2.0%
Memory size21.9 KiB
Minimum1978-04-15 00:00:00
Maximum2024-05-10 12:51:46
2024-05-11T15:41:50.983065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:41:51.305501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

사용허가여부
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
True
2788 
ValueCountFrequency (%)
True 2788
100.0%
2024-05-11T15:41:51.553260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

관리비 업로드
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
False
2745 
True
 
43
ValueCountFrequency (%)
False 2745
98.5%
True 43
 
1.5%
2024-05-11T15:41:51.718560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌표X
Text

MISSING 

Distinct2701
Distinct (%)98.3%
Missing40
Missing (%)1.4%
Memory size21.9 KiB
2024-05-11T15:41:52.124658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length11
Mean length11.063683
Min length8

Characters and Unicode

Total characters30403
Distinct characters12
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

Unique2658 ?
Unique (%)96.7%

Sample

1st row126.9596386
2nd row127.1291789
3rd row127.1287745
4th row126.8406749
5th row127.0582196
ValueCountFrequency (%)
126.9764363 5
 
0.2%
127.0144609 3
 
0.1%
127.0079791 2
 
0.1%
126.9413984 2
 
0.1%
127.016339 2
 
0.1%
127.0484681 2
 
0.1%
127.0464538 2
 
0.1%
126.8425169 2
 
0.1%
126.8397859 2
 
0.1%
126.9425711 2
 
0.1%
Other values (2691) 2724
99.1%
2024-05-11T15:41:52.921548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4783
15.7%
2 4553
15.0%
7 3178
10.5%
6 2969
9.8%
. 2748
9.0%
0 2576
8.5%
9 2291
7.5%
8 2063
6.8%
5 1775
 
5.8%
4 1753
 
5.8%
Other values (2) 1714
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27654
91.0%
Other Punctuation 2749
 
9.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4783
17.3%
2 4553
16.5%
7 3178
11.5%
6 2969
10.7%
0 2576
9.3%
9 2291
8.3%
8 2063
7.5%
5 1775
 
6.4%
4 1753
 
6.3%
3 1713
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 2748
> 99.9%
, 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 30403
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4783
15.7%
2 4553
15.0%
7 3178
10.5%
6 2969
9.8%
. 2748
9.0%
0 2576
8.5%
9 2291
7.5%
8 2063
6.8%
5 1775
 
5.8%
4 1753
 
5.8%
Other values (2) 1714
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30403
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4783
15.7%
2 4553
15.0%
7 3178
10.5%
6 2969
9.8%
. 2748
9.0%
0 2576
8.5%
9 2291
7.5%
8 2063
6.8%
5 1775
 
5.8%
4 1753
 
5.8%
Other values (2) 1714
 
5.6%

좌표Y
Real number (ℝ)

MISSING 

Distinct2703
Distinct (%)98.4%
Missing40
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean37.551465
Minimum37.439508
Maximum37.687725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.6 KiB
2024-05-11T15:41:53.225829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.439508
5-th percentile37.477374
Q137.504997
median37.545063
Q337.590681
95-th percentile37.655275
Maximum37.687725
Range0.2482164
Interquartile range (IQR)0.08568419

Descriptive statistics

Standard deviation0.055369343
Coefficient of variation (CV)0.0014744922
Kurtosis-0.71093114
Mean37.551465
Median Absolute Deviation (MAD)0.041779564
Skewness0.44255083
Sum103191.42
Variance0.0030657641
MonotonicityNot monotonic
2024-05-11T15:41:53.536118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.4886826 5
 
0.2%
37.5464825 3
 
0.1%
37.4785869 2
 
0.1%
37.5580944 2
 
0.1%
37.540028 2
 
0.1%
37.5746696 2
 
0.1%
37.557906 2
 
0.1%
37.5024601 2
 
0.1%
37.4997285 2
 
0.1%
37.6265297 2
 
0.1%
Other values (2693) 2724
97.7%
(Missing) 40
 
1.4%
ValueCountFrequency (%)
37.4395084 1
< 0.1%
37.4478428 1
< 0.1%
37.4479487 1
< 0.1%
37.448100115 1
< 0.1%
37.4482097 1
< 0.1%
37.4482739 1
< 0.1%
37.4490446 1
< 0.1%
37.449377 1
< 0.1%
37.4494632 1
< 0.1%
37.45014991 1
< 0.1%
ValueCountFrequency (%)
37.6877248 1
< 0.1%
37.6876334 1
< 0.1%
37.6868254 1
< 0.1%
37.6860425 1
< 0.1%
37.6857192 1
< 0.1%
37.6846182 1
< 0.1%
37.683250096 1
< 0.1%
37.6829881 1
< 0.1%
37.682620803 1
< 0.1%
37.6825214 1
< 0.1%

단지신청일
Date

MISSING 

Distinct633
Distinct (%)22.9%
Missing29
Missing (%)1.0%
Memory size21.9 KiB
Minimum2013-01-12 09:00:00
Maximum2024-05-10 11:27:24
2024-05-11T15:41:53.804614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:41:54.082546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Sample

번호k-아파트코드k-아파트명k-단지분류(아파트,주상복합등등)kapt도로명주소주소(시도)k-apt주소split주소(시군구)주소(읍면동)나머지주소주소(도로명)주소(도로상세주소)k-전화번호k-팩스번호단지소개기존clob단지첨부파일k-세대타입(분양형태)k-관리방식k-복도유형k-난방방식k-전체동수k-전체세대수k-건설사(시공사)k-시행사k-사용검사일-사용승인일k-연면적k-주거전용면적k-관리비부과면적k-전용면적별세대현황(60㎡이하)k-전용면적별세대현황(60㎡~85㎡이하)k-85㎡~135㎡이하k-135㎡초과k-홈페이지k-등록일자k-수정일자고용보험관리번호경비비관리형태세대전기계약방법청소비관리형태건축면적주차대수기타/의무/임대/임의=1/2/3/4단지승인일사용허가여부관리비 업로드좌표X좌표Y단지신청일
01A15679103우리유앤미아파트서울특별시 동작구 서달로 83서울동작구흑석동우리유앤미아파트서달로83028127541028127542<NA><NA>분양위탁관리혼합식개별난방2206우리건설경수재건축조합2003-12-26 00:00:00.0270971582720098899324<NA><NA><NA>2024-05-09 13:42:18.090800610361위탁단일계약위탁1773.56223의무2018-04-10 15:59:42.0YN126.959638637.5006682013-03-07 09:46:59.0
12A13876112송파파인타운13단지아파트서울특별시 송파구 송파대로8길 10서울송파구장지동857송파대로8길10024002658024002668<NA><NA>분양위탁관리계단식개별난방4197양우건설(주)SH공사2011-01-27 00:00:00.030646167202252001970<NA><NA><NA>2024-05-08 00:12:18.0911-00-18063-1위탁단일계약위탁0.0225의무2013-06-17 19:03:30.0YN127.129178937.4768972013-03-07 09:46:59.0
23A13873701오금현대백조(임대)아파트서울특별시 송파구 양재대로72길 20서울송파구오금동20-2양재대로72길20024000298024000497214<NA>임대위탁관리복도식개별난방1153현대건설현대건설1997-01-24 00:00:00.073184743474315300<NA><NA><NA>2024-05-09 17:56:18.02158007586위탁단일계약위탁6892.7960임의2013-11-21 16:43:07.0YN127.128774537.5089062013-03-07 09:46:59.0
34A15275101개봉건영아파트서울특별시 구로구 고척로21나길 85-6서울구로구개봉동47-1고척로21나길85-602208786060226878616<NA><NA>분양위탁관리계단식개별난방2209건영종합건설철도조합1994-05-09 00:00:00.024523168942441002090<NA><NA><NA>2024-05-07 17:56:14.011382604260위탁단일계약위탁0.00의무2013-06-23 11:30:23.0YN126.840674937.5011622013-03-07 09:46:59.0
46A13991016월계동원베네스트아파트서울특별시 노원구 월계로53길 21서울노원구월계동서울시 노원구 월계2동 940번지월계로53길21029029567029909567<NA><NA>분양위탁관리계단식개별난방5205(주)동원건설월계동원베네스트아파트조합2005-03-31 00:00:00.02468014796185781031020<NA><NA><NA>2024-05-06 08:36:40.091303045251위탁단일계약위탁0.0206의무2015-06-19 09:59:49.0YN127.058219637.6317322013-03-07 09:46:59.0
57A13789201양재우성KBS(113동)아파트서울특별시 서초구 바우뫼로 91서울서초구양재동160-2바우뫼로91025798994025748994<NA><NA>분양위탁관리계단식개별난방1150우성건설우성건설1996-12-31 00:00:00.016533127041653301500<NA><NA><NA>2024-05-09 15:09:15.02148260850위탁종합계약위탁0.097의무2013-06-21 11:22:18.0YN127.029073937.4775012013-03-07 09:46:59.0
68A13486701천호삼익아파트서울특별시 강동구 상암로12길 13서울강동구천호동288-5상암로12길1302628599930262871298<NA><NA>분양위탁관리혼합식개별난방1150삼익건설(주)삼익건설(주)1997-12-03 00:00:00.0159251179315925675825<NA>없음<NA>2024-04-22 15:52:49.021280015640직영단일계약직영0.0152의무2019-03-26 17:32:53.0YN127.128472737.5485892013-03-07 09:47:00.0
79A14319001자양경남아너스빌아파트서울특별시 광진구 자양로3길 55서울광진구자양동경남아너스빌자양로3길5502343744420234374442130<NA>분양위탁관리혼합식개별난방1150대아건설진주타운재건축조합1997-05-24 00:00:00.018340109741527070800<NA><NA><NA>2024-05-10 09:57:34.090700036891위탁종합계약위탁1338.68132의무2016-03-14 14:37:54.0YN127.08399137.5294322013-03-07 09:47:01.0
810A12208101신사성원아파트<NA>서울은평구신사동360<NA><NA>023569537023569539<NA><NA>분양위탁관리혼합식개별난방1150성원건설성원건설2000-04-11 00:00:00.020062105921538286640<NA><NA><NA>2024-05-05 15:05:11.0110-80-00522-0위탁단일계약위탁0.0164의무2013-06-23 15:15:59.0YN126.913312237.6004572013-03-07 09:47:01.0
911A15809001신월대방샤인힐아파트서울특별시 양천구 신월로9길 37서울양천구신월동서울 양천구 신월4동 1017신월로9길3702269002620226900263<NA><NA>분양위탁관리계단식개별난방3150대방건설원광연립재건축조합2003-09-24 00:00:00.017033106791702760900<NA><NA><NA>2024-05-09 11:24:18.090700034491위탁단일계약위탁0.0149의무2013-05-24 10:50:13.0YN126.838499937.5204712013-03-07 09:47:01.0
번호k-아파트코드k-아파트명k-단지분류(아파트,주상복합등등)kapt도로명주소주소(시도)k-apt주소split주소(시군구)주소(읍면동)나머지주소주소(도로명)주소(도로상세주소)k-전화번호k-팩스번호단지소개기존clob단지첨부파일k-세대타입(분양형태)k-관리방식k-복도유형k-난방방식k-전체동수k-전체세대수k-건설사(시공사)k-시행사k-사용검사일-사용승인일k-연면적k-주거전용면적k-관리비부과면적k-전용면적별세대현황(60㎡이하)k-전용면적별세대현황(60㎡~85㎡이하)k-85㎡~135㎡이하k-135㎡초과k-홈페이지k-등록일자k-수정일자고용보험관리번호경비비관리형태세대전기계약방법청소비관리형태건축면적주차대수기타/의무/임대/임의=1/2/3/4단지승인일사용허가여부관리비 업로드좌표X좌표Y단지신청일
277840380A10022928강서금호어울림퍼스티어아파트서울특별시 강서구 화곡로54길 43서울강서구화곡동<NA>화곡로54길4302206522240220652225<NA><NA>기타위탁관리계단식개별난방5523금호건설(주),지에스건설(주),(주)대지건설한국토지주택공사2023-10-23 00:00:00.065625283483985552300<NA><NA>2023-11-18 15:55:17.02024-05-08 20:51:30.0<NA>위탁종합계약위탁4693.06636의무2023-12-07 18:13:41.0YN126.851064737.5507062023-12-07 17:14:49.0
277940386A10022851힐스테이트리슈빌강일아파트서울특별시 강동구 아리수로93다길 80서울강동구강일동<NA>아리수로93다길8002342732950234273297<NA><NA>분양위탁관리혼합식지역난방7809현대건설현대건설(주), 계룡건설산업(주)2023-11-01 00:00:00.014610972783946940562247<NA><NA>2024-01-04 19:04:06.02024-04-29 18:48:56.0<NA>위탁종합계약위탁15486.01086의무2024-01-11 13:47:18.0YN127.174806237.5737162024-01-11 13:34:05.0
278040387A10022859디에이치퍼스티어아이파크<NA>서울특별시 강남구 개포로 310서울강남구개포동<NA>개포로31002205818010220581860<NA><NA>분양위탁관리타워형지역난방746702현대산업개발, 현대건설현대산업개발, 현대건설2023-11-29 00:00:00.01306341570727570727207524831924<NA><NA>2024-01-07 01:00:15.02024-05-09 16:12:40.0<NA>위탁종합계약위탁58447.7213154의무2024-01-09 10:10:41.0YN127.057848037.4802872024-01-09 09:45:49.0
278140389A10022803연신내역루체스테이션아파트서울특별시 은평구 통일로 838-21서울은평구불광동<NA>통일로838-21023567106023567109<NA><NA>임대위탁관리복도식개별난방1264새천년건설(주)위드웰에셋2024-01-24 00:00:00.01153458441153426400<NA><NA>2024-02-02 16:59:42.02024-05-05 14:36:47.0<NA>위탁종합계약위탁771.9578임대2024-04-03 17:10:16.0YN126.923267837.6186892024-04-03 17:07:46.0
278240390A10022801최강타워주상복합서울특별시 관악구 남부순환로180길 6서울관악구신림동<NA>남부순환로180길6028839057028839058<NA><NA>임대직영복도식개별난방2299보미건설신림리더스하우징2023-05-30 00:00:00.02289099012289029900<NA><NA>2024-02-06 22:33:08.02024-05-09 15:07:09.0<NA>기타종합계약기타1662.0158임대2024-04-26 16:00:43.0YN126.930519437.4835692024-04-26 15:56:15.0
278340394A10022761힐스테이트 남산<NA>서울특별시 중구 퇴계로46길 26서울중구묵정동<NA>퇴계로46길2602227993320222799333<NA><NA>분양위탁관리복도식중앙난방2282현대건설(주)파빌리온충무로피에프브이(주)2024-01-29 00:00:00.032569110581592028200<NA><NA>2024-03-05 04:22:52.02024-05-10 04:29:12.0<NA>위탁종합계약위탁2982.98302의무2024-03-12 09:08:34.0YN126.999437037.5616782024-03-11 18:59:26.0
278440396A10022749원에디션강남<NA>서울특별시 강남구 언주로 563서울강남구역삼동<NA>언주로563025655800025655801<NA><NA>분양위탁관리타워형지역난방2229현대 엔지니어링(주)지엘스포월드피에프브이(주)2024-02-06 00:00:00.03029696363029622900<NA><NA>2024-03-08 13:05:13.02024-05-07 19:18:43.0<NA>위탁+직영종합계약위탁+직영3239.98229의무2024-03-12 11:26:25.0YN127.038266437.5078842024-03-12 10:11:38.0
278540397A10022756e편한세상 고덕 어반브릿지<NA>서울특별시 강동구 고덕로98길 75서울강동구상일동<NA>고덕로98길75024299260024299261<NA><NA>분양위탁관리혼합식지역난방6593DL 이앤씨(주)주식회사 강일고덕10피에프브이2024-02-22 00:00:00.010546252849696920419174<NA><NA>2024-03-08 16:40:42.02024-05-09 18:31:21.0<NA>위탁종합계약위탁10164.67796의무2024-04-12 13:47:43.0YN127.180692237.555632024-04-12 13:35:38.0
278640399A10022715고덕풍경채어바니티아파트서울특별시 강동구 아리수로 375서울강동구고덕동<NA>아리수로375024819115024819116<NA><NA>분양위탁관리혼합식지역난방6780제일건설고덕강일1피에프브이2024-01-30 00:00:00.018380069378963850561219<NA><NA>2024-03-30 13:25:53.02024-05-09 18:25:09.0<NA>위탁종합계약위탁+직영14512.971353의무2024-04-08 13:38:09.0YN127.168671037.5634012024-04-08 13:19:59.0
278740407A10022649송파더플래티넘<NA>서울특별시 송파구 성내천로6길 1-13서울송파구오금동<NA>성내천로6길1-13024498330024498331<NA><NA>분양직영계단식지역난방2328주식회사쌍용아남아파트리모델링주택조합2024-01-30 00:00:00.049938276013625530119179<NA><NA>2024-05-09 17:59:02.02024-05-09 17:59:02.0<NA>위탁종합계약위탁3020.38320의무2024-05-10 12:51:46.0YN127.136305637.5032972024-05-10 11:27:24.0