Overview

Dataset statistics

Number of variables7
Number of observations336
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.8 KiB
Average record size in memory57.4 B

Variable types

Text3
Categorical2
DateTime1
Numeric1

Dataset

Description주택관리공단 지자체별 임대주택 단지현황에 대한 데이터로 단지명, 소재지 주소, 유형, 입주개시일, 세대수 등의 항목을 제공합니다.
Author주택관리공단(주)
URLhttps://www.data.go.kr/data/15068613/fileData.do

Alerts

단지명 has unique valuesUnique
소재지 주소 has unique valuesUnique
관리소 연락처 has unique valuesUnique

Reproduction

Analysis started2024-04-21 12:17:27.730289
Analysis finished2024-04-21 12:17:29.238892
Duration1.51 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

단지명
Text

UNIQUE 

Distinct336
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-04-21T21:17:29.831871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length8
Mean length8.8571429
Min length5

Characters and Unicode

Total characters2976
Distinct characters254
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

Unique336 ?
Unique (%)100.0%

Sample

1st row서울수서아파트
2nd row서울번동2아파트
3rd row서울번동3아파트
4th row서울번동5아파트
5th row서울등촌1아파트
ValueCountFrequency (%)
서울수서아파트 1
 
0.3%
청주성화1아파트 1
 
0.3%
군산창성아파트 1
 
0.3%
경주금장아파트 1
 
0.3%
칠곡왜관3아파트 1
 
0.3%
영천문내아파트 1
 
0.3%
전주효자4-3아파트 1
 
0.3%
전주효자4-1아파트 1
 
0.3%
무안남악회룡마을아파트 1
 
0.3%
대전삼성1아파트 1
 
0.3%
Other values (327) 327
97.0%
2024-04-21T21:17:31.069548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 324
 
10.9%
파 310
 
10.4%
트 306
 
10.3%
주 99
 
3.3%
1 94
 
3.2%
산 79
 
2.7%
2 62
 
2.1%
천 47
 
1.6%
3 44
 
1.5%
대 44
 
1.5%
Other values (244) 1567
52.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2649
89.0%
Decimal Number 268
 
9.0%
Open Punctuation 21
 
0.7%
Close Punctuation 21
 
0.7%
Dash Punctuation 7
 
0.2%
Uppercase Letter 6
 
0.2%
Space Separator 2
 
0.1%
Control 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 324
 
12.2%
파 310
 
11.7%
트 306
 
11.6%
주 99
 
3.7%
산 79
 
3.0%
천 47
 
1.8%
대 44
 
1.7%
동 36
 
1.4%
원 33
 
1.2%
부 33
 
1.2%
Other values (225) 1338
50.5%
Decimal Number
ValueCountFrequency (%)
1 94
35.1%
2 62
23.1%
3 44
16.4%
4 23
 
8.6%
6 13
 
4.9%
5 10
 
3.7%
7 9
 
3.4%
9 6
 
2.2%
8 5
 
1.9%
0 2
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
C 2
33.3%
J 2
33.3%
D 2
33.3%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Control
ValueCountFrequency (%)
1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2649
89.0%
Common 321
 
10.8%
Latin 6
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 324
 
12.2%
파 310
 
11.7%
트 306
 
11.6%
주 99
 
3.7%
산 79
 
3.0%
천 47
 
1.8%
대 44
 
1.7%
동 36
 
1.4%
원 33
 
1.2%
부 33
 
1.2%
Other values (225) 1338
50.5%
Common
ValueCountFrequency (%)
1 94
29.3%
2 62
19.3%
3 44
13.7%
4 23
 
7.2%
( 21
 
6.5%
) 21
 
6.5%
6 13
 
4.0%
5 10
 
3.1%
7 9
 
2.8%
- 7
 
2.2%
Other values (6) 17
 
5.3%
Latin
ValueCountFrequency (%)
C 2
33.3%
J 2
33.3%
D 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2649
89.0%
ASCII 327
 
11.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 324
 
12.2%
파 310
 
11.7%
트 306
 
11.6%
주 99
 
3.7%
산 79
 
3.0%
천 47
 
1.8%
대 44
 
1.7%
동 36
 
1.4%
원 33
 
1.2%
부 33
 
1.2%
Other values (225) 1338
50.5%
ASCII
ValueCountFrequency (%)
1 94
28.7%
2 62
19.0%
3 44
13.5%
4 23
 
7.0%
( 21
 
6.4%
) 21
 
6.4%
6 13
 
4.0%
5 10
 
3.1%
7 9
 
2.8%
- 7
 
2.1%
Other values (9) 23
 
7.0%

소재지 주소
Text

UNIQUE 

Distinct336
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-04-21T21:17:32.475842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length34
Mean length19.616071
Min length12

Characters and Unicode

Total characters6591
Distinct characters281
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique336 ?
Unique (%)100.0%

Sample

1st row서울특별시 강남구 광평로51길 49
2nd row서울특별시 강북구 한천로 105길 24
3rd row서울특별시 강북구 오현로 208
4th row서울특별시 강북구 한천로 115길 20
5th row서울특별시 강서구 강서로68길 36
ValueCountFrequency (%)
경기도 73
 
4.7%
경상북도 24
 
1.6%
북구 24
 
1.6%
전라북도 23
 
1.5%
서울특별시 22
 
1.4%
강원도 22
 
1.4%
충청북도 21
 
1.4%
경상남도 19
 
1.2%
부산광역시 17
 
1.1%
대구광역시 17
 
1.1%
Other values (814) 1286
83.1%
2024-04-21T21:17:34.304414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1217
 
18.5%
시 316
 
4.8%
로 282
 
4.3%
도 242
 
3.7%
1 234
 
3.6%
구 179
 
2.7%
2 176
 
2.7%
길 138
 
2.1%
3 127
 
1.9%
경 126
 
1.9%
Other values (271) 3554
53.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4164
63.2%
Space Separator 1217
 
18.5%
Decimal Number 1119
 
17.0%
Dash Punctuation 37
 
0.6%
Other Punctuation 19
 
0.3%
Open Punctuation 17
 
0.3%
Close Punctuation 17
 
0.3%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 316
 
7.6%
로 282
 
6.8%
도 242
 
5.8%
구 179
 
4.3%
길 138
 
3.3%
경 126
 
3.0%
광 114
 
2.7%
북 111
 
2.7%
산 93
 
2.2%
주 93
 
2.2%
Other values (254) 2470
59.3%
Decimal Number
ValueCountFrequency (%)
1 234
20.9%
2 176
15.7%
3 127
11.3%
5 106
9.5%
4 101
9.0%
0 91
 
8.1%
6 89
 
8.0%
7 81
 
7.2%
9 58
 
5.2%
8 56
 
5.0%
Other Punctuation
ValueCountFrequency (%)
, 18
94.7%
* 1
 
5.3%
Space Separator
ValueCountFrequency (%)
1217
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4164
63.2%
Common 2426
36.8%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 316
 
7.6%
로 282
 
6.8%
도 242
 
5.8%
구 179
 
4.3%
길 138
 
3.3%
경 126
 
3.0%
광 114
 
2.7%
북 111
 
2.7%
산 93
 
2.2%
주 93
 
2.2%
Other values (254) 2470
59.3%
Common
ValueCountFrequency (%)
1217
50.2%
1 234
 
9.6%
2 176
 
7.3%
3 127
 
5.2%
5 106
 
4.4%
4 101
 
4.2%
0 91
 
3.8%
6 89
 
3.7%
7 81
 
3.3%
9 58
 
2.4%
Other values (6) 146
 
6.0%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4164
63.2%
ASCII 2427
36.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1217
50.1%
1 234
 
9.6%
2 176
 
7.3%
3 127
 
5.2%
5 106
 
4.4%
4 101
 
4.2%
0 91
 
3.7%
6 89
 
3.7%
7 81
 
3.3%
9 58
 
2.4%
Other values (7) 147
 
6.1%
Hangul
ValueCountFrequency (%)
시 316
 
7.6%
로 282
 
6.8%
도 242
 
5.8%
구 179
 
4.3%
길 138
 
3.3%
경 126
 
3.0%
광 114
 
2.7%
북 111
 
2.7%
산 93
 
2.2%
주 93
 
2.2%
Other values (254) 2470
59.3%

임대유형
Categorical

Distinct25
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
국민임대
124 
영구임대, 임대상가
115 
공공임대(50년)
36 
군주거시설
17 
공공임대(50년), 영구임대, 임대상가
 
7
Other values (20)
37 

Length

Max length32
Median length24
Mean length7.7559524
Min length4

Unique

Unique13 ?
Unique (%)3.9%

Sample

1st row영구임대, 임대상가
2nd row영구임대, 임대상가
3rd row영구임대, 임대상가, 행복주택
4th row영구임대, 임대상가
5th row영구임대, 임대상가

Common Values

ValueCountFrequency (%)
국민임대 124
36.9%
영구임대, 임대상가 115
34.2%
공공임대(50년) 36
 
10.7%
군주거시설 17
 
5.1%
공공임대(50년), 영구임대, 임대상가 7
 
2.1%
국민임대, 임대상가 6
 
1.8%
분양주택, 국민임대 5
 
1.5%
영구임대 4
 
1.2%
공공임대(10년) 3
 
0.9%
공공임대(50년), 임대상가 2
 
0.6%
Other values (15) 17
 
5.1%

Length

2024-04-21T21:17:34.736053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
국민임대 140
28.1%
임대상가 135
27.1%
영구임대 132
26.5%
공공임대(50년 47
 
9.4%
군주거시설 17
 
3.4%
분양주택 10
 
2.0%
행복주택 5
 
1.0%
공공임대(10년 4
 
0.8%
na 2
 
0.4%
기존주택매입임대 1
 
0.2%
Other values (6) 6
 
1.2%

관리소 연락처
Text

UNIQUE 

Distinct336
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-04-21T21:17:35.662669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.958333
Min length11

Characters and Unicode

Total characters4018
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

Unique336 ?
Unique (%)100.0%

Sample

1st row02-459-1567
2nd row02-987-1462
3rd row02-984-6152
4th row02-987-3588
5th row02-2658-5000
ValueCountFrequency (%)
02-459-1567 1
 
0.3%
043-237-2366 1
 
0.3%
063-442-7815 1
 
0.3%
054-777-2101 1
 
0.3%
054-976-4545 1
 
0.3%
054-336-1601 1
 
0.3%
063-225-6910 1
 
0.3%
063-229-9490 1
 
0.3%
061-284-9953 1
 
0.3%
042-623-7900 1
 
0.3%
Other values (326) 326
97.0%
2024-04-21T21:17:37.057445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 672
16.7%
0 535
13.3%
3 449
11.2%
5 365
9.1%
1 351
8.7%
2 342
8.5%
4 326
8.1%
6 322
8.0%
8 236
 
5.9%
7 232
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3346
83.3%
Dash Punctuation 672
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 535
16.0%
3 449
13.4%
5 365
10.9%
1 351
10.5%
2 342
10.2%
4 326
9.7%
6 322
9.6%
8 236
7.1%
7 232
6.9%
9 188
 
5.6%
Dash Punctuation
ValueCountFrequency (%)
- 672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4018
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 672
16.7%
0 535
13.3%
3 449
11.2%
5 365
9.1%
1 351
8.7%
2 342
8.5%
4 326
8.1%
6 322
8.0%
8 236
 
5.9%
7 232
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4018
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 672
16.7%
0 535
13.3%
3 449
11.2%
5 365
9.1%
1 351
8.7%
2 342
8.5%
4 326
8.1%
6 322
8.0%
8 236
 
5.9%
7 232
 
5.8%

입주개시일
Date

Distinct295
Distinct (%)87.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Minimum1983-09-30 00:00:00
Maximum2023-01-27 00:00:00
2024-04-21T21:17:37.459309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:17:37.897528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

세대수
Real number (ℝ)

Distinct276
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean888.60119
Minimum71
Maximum3861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-04-21T21:17:38.197296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum71
5-th percentile243.5
Q1487.25
median714
Q31144.5
95-th percentile1969
Maximum3861
Range3790
Interquartile range (IQR)657.25

Descriptive statistics

Standard deviation598.31612
Coefficient of variation (CV)0.67332356
Kurtosis4.4904461
Mean888.60119
Median Absolute Deviation (MAD)277
Skewness1.7897788
Sum298570
Variance357982.18
MonotonicityNot monotonic
2024-04-21T21:17:38.664669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
984 5
 
1.5%
480 4
 
1.2%
925 3
 
0.9%
1650 3
 
0.9%
476 3
 
0.9%
638 3
 
0.9%
428 3
 
0.9%
390 3
 
0.9%
447 3
 
0.9%
1074 3
 
0.9%
Other values (266) 303
90.2%
ValueCountFrequency (%)
71 1
 
0.3%
79 1
 
0.3%
80 1
 
0.3%
100 1
 
0.3%
120 1
 
0.3%
128 1
 
0.3%
138 1
 
0.3%
150 3
0.9%
152 1
 
0.3%
194 1
 
0.3%
ValueCountFrequency (%)
3861 1
0.3%
3593 1
0.3%
3292 1
0.3%
3264 1
0.3%
3151 1
0.3%
2953 1
0.3%
2634 1
0.3%
2565 1
0.3%
2529 1
0.3%
2415 1
0.3%

난방방식
Categorical

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
개별난방
176 
중앙난방
83 
지역난방
77 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지역난방
2nd row중앙난방
3rd row중앙난방
4th row중앙난방
5th row지역난방

Common Values

ValueCountFrequency (%)
개별난방 176
52.4%
중앙난방 83
24.7%
지역난방 77
22.9%

Length

2024-04-21T21:17:38.911347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:39.183722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개별난방 176
52.4%
중앙난방 83
24.7%
지역난방 77
22.9%

Interactions

2024-04-21T21:17:28.351847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T21:17:39.383653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
임대유형세대수난방방식
임대유형1.0000.3160.734
세대수0.3161.0000.462
난방방식0.7340.4621.000
2024-04-21T21:17:39.618158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
임대유형난방방식
임대유형1.0000.453
난방방식0.4531.000
2024-04-21T21:17:39.852302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수임대유형난방방식
세대수1.0000.1220.310
임대유형0.1221.0000.453
난방방식0.3100.4531.000

Missing values

2024-04-21T21:17:28.710520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T21:17:29.089792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

단지명소재지 주소임대유형관리소 연락처입주개시일세대수난방방식
0서울수서아파트서울특별시 강남구 광평로51길 49영구임대, 임대상가02-459-15671992-10-312565지역난방
1서울번동2아파트서울특별시 강북구 한천로 105길 24영구임대, 임대상가02-987-14621991-05-201766중앙난방
2서울번동3아파트서울특별시 강북구 오현로 208영구임대, 임대상가, 행복주택02-984-61521990-11-101292중앙난방
3서울번동5아파트서울특별시 강북구 한천로 115길 20영구임대, 임대상가02-987-35881991-05-151123중앙난방
4서울등촌1아파트서울특별시 강서구 강서로68길 36영구임대, 임대상가02-2658-50001994-11-111670지역난방
5서울등촌4아파트서울특별시 강서구 공항대로39길 59영구임대, 임대상가02-2658-20771995-05-291575지역난방
6서울등촌6아파트서울특별시 강서구 화곡로63가길 18공공임대(50년), 임대상가02-2658-37461995-10-02559지역난방
7서울등촌7아파트서울특별시 강서구 공항대로 43길 104영구임대, 임대상가02-2658-01111994-11-071146지역난방
8서울등촌9아파트서울특별시 강서구 화곡로 63가길 92영구임대, 임대상가02-2658-80001994-11-111445지역난방
9서울가양아파트서울특별시 강서구 허준로 209영구임대, 임대상가02-2668-30881992-09-301998지역난방
단지명소재지 주소임대유형관리소 연락처입주개시일세대수난방방식
326동화천주거지원사업소강원도 화천군 화천읍 노신로17 푸르미아파트 A동 106호군주거시설033-442-99072019-06-211465개별난방
327파주2주거지원사업소경기도 파주시 문산읍 선유리 444군주거시설031-939-09632018-01-013264개별난방
328양주의정부주거지원사업소경기도 양주시 화합로 1325번길 112 나동 102호군주거시설031-822-75512018-01-011618중앙난방
329서화천주거지원사업소강원도 화천군 사내면 사내로3길 66군주거시설033-441-15392019-06-212212개별난방
330원주주거지원사업소강원도 원주시 치악로 1690군주거시설033-763-28652019-06-211650개별난방
331도계새롬관리소강원도 삼척시 도계읍 도계우회로2길 8공공임대(5년), 분양주택070-7436-25002019-04-15200개별난방
332국방대아파트주거지원센터충청남도 논산시 양촌면 황산벌로 1090, 1089번길 46군주거시설041-735-67012019-06-211074개별난방
333춘천주거지원사업소춘천시 영서로 2793군주거시설033-241-09832019-06-211309개별난방
334다시봄입주민지원센터경상남도 고성군 고성읍 송학로 47-3영구임대055-953-99012021-01-25100개별난방
335홍천북방관리소강원도 홍천군 북방면 홍천로 211영구임대033-956-99192023-01-27128개별난방