Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells2368
Missing cells (%)2.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory820.3 KiB
Average record size in memory84.0 B

Variable types

Numeric4
Text2
Categorical1
DateTime2

Dataset

Description한국토지주택공사가 제공하는 임대주택단지의 단지명, 주소, 공급유형, 공급면적등 임대주택단지에 관련된 정보를 제공합니다.
Author한국토지주택공사
URLhttps://www.data.go.kr/data/15050701/fileData.do

Alerts

공급면적 is highly overall correlated with 세대수High correlation
세대수 is highly overall correlated with 공급면적High correlation
공급유형 is highly imbalanced (84.3%)Imbalance
건축사용승인일자 has 404 (4.0%) missing valuesMissing
제품대체일자 has 1961 (19.6%) missing valuesMissing
순번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 21:46:30.355788
Analysis finished2023-12-12 21:46:34.391671
Duration4.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9288.1553
Minimum2
Maximum18534
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:46:34.463988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile954.9
Q14701.75
median9285.5
Q313813.25
95-th percentile17615.1
Maximum18534
Range18532
Interquartile range (IQR)9111.5

Descriptive statistics

Standard deviation5309.395
Coefficient of variation (CV)0.57163073
Kurtosis-1.172531
Mean9288.1553
Median Absolute Deviation (MAD)4554
Skewness-0.0071522181
Sum92881553
Variance28189675
MonotonicityNot monotonic
2023-12-13T06:46:34.620319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76 1
 
< 0.1%
17267 1
 
< 0.1%
102 1
 
< 0.1%
2027 1
 
< 0.1%
5052 1
 
< 0.1%
13360 1
 
< 0.1%
4371 1
 
< 0.1%
12830 1
 
< 0.1%
155 1
 
< 0.1%
6419 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
2 1
< 0.1%
5 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%
12 1
< 0.1%
15 1
< 0.1%
ValueCountFrequency (%)
18534 1
< 0.1%
18532 1
< 0.1%
18530 1
< 0.1%
18529 1
< 0.1%
18527 1
< 0.1%
18526 1
< 0.1%
18521 1
< 0.1%
18520 1
< 0.1%
18514 1
< 0.1%
18512 1
< 0.1%

단지명
Text

Distinct255
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T06:46:34.890758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length12
Mean length12.2048
Min length7

Characters and Unicode

Total characters122048
Distinct characters144
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)0.5%

Sample

1st row매입다가구(서울광진구)
2nd row매입다가구(인천부평구)
3rd row매입다가구(경기남양주시)
4th row매입다가구(서울금천구)
5th row매입다가구(부산동구)
ValueCountFrequency (%)
매입다가구(충북청주시 384
 
3.8%
매입다가구(인천미추홀구 304
 
3.0%
매입다가구(경기수원시 291
 
2.9%
매입다가구(경기안산시 266
 
2.7%
매입다가구(전북전주시 228
 
2.3%
매입다가구(대구달성군 206
 
2.1%
매입다가구(부산북구 204
 
2.0%
매입다가구(경기남양주시 198
 
2.0%
매입다가구(경남창원시 194
 
1.9%
매입다가구(광주북구 187
 
1.9%
Other values (249) 7547
75.4%
2023-12-13T06:46:35.335865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 15169
12.4%
( 9999
 
8.2%
) 9997
 
8.2%
매 9976
 
8.2%
입 9976
 
8.2%
가 9412
 
7.7%
다 9403
 
7.7%
시 4906
 
4.0%
경 3417
 
2.8%
부 2708
 
2.2%
Other values (134) 37085
30.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 102034
83.6%
Open Punctuation 10003
 
8.2%
Close Punctuation 10001
 
8.2%
Space Separator 9
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 15169
14.9%
매 9976
 
9.8%
입 9976
 
9.8%
가 9412
 
9.2%
다 9403
 
9.2%
시 4906
 
4.8%
경 3417
 
3.3%
부 2708
 
2.7%
산 2356
 
2.3%
기 2306
 
2.3%
Other values (128) 32405
31.8%
Open Punctuation
ValueCountFrequency (%)
( 9999
> 99.9%
[ 4
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 9997
> 99.9%
] 4
 
< 0.1%
Space Separator
ValueCountFrequency (%)
9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 102034
83.6%
Common 20014
 
16.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 15169
14.9%
매 9976
 
9.8%
입 9976
 
9.8%
가 9412
 
9.2%
다 9403
 
9.2%
시 4906
 
4.8%
경 3417
 
3.3%
부 2708
 
2.7%
산 2356
 
2.3%
기 2306
 
2.3%
Other values (128) 32405
31.8%
Common
ValueCountFrequency (%)
( 9999
50.0%
) 9997
50.0%
9
 
< 0.1%
] 4
 
< 0.1%
[ 4
 
< 0.1%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 102034
83.6%
ASCII 20014
 
16.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 15169
14.9%
매 9976
 
9.8%
입 9976
 
9.8%
가 9412
 
9.2%
다 9403
 
9.2%
시 4906
 
4.8%
경 3417
 
3.3%
부 2708
 
2.7%
산 2356
 
2.3%
기 2306
 
2.3%
Other values (128) 32405
31.8%
ASCII
ValueCountFrequency (%)
( 9999
50.0%
) 9997
50.0%
9
 
< 0.1%
] 4
 
< 0.1%
[ 4
 
< 0.1%
- 1
 
< 0.1%

주소
Text

Distinct6520
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T06:46:35.713994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length37
Mean length25.9617
Min length11

Characters and Unicode

Total characters259617
Distinct characters592
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5545 ?
Unique (%)55.5%

Sample

1st row서울특별시 광진구 동일로66길
2nd row인천광역시 부평구 평천로 368(갈산동)
3rd row경기도 남양주시 의안로 155(평내동 평내마을신명스카이뷰아파트)
4th row서울특별시 금천구 시흥대로94길 39-7(독산동)
5th row부산광역시 동구 망양로 850(좌천동 좌천 서린 엘마르 더뷰)
ValueCountFrequency (%)
경기도 2293
 
5.1%
서울특별시 1154
 
2.5%
부산광역시 1093
 
2.4%
대구광역시 805
 
1.8%
인천광역시 740
 
1.6%
경상남도 652
 
1.4%
북구 585
 
1.3%
광주광역시 529
 
1.2%
대전광역시 475
 
1.0%
충청북도 450
 
1.0%
Other values (10635) 36614
80.7%
2023-12-13T06:46:36.279853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35500
 
13.7%
시 10533
 
4.1%
로 8949
 
3.4%
구 8392
 
3.2%
동 8112
 
3.1%
1 7699
 
3.0%
) 6843
 
2.6%
( 6843
 
2.6%
길 6835
 
2.6%
도 5645
 
2.2%
Other values (582) 154266
59.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 172611
66.5%
Space Separator 35500
 
13.7%
Decimal Number 34838
 
13.4%
Close Punctuation 6843
 
2.6%
Open Punctuation 6843
 
2.6%
Dash Punctuation 2222
 
0.9%
Uppercase Letter 562
 
0.2%
Lowercase Letter 183
 
0.1%
Other Punctuation 11
 
< 0.1%
Letter Number 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 10533
 
6.1%
로 8949
 
5.2%
구 8392
 
4.9%
동 8112
 
4.7%
길 6835
 
4.0%
도 5645
 
3.3%
광 4876
 
2.8%
산 4155
 
2.4%
역 4087
 
2.4%
번 3911
 
2.3%
Other values (525) 107116
62.1%
Uppercase Letter
ValueCountFrequency (%)
S 96
17.1%
K 66
11.7%
I 45
 
8.0%
U 42
 
7.5%
H 39
 
6.9%
A 30
 
5.3%
B 26
 
4.6%
J 21
 
3.7%
E 18
 
3.2%
C 18
 
3.2%
Other values (14) 161
28.6%
Lowercase Letter
ValueCountFrequency (%)
e 46
25.1%
o 24
13.1%
w 22
12.0%
v 20
10.9%
l 20
10.9%
i 20
10.9%
y 11
 
6.0%
k 11
 
6.0%
t 3
 
1.6%
h 2
 
1.1%
Other values (4) 4
 
2.2%
Decimal Number
ValueCountFrequency (%)
1 7699
22.1%
2 5368
15.4%
3 3796
10.9%
5 3217
9.2%
4 3050
 
8.8%
6 2744
 
7.9%
7 2410
 
6.9%
0 2344
 
6.7%
8 2207
 
6.3%
9 2003
 
5.7%
Letter Number
ValueCountFrequency (%)
Ⅱ 2
50.0%
Ⅰ 1
25.0%
Ⅲ 1
25.0%
Other Punctuation
ValueCountFrequency (%)
· 10
90.9%
& 1
 
9.1%
Space Separator
ValueCountFrequency (%)
35500
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6843
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6843
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2222
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 172611
66.5%
Common 86257
33.2%
Latin 749
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 10533
 
6.1%
로 8949
 
5.2%
구 8392
 
4.9%
동 8112
 
4.7%
길 6835
 
4.0%
도 5645
 
3.3%
광 4876
 
2.8%
산 4155
 
2.4%
역 4087
 
2.4%
번 3911
 
2.3%
Other values (525) 107116
62.1%
Latin
ValueCountFrequency (%)
S 96
 
12.8%
K 66
 
8.8%
e 46
 
6.1%
I 45
 
6.0%
U 42
 
5.6%
H 39
 
5.2%
A 30
 
4.0%
B 26
 
3.5%
o 24
 
3.2%
w 22
 
2.9%
Other values (31) 313
41.8%
Common
ValueCountFrequency (%)
35500
41.2%
1 7699
 
8.9%
) 6843
 
7.9%
( 6843
 
7.9%
2 5368
 
6.2%
3 3796
 
4.4%
5 3217
 
3.7%
4 3050
 
3.5%
6 2744
 
3.2%
7 2410
 
2.8%
Other values (6) 8787
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 172611
66.5%
ASCII 86992
33.5%
None 10
 
< 0.1%
Number Forms 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
35500
40.8%
1 7699
 
8.9%
) 6843
 
7.9%
( 6843
 
7.9%
2 5368
 
6.2%
3 3796
 
4.4%
5 3217
 
3.7%
4 3050
 
3.5%
6 2744
 
3.2%
7 2410
 
2.8%
Other values (43) 9522
 
10.9%
Hangul
ValueCountFrequency (%)
시 10533
 
6.1%
로 8949
 
5.2%
구 8392
 
4.9%
동 8112
 
4.7%
길 6835
 
4.0%
도 5645
 
3.3%
광 4876
 
2.8%
산 4155
 
2.4%
역 4087
 
2.4%
번 3911
 
2.3%
Other values (525) 107116
62.1%
None
ValueCountFrequency (%)
· 10
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 2
50.0%
Ⅰ 1
25.0%
Ⅲ 1
25.0%

공급유형
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
기존주택매입임대
9403 
청년신혼부부매입임대
 
535
집주인건설개량
 
26
국민희망임대리츠
 
18
집주인매입
 
18

Length

Max length10
Median length8
Mean length8.099
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기존주택매입임대 9403
94.0%
청년신혼부부매입임대 535
 
5.3%
집주인건설개량 26
 
0.3%
국민희망임대리츠 18
 
0.2%
집주인매입 18
 
0.2%

Length

2023-12-13T06:46:36.448980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:46:36.582504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기존주택매입임대 9403
94.0%
청년신혼부부매입임대 535
 
5.3%
집주인건설개량 26
 
0.3%
국민희망임대리츠 18
 
0.2%
집주인매입 18
 
0.2%

공급면적
Real number (ℝ)

HIGH CORRELATION 

Distinct6892
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean311.24
Minimum15
Maximum5388.076
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:46:36.734700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile50.0152
Q169.9959
median285.5
Q3479.415
95-th percentile596.0305
Maximum5388.076
Range5373.076
Interquartile range (IQR)409.4191

Descriptive statistics

Standard deviation305.64647
Coefficient of variation (CV)0.98202822
Kurtosis60.309081
Mean311.24
Median Absolute Deviation (MAD)210.71
Skewness5.2768566
Sum3112400
Variance93419.763
MonotonicityNot monotonic
2023-12-13T06:46:36.912772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.9605 62
 
0.6%
59.1416 48
 
0.5%
59.98 39
 
0.4%
59.9016 34
 
0.3%
50.0152 34
 
0.3%
59.735 30
 
0.3%
59.97 28
 
0.3%
59.8009 28
 
0.3%
59.8947 28
 
0.3%
59.986 26
 
0.3%
Other values (6882) 9643
96.4%
ValueCountFrequency (%)
15.0 1
 
< 0.1%
16.3516 1
 
< 0.1%
17.1621 1
 
< 0.1%
17.6 4
< 0.1%
17.8154 1
 
< 0.1%
18.2925 1
 
< 0.1%
18.75 2
< 0.1%
18.943 1
 
< 0.1%
18.961 1
 
< 0.1%
19.35 1
 
< 0.1%
ValueCountFrequency (%)
5388.076 1
< 0.1%
5333.5232 1
< 0.1%
5135.08 1
< 0.1%
4955.794 1
< 0.1%
4586.16 1
< 0.1%
4361.4 1
< 0.1%
4213.6254 1
< 0.1%
4170.45 1
< 0.1%
4144.32 1
< 0.1%
4033.6438 1
< 0.1%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7102
Minimum1
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:46:37.108491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median6
Q310
95-th percentile16
Maximum200
Range199
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.4911433
Coefficient of variation (CV)1.1163815
Kurtosis124.31302
Mean6.7102
Median Absolute Deviation (MAD)5
Skewness7.3347939
Sum67102
Variance56.117228
MonotonicityNot monotonic
2023-12-13T06:46:37.271068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3519
35.2%
8 1184
 
11.8%
12 600
 
6.0%
5 572
 
5.7%
10 558
 
5.6%
11 508
 
5.1%
6 488
 
4.9%
9 465
 
4.7%
7 445
 
4.5%
4 284
 
2.8%
Other values (62) 1377
 
13.8%
ValueCountFrequency (%)
1 3519
35.2%
2 80
 
0.8%
3 259
 
2.6%
4 284
 
2.8%
5 572
 
5.7%
6 488
 
4.9%
7 445
 
4.5%
8 1184
 
11.8%
9 465
 
4.7%
10 558
 
5.6%
ValueCountFrequency (%)
200 1
< 0.1%
180 1
< 0.1%
163 1
< 0.1%
128 1
< 0.1%
116 1
< 0.1%
101 1
< 0.1%
99 1
< 0.1%
95 1
< 0.1%
91 1
< 0.1%
89 1
< 0.1%

건축사용승인일자
Date

MISSING 

Distinct3926
Distinct (%)40.9%
Missing404
Missing (%)4.0%
Memory size156.2 KiB
Minimum1965-03-05 00:00:00
Maximum2020-03-31 00:00:00
2023-12-13T06:46:37.434390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:37.577792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

제품대체일자
Date

MISSING 

Distinct1699
Distinct (%)21.1%
Missing1961
Missing (%)19.6%
Memory size156.2 KiB
Minimum2004-12-31 00:00:00
Maximum2020-05-31 00:00:00
2023-12-13T06:46:37.703455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:37.854626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

매입년도
Real number (ℝ)

Distinct17
Distinct (%)0.2%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2014.743
Minimum2004
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:46:37.963141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2004
5-th percentile2006
Q12010
median2017
Q32019
95-th percentile2019
Maximum2020
Range16
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.7629567
Coefficient of variation (CV)0.0023640517
Kurtosis-0.9860249
Mean2014.743
Median Absolute Deviation (MAD)2
Skewness-0.72989131
Sum20141386
Variance22.685757
MonotonicityNot monotonic
2023-12-13T06:46:38.073038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2019 3287
32.9%
2018 1151
 
11.5%
2017 718
 
7.2%
2008 583
 
5.8%
2015 479
 
4.8%
2007 477
 
4.8%
2009 468
 
4.7%
2016 459
 
4.6%
2010 396
 
4.0%
2006 366
 
3.7%
Other values (7) 1613
16.1%
ValueCountFrequency (%)
2004 36
 
0.4%
2005 299
3.0%
2006 366
3.7%
2007 477
4.8%
2008 583
5.8%
2009 468
4.7%
2010 396
4.0%
2011 232
 
2.3%
2012 204
 
2.0%
2013 326
3.3%
ValueCountFrequency (%)
2020 168
 
1.7%
2019 3287
32.9%
2018 1151
 
11.5%
2017 718
 
7.2%
2016 459
 
4.6%
2015 479
 
4.8%
2014 348
 
3.5%
2013 326
 
3.3%
2012 204
 
2.0%
2011 232
 
2.3%

Interactions

2023-12-13T06:46:33.449399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:31.656789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:32.476170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:32.922347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:33.579426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:31.775016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:32.575609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:33.030943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:33.687378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:31.889060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:32.680743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:33.151424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:33.819212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:32.066754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:32.809462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:33.300461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:46:38.160394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번공급유형공급면적세대수매입년도
순번1.0000.7210.1700.0500.520
공급유형0.7211.0000.1050.0000.541
공급면적0.1700.1051.0000.5640.241
세대수0.0500.0000.5641.0000.058
매입년도0.5200.5410.2410.0581.000
2023-12-13T06:46:38.258008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번공급면적세대수매입년도공급유형
순번1.000-0.083-0.1110.2220.381
공급면적-0.0831.0000.806-0.3470.044
세대수-0.1110.8061.000-0.4450.000
매입년도0.222-0.347-0.4451.0000.254
공급유형0.3810.0440.0000.2541.000

Missing values

2023-12-13T06:46:34.005783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:46:34.179708image/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.
2023-12-13T06:46:34.317318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

순번단지명주소공급유형공급면적세대수건축사용승인일자제품대체일자매입년도
7576매입다가구(서울광진구)서울특별시 광진구 동일로66길기존주택매입임대308.2172008-08-262009-06-132009
66836684매입다가구(인천부평구)인천광역시 부평구 평천로 368(갈산동)기존주택매입임대718.34102019-07-122020-02-262019
1141211414매입다가구(경기남양주시)경기도 남양주시 의안로 155(평내동 평내마을신명스카이뷰아파트)기존주택매입임대84.927212004-09-182019-06-212019
15261527매입다가구(서울금천구)서울특별시 금천구 시흥대로94길 39-7(독산동)기존주택매입임대346.3282019-08-262019-12-132019
22602261매입다가구(부산동구)부산광역시 동구 망양로 850(좌천동 좌천 서린 엘마르 더뷰)기존주택매입임대59.894712018-08-032019-12-062019
30973098매입다가구(부산북구)부산광역시 북구 만덕3로57번길기존주택매입임대189.7231991-06-252008-12-302008
1391913921매입다가구(충북청주시)충청북도 청주시 청원구 향군로 110(우암동 라임미소가)기존주택매입임대48.710112017-11-142019-11-042019
1839018392청년신혼부부매입임대(전북전주시)전라북도 전주시 덕진구 반월로 32(반월동 월드컵이지움)청년신혼부부매입임대59.879612015-03-19<NA>2018
581582매입다가구(서울도봉구)서울특별시 도봉구 도봉로 415(쌍문동)기존주택매입임대46.8412019-07-10<NA>2019
66186619매입다가구(인천부평구)인천광역시 부평구 백범로506번길 22-16(십정동)기존주택매입임대518.0282018-07-272018-10-222018
순번단지명주소공급유형공급면적세대수건축사용승인일자제품대체일자매입년도
76487649매입다가구(광주북구)광주광역시 북구 하서로 71(운암동 유탑하늘세움아파트)기존주택매입임대84.995212005-03-08<NA>2019
1810018102청년신혼부부매입임대(경기남양주시)경기도 남양주시 화도읍 맷돌로 50(보미청광플러스원아파트)청년신혼부부매입임대59.97912005-12-15<NA>2018
37033704매입다가구(부산금정구)부산광역시 금정구 팔송로59번길기존주택매입임대171.9331991-10-302008-06-272008
1307213074매입다가구(강원춘천시)강원도 춘천시 승지골길16번길 47(퇴계동 뜨란채아파트)기존주택매입임대318.112006-04-25<NA>2019
30253026매입다가구(부산북구)부산광역시 북구 덕천로276번길 85(만덕동 백양산동문굿모닝힐)기존주택매입임대70.184712015-10-21<NA>2019
1657816580매입다가구(경남창원시)경상남도 창원시 진해구 행암로 25(장천동 진해 장천 대동 다숲아파트)기존주택매입임대155.3412007-12-272019-12-172019
1328013282매입다가구(강원원주시)강원도 원주시 우산공단길 23(우산동 신일유토빌)기존주택매입임대84.7912012-02-08<NA>2018
1313613138매입다가구(강원원주시)강원도 원주시 개운2길 20(개운동)기존주택매입임대488.3511<NA>2014-05-222014
1308813090매입다가구(강원춘천시)강원도 춘천시 옥산포길 40-6(사농동)기존주택매입임대570.5192019-04-04<NA>2019
1567715679매입다가구(경북포항시)경상북도 포항시 북구 법원로112번길기존주택매입임대569.1112007-06-292007-09-102007