Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells368
Missing cells (%)0.3%
Duplicate rows4
Duplicate rows (%)< 0.1%
Total size in memory1.1 MiB
Average record size in memory111.0 B

Variable types

Categorical5
Numeric6
DateTime1

Dataset

Description한국토지주택공사가 전세임대로 제공하는 호수에 대한 공급현황 정보(지역본부, 지자체, 유형, 주택유형, 방갯수, 계약면적, 전용면적, 세대원수, 계약일자, 전세금, 전세지원금, 월임대료)를 제공합니다.
Author한국토지주택공사
URLhttps://www.data.go.kr/data/15113587/fileData.do

Alerts

Dataset has 4 (< 0.1%) duplicate rowsDuplicates
지역본부 is highly overall correlated with 지자체High correlation
지자체 is highly overall correlated with 지역본부High correlation
계약면적 is highly overall correlated with 전용면적 and 1 other fieldsHigh correlation
전용면적 is highly overall correlated with 계약면적 and 1 other fieldsHigh correlation
전세금 is highly overall correlated with 전세지원금 and 1 other fieldsHigh correlation
전세지원금 is highly overall correlated with 전세금 and 1 other fieldsHigh correlation
월임대료 is highly overall correlated with 전세금 and 1 other fieldsHigh correlation
방갯수 is highly overall correlated with 계약면적 and 1 other fieldsHigh correlation
계약일자 has 367 (3.7%) missing valuesMissing
세대원수 has 4814 (48.1%) zerosZeros

Reproduction

Analysis started2024-04-29 23:09:19.526582
Analysis finished2024-04-29 23:09:26.934064
Duration7.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역본부
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
서울지역본부
8082 
부산울산지역본부
1918 

Length

Max length8
Median length6
Mean length6.3836
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울지역본부
2nd row서울지역본부
3rd row부산울산지역본부
4th row서울지역본부
5th row서울지역본부

Common Values

ValueCountFrequency (%)
서울지역본부 8082
80.8%
부산울산지역본부 1918
 
19.2%

Length

2024-04-30T08:09:27.027794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T08:09:27.139962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울지역본부 8082
80.8%
부산울산지역본부 1918
 
19.2%

지자체
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
전세(서울관악구)
731 
전세(서울강서구)
 
657
전세(서울은평구)
 
548
전세(서울중랑구)
 
490
전세(서울성북구)
 
453
Other values (33)
7121 

Length

Max length10
Median length9
Mean length9.0648
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전세(서울용산구)
2nd row전세(서울송파구)
3rd row전세(부산사하구)
4th row전세(서울노원구)
5th row전세(서울관악구)

Common Values

ValueCountFrequency (%)
전세(서울관악구) 731
 
7.3%
전세(서울강서구) 657
 
6.6%
전세(서울은평구) 548
 
5.5%
전세(서울중랑구) 490
 
4.9%
전세(서울성북구) 453
 
4.5%
전세(서울강북구) 446
 
4.5%
전세(서울동대문구) 402
 
4.0%
전세(서울양천구) 368
 
3.7%
전세(서울광진구) 368
 
3.7%
전세(서울노원구) 348
 
3.5%
Other values (28) 5189
51.9%

Length

2024-04-30T08:09:27.275529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전세(서울관악구 731
 
7.3%
전세(서울강서구 657
 
6.6%
전세(서울은평구 548
 
5.5%
전세(서울중랑구 490
 
4.9%
전세(서울성북구 453
 
4.5%
전세(서울강북구 446
 
4.5%
전세(서울동대문구 402
 
4.0%
전세(서울양천구 368
 
3.7%
전세(서울광진구 368
 
3.7%
전세(서울노원구 348
 
3.5%
Other values (28) 5189
51.9%

유형
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
기존주택
5386 
청년(19~39세)
2263 
신혼부부
942 
청년(대학생)
 
527
신혼부부Ⅱ
 
252
Other values (4)
630 

Length

Max length10
Median length4
Mean length5.7213
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기존주택
2nd row기존주택
3rd row기존주택
4th row신혼부부Ⅱ
5th row소년소녀가정

Common Values

ValueCountFrequency (%)
기존주택 5386
53.9%
청년(19~39세) 2263
22.6%
신혼부부 942
 
9.4%
청년(대학생) 527
 
5.3%
신혼부부Ⅱ 252
 
2.5%
소년소녀가정 234
 
2.3%
청년(취업준비생) 230
 
2.3%
다자녀가구 160
 
1.6%
청년(기숙사형) 6
 
0.1%

Length

2024-04-30T08:09:27.405982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T08:09:27.516619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기존주택 5386
53.9%
청년(19~39세 2263
22.6%
신혼부부 942
 
9.4%
청년(대학생 527
 
5.3%
신혼부부ⅱ 252
 
2.5%
소년소녀가정 234
 
2.3%
청년(취업준비생 230
 
2.3%
다자녀가구 160
 
1.6%
청년(기숙사형 6
 
0.1%

주택유형
Categorical

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
다세대주택
3152 
다가구용단독주택
2161 
단독주택
1389 
아파트
975 
점포주택등복합용도주택
734 
Other values (6)
1589 

Length

Max length11
Median length10
Mean length5.7583
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
다세대주택 3152
31.5%
다가구용단독주택 2161
21.6%
단독주택 1389
13.9%
아파트 975
 
9.8%
점포주택등복합용도주택 734
 
7.3%
오피스텔 607
 
6.1%
도시형생활주택 532
 
5.3%
연립주택 332
 
3.3%
다중주택 112
 
1.1%
기숙사및특수사회시설 5
 
0.1%

Length

2024-04-30T08:09:27.660442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
다세대주택 3152
31.5%
다가구용단독주택 2161
21.6%
단독주택 1389
13.9%
아파트 975
 
9.8%
점포주택등복합용도주택 734
 
7.3%
오피스텔 607
 
6.1%
도시형생활주택 532
 
5.3%
연립주택 332
 
3.3%
다중주택 112
 
1.1%
기숙사및특수사회시설 5
 
< 0.1%

방갯수
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
4521 
1
2759 
3
2673 
<NA>
 
40
4
 
7

Length

Max length4
Median length1
Mean length1.012
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row<NA>
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 4521
45.2%
1 2759
27.6%
3 2673
26.7%
<NA> 40
 
0.4%
4 7
 
0.1%

Length

2024-04-30T08:09:27.779660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T08:09:27.892527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 4521
45.2%
1 2759
27.6%
3 2673
26.7%
na 40
 
0.4%
4 7
 
0.1%

계약면적
Real number (ℝ)

HIGH CORRELATION 

Distinct4845
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.939641
Minimum9.37
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T08:09:28.002925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.37
5-th percentile15.6
Q128.7475
median40
Q351.34
95-th percentile72.4705
Maximum159
Range149.63
Interquartile range (IQR)22.5925

Descriptive statistics

Standard deviation17.00516
Coefficient of variation (CV)0.41537149
Kurtosis0.59145702
Mean40.939641
Median Absolute Deviation (MAD)11.3
Skewness0.56041361
Sum409396.41
Variance289.17547
MonotonicityNot monotonic
2024-04-30T08:09:28.137104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.0 65
 
0.7%
30.0 58
 
0.6%
33.0 48
 
0.5%
20.0 38
 
0.4%
18.0 35
 
0.4%
50.0 34
 
0.3%
35.0 32
 
0.3%
15.0 32
 
0.3%
49.08 30
 
0.3%
25.0 29
 
0.3%
Other values (4835) 9599
96.0%
ValueCountFrequency (%)
9.37 1
 
< 0.1%
10.0 3
< 0.1%
10.37 1
 
< 0.1%
10.51 1
 
< 0.1%
10.6 1
 
< 0.1%
10.85 1
 
< 0.1%
10.93 1
 
< 0.1%
11.0 2
< 0.1%
11.03 1
 
< 0.1%
11.12 1
 
< 0.1%
ValueCountFrequency (%)
159.0 1
< 0.1%
153.03 1
< 0.1%
142.08 1
< 0.1%
134.96 1
< 0.1%
131.27 2
< 0.1%
116.06 1
< 0.1%
113.31 1
< 0.1%
107.947 1
< 0.1%
107.83 1
< 0.1%
107.56 1
< 0.1%

전용면적
Real number (ℝ)

HIGH CORRELATION 

Distinct4851
Distinct (%)48.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.927159
Minimum9.37
Maximum153.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T08:09:28.257885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.37
5-th percentile15.6
Q128.7475
median40
Q351.315
95-th percentile72.4505
Maximum153.03
Range143.66
Interquartile range (IQR)22.5675

Descriptive statistics

Standard deviation16.961328
Coefficient of variation (CV)0.4144272
Kurtosis0.3940755
Mean40.927159
Median Absolute Deviation (MAD)11.3
Skewness0.53312299
Sum409271.59
Variance287.68665
MonotonicityNot monotonic
2024-04-30T08:09:28.383903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.0 65
 
0.7%
30.0 58
 
0.6%
33.0 48
 
0.5%
20.0 38
 
0.4%
18.0 35
 
0.4%
50.0 34
 
0.3%
15.0 32
 
0.3%
35.0 32
 
0.3%
49.08 30
 
0.3%
25.0 29
 
0.3%
Other values (4841) 9599
96.0%
ValueCountFrequency (%)
9.37 1
 
< 0.1%
10.0 3
< 0.1%
10.37 1
 
< 0.1%
10.51 1
 
< 0.1%
10.6 1
 
< 0.1%
10.85 1
 
< 0.1%
10.93 1
 
< 0.1%
11.0 2
< 0.1%
11.03 1
 
< 0.1%
11.12 1
 
< 0.1%
ValueCountFrequency (%)
153.03 1
< 0.1%
142.08 1
< 0.1%
134.96 1
< 0.1%
131.27 2
< 0.1%
116.06 1
< 0.1%
113.31 1
< 0.1%
107.947 1
< 0.1%
107.83 1
< 0.1%
107.56 1
< 0.1%
102.78 1
< 0.1%

세대원수
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.93859386
Minimum0
Maximum8
Zeros4814
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T08:09:28.499118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1184274
Coefficient of variation (CV)1.1915989
Kurtosis1.1019906
Mean0.93859386
Median Absolute Deviation (MAD)1
Skewness1.1417002
Sum9385
Variance1.2508798
MonotonicityNot monotonic
2024-04-30T08:09:28.602384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 4814
48.1%
1 2283
22.8%
2 1960
19.6%
3 671
 
6.7%
4 204
 
2.0%
5 55
 
0.5%
6 8
 
0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
0 4814
48.1%
1 2283
22.8%
2 1960
19.6%
3 671
 
6.7%
4 204
 
2.0%
5 55
 
0.5%
6 8
 
0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 2
 
< 0.1%
6 8
 
0.1%
5 55
 
0.5%
4 204
 
2.0%
3 671
 
6.7%
2 1960
19.6%
1 2283
22.8%
0 4814
48.1%

계약일자
Date

MISSING 

Distinct1363
Distinct (%)14.1%
Missing367
Missing (%)3.7%
Memory size156.2 KiB
Minimum2016-07-04 00:00:00
Maximum2024-12-23 00:00:00
2024-04-30T08:09:28.899253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:29.025902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

전세금
Real number (ℝ)

HIGH CORRELATION 

Distinct382
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0197992 × 108
Minimum5000000
Maximum5.8 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T08:09:29.190553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5000000
5-th percentile50000000
Q180000000
median1 × 108
Q31.2 × 108
95-th percentile1.6 × 108
Maximum5.8 × 108
Range5.75 × 108
Interquartile range (IQR)40000000

Descriptive statistics

Standard deviation38301008
Coefficient of variation (CV)0.37557403
Kurtosis8.5808384
Mean1.0197992 × 108
Median Absolute Deviation (MAD)20000000
Skewness1.5990219
Sum1.0197992 × 1012
Variance1.4669672 × 1015
MonotonicityNot monotonic
2024-04-30T08:09:29.357587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120000000 1614
16.1%
90000000 916
 
9.2%
100000000 860
 
8.6%
80000000 785
 
7.8%
70000000 550
 
5.5%
130000000 548
 
5.5%
110000000 520
 
5.2%
60000000 411
 
4.1%
50000000 267
 
2.7%
85000000 241
 
2.4%
Other values (372) 3288
32.9%
ValueCountFrequency (%)
5000000 5
 
0.1%
10000000 18
0.2%
12000000 1
 
< 0.1%
15000000 3
 
< 0.1%
17000000 1
 
< 0.1%
20000000 22
0.2%
22000000 1
 
< 0.1%
23000000 3
 
< 0.1%
24000000 1
 
< 0.1%
25000000 11
0.1%
ValueCountFrequency (%)
580000000 1
 
< 0.1%
520000000 1
 
< 0.1%
400000000 1
 
< 0.1%
370000000 1
 
< 0.1%
360000000 2
< 0.1%
350000000 3
< 0.1%
340000000 1
 
< 0.1%
330000000 3
< 0.1%
320000000 2
< 0.1%
315000000 1
 
< 0.1%

전세지원금
Real number (ℝ)

HIGH CORRELATION 

Distinct1435
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92583549
Minimum3060000
Maximum1.954 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T08:09:29.519540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3060000
5-th percentile46300000
Q174500000
median90000000
Q31.177 × 108
95-th percentile1.2905 × 108
Maximum1.954 × 108
Range1.9234 × 108
Interquartile range (IQR)43200000

Descriptive statistics

Standard deviation28652776
Coefficient of variation (CV)0.30948021
Kurtosis0.73668515
Mean92583549
Median Absolute Deviation (MAD)23500000
Skewness0.13986384
Sum9.2583549 × 1011
Variance8.2098158 × 1014
MonotonicityNot monotonic
2024-04-30T08:09:29.733709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119000000 531
 
5.3%
85500000 474
 
4.7%
118000000 394
 
3.9%
114000000 308
 
3.1%
76000000 279
 
2.8%
66500000 250
 
2.5%
80750000 192
 
1.9%
119500000 159
 
1.6%
57000000 158
 
1.6%
89500000 144
 
1.4%
Other values (1425) 7111
71.1%
ValueCountFrequency (%)
3060000 1
 
< 0.1%
3900000 1
 
< 0.1%
4150000 2
 
< 0.1%
4390000 1
 
< 0.1%
4600000 1
 
< 0.1%
5000000 1
 
< 0.1%
6800000 1
 
< 0.1%
8150000 3
< 0.1%
8420000 1
 
< 0.1%
8600000 5
0.1%
ValueCountFrequency (%)
195400000 1
 
< 0.1%
192000000 82
0.8%
191700000 2
 
< 0.1%
191520000 1
 
< 0.1%
191500000 1
 
< 0.1%
191400000 1
 
< 0.1%
191200000 3
 
< 0.1%
191100000 1
 
< 0.1%
190880000 1
 
< 0.1%
190500000 1
 
< 0.1%

월임대료
Real number (ℝ)

HIGH CORRELATION 

Distinct2190
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132857.86
Minimum0
Maximum515970
Zeros83
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T08:09:29.932621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39618
Q1104902.5
median134580
Q3163330
95-th percentile205830
Maximum515970
Range515970
Interquartile range (IQR)58427.5

Descriptive statistics

Standard deviation52205.037
Coefficient of variation (CV)0.392939
Kurtosis1.3924386
Mean132857.86
Median Absolute Deviation (MAD)29220
Skewness0.16135022
Sum1.3285786 × 109
Variance2.7253659 × 109
MonotonicityNot monotonic
2024-04-30T08:09:30.146407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148750 432
 
4.3%
142500 250
 
2.5%
128250 240
 
2.4%
114000 208
 
2.1%
147500 205
 
2.1%
99750 184
 
1.8%
196660 176
 
1.8%
171000 148
 
1.5%
121120 121
 
1.2%
179250 110
 
1.1%
Other values (2180) 7926
79.3%
ValueCountFrequency (%)
0 83
0.8%
2550 1
 
< 0.1%
2830 1
 
< 0.1%
3250 1
 
< 0.1%
3450 2
 
< 0.1%
3650 1
 
< 0.1%
3830 1
 
< 0.1%
4160 1
 
< 0.1%
6790 3
 
< 0.1%
7010 1
 
< 0.1%
ValueCountFrequency (%)
515970 1
 
< 0.1%
489600 1
 
< 0.1%
370490 1
 
< 0.1%
351250 1
 
< 0.1%
342000 3
 
< 0.1%
328500 1
 
< 0.1%
326700 1
 
< 0.1%
320000 30
0.3%
319500 1
 
< 0.1%
319200 1
 
< 0.1%

Interactions

2024-04-30T08:09:25.903614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:22.238774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:22.995021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:23.779397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:24.445800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:25.048486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:26.004561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:22.391583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:23.139036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:23.876032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:24.541803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:25.168164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:26.104908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:22.486599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:23.282939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:23.990664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:24.643856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:25.285889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:26.207009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:22.585022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:23.431069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:24.119907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:24.740778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:25.395181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:26.302909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:22.714164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:23.565772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:24.222414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:24.843237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:25.497774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:26.431772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:22.867267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:23.686218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:24.332609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:24.956248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:09:25.607033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T08:09:30.281534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역본부지자체유형주택유형방갯수계약면적전용면적세대원수전세금전세지원금월임대료
지역본부1.0001.0000.1630.4150.1640.3950.3800.0760.3970.6120.462
지자체1.0001.0000.3390.5600.3430.3950.3810.1490.3820.4410.438
유형0.1630.3391.0000.5780.4670.4310.4310.5920.6570.5880.646
주택유형0.4150.5600.5781.0000.4740.4460.4420.1610.1600.1930.137
방갯수0.1640.3430.4670.4741.0000.8630.8770.4130.1770.1880.148
계약면적0.3950.3950.4310.4460.8631.0000.9920.6180.1550.2370.128
전용면적0.3800.3810.4310.4420.8770.9921.0000.5700.1580.2320.129
세대원수0.0760.1490.5920.1610.4130.6180.5701.0000.2160.1900.181
전세금0.3970.3820.6570.1600.1770.1550.1580.2161.0000.7900.879
전세지원금0.6120.4410.5880.1930.1880.2370.2320.1900.7901.0000.788
월임대료0.4620.4380.6460.1370.1480.1280.1290.1810.8790.7881.000
2024-04-30T08:09:30.421358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역본부유형지자체주택유형방갯수
지역본부1.0000.1620.9980.3980.109
유형0.1621.0000.1300.3070.317
지자체0.9980.1301.0000.2210.180
주택유형0.3980.3070.2211.0000.306
방갯수0.1090.3170.1800.3061.000
2024-04-30T08:09:30.523387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계약면적전용면적세대원수전세금전세지원금월임대료지역본부지자체유형주택유형방갯수
계약면적1.0001.0000.3060.1150.0220.0760.3030.1480.2120.2070.719
전용면적1.0001.0000.3070.1150.0220.0760.2910.1420.2120.2050.741
세대원수0.3060.3071.0000.1690.1050.0980.0760.0550.2250.0730.276
전세금0.1150.1150.1691.0000.9300.8380.3980.1500.2630.0720.114
전세지원금0.0220.0220.1050.9301.0000.8670.4740.1690.3150.0830.113
월임대료0.0760.0760.0980.8380.8671.0000.4630.1760.2560.0620.095
지역본부0.3030.2910.0760.3980.4740.4631.0000.9980.1620.3980.109
지자체0.1480.1420.0550.1500.1690.1760.9981.0000.1300.2210.180
유형0.2120.2120.2250.2630.3150.2560.1620.1301.0000.3070.317
주택유형0.2070.2050.0730.0720.0830.0620.3980.2210.3071.0000.306
방갯수0.7190.7410.2760.1140.1130.0950.1090.1800.3170.3061.000

Missing values

2024-04-30T08:09:26.570672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T08:09:26.743955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-04-30T08:09:26.866278image/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

지역본부지자체유형주택유형방갯수계약면적전용면적세대원수계약일자전세금전세지원금월임대료
63694서울지역본부전세(서울용산구)기존주택다세대주택252.3252.3212023-12-09150000000123500000185250
54900서울지역본부전세(서울송파구)기존주택다세대주택229.9729.9712022-08-088500000079550000159090
88349부산울산지역본부전세(부산사하구)기존주택다세대주택<NA>62.4662.4622020-09-01750000006650000099750
31971서울지역본부전세(서울노원구)신혼부부Ⅱ아파트259.2659.2612019-09-19250000000100000000166660
16971서울지역본부전세(서울관악구)소년소녀가정다세대주택229.7329.7302023-08-0690000000900000000
91739부산울산지역본부전세(부산수영구)기존주택다세대주택371.4171.4132024-01-04650000006100000076250
54791서울지역본부전세(서울송파구)기존주택다세대주택241.3841.3802023-10-18130000000128300000213830
33319서울지역본부전세(서울도봉구)기존주택다세대주택140.0340.0302023-07-119000000085500000128250
75210서울지역본부전세(서울중랑구)청년(19~39세)다세대주택348.8548.8522022-02-04180000000119000000148750
2503서울지역본부전세(서울강동구)기존주택단독주택236.4636.4602022-04-13110000000109000000163500
지역본부지자체유형주택유형방갯수계약면적전용면적세대원수계약일자전세금전세지원금월임대료
80639부산울산지역본부전세(부산동구)기존주택단독주택238.038.002023-01-05350000003325000027700
21247서울지역본부전세(서울광진구)기존주택다가구용단독주택231.4331.4302023-08-307500000071250000106870
88978부산울산지역본부전세(부산사하구)기존주택아파트252.1152.1102022-05-09700000006620000099300
30626서울지역본부전세(서울노원구)기존주택아파트132.3932.3902023-07-11115000000114425000190700
69127서울지역본부전세(서울은평구)청년(19~39세)다세대주택245.6245.6222024-02-23150000000118000000147500
84300부산울산지역본부전세(부산부산진구)소년소녀가정오피스텔123.1923.1902023-02-1592000000800000000
24861서울지역본부전세(서울구로구)기존주택다세대주택354.0654.0622023-08-23110000000104050000156070
14530서울지역본부전세(서울관악구)기존주택다가구용단독주택239.3539.3502022-07-047000000068900000103350
21665서울지역본부전세(서울광진구)기존주택단독주택121.2421.2412023-08-1110000000094400000157330
80328부산울산지역본부전세(부산남구)청년(19~39세)오피스텔128.2828.2822023-10-139500000093790000117230

Duplicate rows

Most frequently occurring

지역본부지자체유형주택유형방갯수계약면적전용면적세대원수계약일자전세금전세지원금월임대료# duplicates
0서울지역본부전세(서울도봉구)청년(19~39세)다세대주택356.0656.0602023-01-317000000069000000862502
1서울지역본부전세(서울서대문구)청년(19~39세)다세대주택349.0249.0202024-03-216400000063000000787502
2서울지역본부전세(서울성동구)청년(기숙사형)기숙사및특수사회시설113.213.202023-01-103000000029000000193302
3서울지역본부전세(서울영등포구)청년(19~39세)도시형생활주택112.6212.6202024-02-201200000001184000001480002