Overview

Dataset statistics

Number of variables10
Number of observations1372
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory118.0 KiB
Average record size in memory88.1 B

Variable types

Categorical3
Numeric7

Dataset

Description대전도시공사 순환형임대아파트는 저소득층 주거공급 및 취약계층 주거복지를 위해 제공되는 순환형임대아파트 임대현황자료 입니다.
URLhttps://www.data.go.kr/data/15103610/fileData.do

Alerts

순번 is highly overall correlated with 임대대상명 and 1 other fieldsHigh correlation
임대면적 is highly overall correlated with 전용면적 and 2 other fieldsHigh correlation
전용면적 is highly overall correlated with 임대면적 and 2 other fieldsHigh correlation
공용면적 is highly overall correlated with 임대면적 and 2 other fieldsHigh correlation
보증금 is highly overall correlated with 월임대료 and 1 other fieldsHigh correlation
월임대료 is highly overall correlated with 보증금 and 1 other fieldsHigh correlation
임대대상명 is highly overall correlated with 순번 and 4 other fieldsHigh correlation
동 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
임대구분 is highly overall correlated with 보증금 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 16:50:13.131173
Analysis finished2023-12-12 16:50:21.130660
Duration8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

임대대상명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
인동(누리보듬)
976 
성남동(누리보듬)
396 

Length

Max length9
Median length8
Mean length8.2886297
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row성남동(누리보듬)
2nd row성남동(누리보듬)
3rd row성남동(누리보듬)
4th row성남동(누리보듬)
5th row성남동(누리보듬)

Common Values

ValueCountFrequency (%)
인동(누리보듬) 976
71.1%
성남동(누리보듬) 396
28.9%

Length

2023-12-13T01:50:21.228996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:50:21.355276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인동(누리보듬 976
71.1%
성남동(누리보듬 396
28.9%

순번
Real number (ℝ)

HIGH CORRELATION 

Distinct976
Distinct (%)71.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean404.79738
Minimum1
Maximum976
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-13T01:50:21.507901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q1172
median343.5
Q3633.25
95-th percentile907.45
Maximum976
Range975
Interquartile range (IQR)461.25

Descriptive statistics

Standard deviation278.50568
Coefficient of variation (CV)0.68801257
Kurtosis-0.99312234
Mean404.79738
Median Absolute Deviation (MAD)211
Skewness0.45133777
Sum555382
Variance77565.416
MonotonicityNot monotonic
2023-12-13T01:50:21.721292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2
 
0.1%
274 2
 
0.1%
272 2
 
0.1%
271 2
 
0.1%
270 2
 
0.1%
269 2
 
0.1%
268 2
 
0.1%
267 2
 
0.1%
266 2
 
0.1%
265 2
 
0.1%
Other values (966) 1352
98.5%
ValueCountFrequency (%)
1 2
0.1%
2 2
0.1%
3 2
0.1%
4 2
0.1%
5 2
0.1%
6 2
0.1%
7 2
0.1%
8 2
0.1%
9 2
0.1%
10 2
0.1%
ValueCountFrequency (%)
976 1
0.1%
975 1
0.1%
974 1
0.1%
973 1
0.1%
972 1
0.1%
971 1
0.1%
970 1
0.1%
969 1
0.1%
968 1
0.1%
967 1
0.1%

동
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
101
764 
104
368 
102
120 
103
120 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row101
2nd row101
3rd row101
4th row101
5th row101

Common Values

ValueCountFrequency (%)
101 764
55.7%
104 368
26.8%
102 120
 
8.7%
103 120
 
8.7%

Length

2023-12-13T01:50:21.914852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:50:22.065935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
101 764
55.7%
104 368
26.8%
102 120
 
8.7%
103 120
 
8.7%

호
Real number (ℝ)

Distinct119
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.8105
Minimum201
Maximum906
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-13T01:50:22.232626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201
5-th percentile206
Q1312
median502
Q3606
95-th percentile712
Maximum906
Range705
Interquartile range (IQR)294

Descriptive statistics

Standard deviation170.37365
Coefficient of variation (CV)0.36034237
Kurtosis-0.69828876
Mean472.8105
Median Absolute Deviation (MAD)109
Skewness0.22648733
Sum648696
Variance29027.182
MonotonicityNot monotonic
2023-12-13T01:50:22.449162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
604 20
 
1.5%
405 20
 
1.5%
504 20
 
1.5%
603 20
 
1.5%
401 20
 
1.5%
402 20
 
1.5%
403 20
 
1.5%
404 20
 
1.5%
406 20
 
1.5%
606 20
 
1.5%
Other values (109) 1172
85.4%
ValueCountFrequency (%)
201 12
0.9%
202 12
0.9%
203 12
0.9%
204 12
0.9%
205 12
0.9%
206 12
0.9%
207 8
0.6%
208 8
0.6%
209 8
0.6%
210 8
0.6%
ValueCountFrequency (%)
906 4
0.3%
905 4
0.3%
904 4
0.3%
903 4
0.3%
902 4
0.3%
901 4
0.3%
806 4
0.3%
805 4
0.3%
804 4
0.3%
803 4
0.3%

임대면적
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.270487
Minimum38.538
Maximum66.567
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-13T01:50:22.613374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38.538
5-th percentile38.538
Q138.538
median46.057
Q346.791
95-th percentile66.567
Maximum66.567
Range28.029
Interquartile range (IQR)8.253

Descriptive statistics

Standard deviation7.4525757
Coefficient of variation (CV)0.16462327
Kurtosis2.4138641
Mean45.270487
Median Absolute Deviation (MAD)0.734
Skewness1.5926053
Sum62111.108
Variance55.540885
MonotonicityNot monotonic
2023-12-13T01:50:22.740974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
38.538 512
37.3%
46.791 384
28.0%
46.057 332
24.2%
66.567 80
 
5.8%
65.444 24
 
1.7%
60.637 24
 
1.7%
48.105 16
 
1.2%
ValueCountFrequency (%)
38.538 512
37.3%
46.057 332
24.2%
46.791 384
28.0%
48.105 16
 
1.2%
60.637 24
 
1.7%
65.444 24
 
1.7%
66.567 80
 
5.8%
ValueCountFrequency (%)
66.567 80
 
5.8%
65.444 24
 
1.7%
60.637 24
 
1.7%
48.105 16
 
1.2%
46.791 384
28.0%
46.057 332
24.2%
38.538 512
37.3%

전용면적
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.745825
Minimum21.278
Maximum36.783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-13T01:50:22.886425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21.278
5-th percentile21.278
Q121.295
median21.295
Q325.855
95-th percentile36.783
Maximum36.783
Range15.505
Interquartile range (IQR)4.56

Descriptive statistics

Standard deviation3.9866993
Coefficient of variation (CV)0.16789054
Kurtosis3.9359916
Mean23.745825
Median Absolute Deviation (MAD)0.017
Skewness2.0273219
Sum32579.272
Variance15.893772
MonotonicityNot monotonic
2023-12-13T01:50:23.048050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
21.295 512
37.3%
25.855 384
28.0%
21.278 332
24.2%
36.783 80
 
5.8%
30.055 24
 
1.7%
27.765 24
 
1.7%
22.081 16
 
1.2%
ValueCountFrequency (%)
21.278 332
24.2%
21.295 512
37.3%
22.081 16
 
1.2%
25.855 384
28.0%
27.765 24
 
1.7%
30.055 24
 
1.7%
36.783 80
 
5.8%
ValueCountFrequency (%)
36.783 80
 
5.8%
30.055 24
 
1.7%
27.765 24
 
1.7%
25.855 384
28.0%
22.081 16
 
1.2%
21.295 512
37.3%
21.278 332
24.2%

공용면적
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.872901
Minimum12.695
Maximum21.929
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-13T01:50:23.187626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.695
5-th percentile12.695
Q112.695
median14.995
Q315.414
95-th percentile21.929
Maximum21.929
Range9.234
Interquartile range (IQR)2.719

Descriptive statistics

Standard deviation2.4573073
Coefficient of variation (CV)0.16522045
Kurtosis2.5420137
Mean14.872901
Median Absolute Deviation (MAD)0.419
Skewness1.6512058
Sum20405.62
Variance6.0383593
MonotonicityNot monotonic
2023-12-13T01:50:23.313455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
12.695 512
37.3%
15.414 384
28.0%
14.995 332
24.2%
21.929 80
 
5.8%
21.57 24
 
1.7%
20.106 24
 
1.7%
15.87 16
 
1.2%
ValueCountFrequency (%)
12.695 512
37.3%
14.995 332
24.2%
15.414 384
28.0%
15.87 16
 
1.2%
20.106 24
 
1.7%
21.57 24
 
1.7%
21.929 80
 
5.8%
ValueCountFrequency (%)
21.929 80
 
5.8%
21.57 24
 
1.7%
20.106 24
 
1.7%
15.87 16
 
1.2%
15.414 384
28.0%
14.995 332
24.2%
12.695 512
37.3%

임대구분
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
수급자
343 
제외1차
343 
제외2차
343 
일반
343 

Length

Max length4
Median length3.5
Mean length3.25
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수급자
2nd row제외1차
3rd row제외2차
4th row일반
5th row수급자

Common Values

ValueCountFrequency (%)
수급자 343
25.0%
제외1차 343
25.0%
제외2차 343
25.0%
일반 343
25.0%

Length

2023-12-13T01:50:23.442756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:50:23.552406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수급자 343
25.0%
제외1차 343
25.0%
제외2차 343
25.0%
일반 343
25.0%

보증금
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7188913.3
Minimum1911000
Maximum20083000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-13T01:50:23.673094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1911000
5-th percentile1911000
Q14289000
median7269000
Q310841000
95-th percentile14116000
Maximum20083000
Range18172000
Interquartile range (IQR)6552000

Descriptive statistics

Standard deviation4173184
Coefficient of variation (CV)0.58050276
Kurtosis-0.23789694
Mean7188913.3
Median Absolute Deviation (MAD)3572000
Skewness0.54825395
Sum9.863189 × 109
Variance1.7415464 × 1013
MonotonicityNot monotonic
2023-12-13T01:50:23.804831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
11627000 128
9.3%
7760000 128
9.3%
4860000 128
9.3%
1960000 128
9.3%
14116000 96
 
7.0%
9421000 96
 
7.0%
5900000 96
 
7.0%
2380000 96
 
7.0%
4590000 83
 
6.0%
1911000 83
 
6.0%
Other values (18) 310
22.6%
ValueCountFrequency (%)
1911000 83
6.0%
1960000 128
9.3%
1984000 4
 
0.3%
2380000 96
7.0%
2494000 6
 
0.4%
2700000 6
 
0.4%
3386000 20
 
1.5%
4590000 83
6.0%
4789000 4
 
0.3%
4860000 128
9.3%
ValueCountFrequency (%)
20083000 20
 
1.5%
15345000 6
 
0.4%
14298000 6
 
0.4%
14116000 96
7.0%
13404000 20
 
1.5%
11627000 128
9.3%
11337000 4
 
0.3%
10841000 83
6.0%
10287000 6
 
0.4%
9576000 6
 
0.4%

월임대료
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76688.703
Minimum38070
Maximum175890
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-13T01:50:23.948496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38070
5-th percentile38070
Q156980
median75900
Q3101130
95-th percentile123970
Maximum175890
Range137820
Interquartile range (IQR)44150

Descriptive statistics

Standard deviation29421.736
Coefficient of variation (CV)0.3836515
Kurtosis0.36422406
Mean76688.703
Median Absolute Deviation (MAD)24645
Skewness0.69982116
Sum1.052169 × 108
Variance8.6563855 × 108
MonotonicityNot monotonic
2023-12-13T01:50:24.081121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
102300 128
9.3%
76990 128
9.3%
58010 128
9.3%
39030 128
9.3%
123970 96
 
7.0%
93330 96
 
7.0%
70360 96
 
7.0%
47390 96
 
7.0%
56980 83
 
6.0%
38070 83
 
6.0%
Other values (18) 310
22.6%
ValueCountFrequency (%)
38070 83
6.0%
39030 128
9.3%
39500 4
 
0.3%
47390 96
7.0%
49670 6
 
0.4%
53770 6
 
0.4%
56980 83
6.0%
58010 128
9.3%
59360 4
 
0.3%
67420 20
 
1.5%
ValueCountFrequency (%)
175890 20
 
1.5%
142800 6
 
0.4%
133110 6
 
0.4%
132500 20
 
1.5%
123970 96
7.0%
107180 6
 
0.4%
105720 4
 
0.3%
102300 128
9.3%
101130 83
6.0%
99960 20
 
1.5%

Interactions

2023-12-13T01:50:19.505892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:13.961284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:14.825719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:15.730909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:16.589049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:17.398108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:18.363060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:19.651700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:14.060182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:14.950389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:15.861565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:16.712321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:17.498693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:18.538444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:19.805476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:14.194527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:15.068768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:15.988928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:16.844827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:17.609390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:18.689982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:20.294261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:14.315094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:15.195003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:16.096380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:16.977189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:17.731863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:18.896218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:20.411359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:14.451906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:15.300962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:16.229587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:17.086473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:17.883882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:19.075850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:20.528911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:14.565278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:15.410031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:16.356125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:17.168892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:18.040197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:19.212264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:20.705435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:14.691850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:15.571875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:16.479719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:17.289046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:18.192411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:19.355875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:50:24.177732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
임대대상명순번동호임대면적전용면적공용면적임대구분보증금월임대료
임대대상명1.0000.6840.7750.3600.4510.4210.4510.0000.5020.367
순번0.6841.0000.9770.7780.6830.6890.6830.0000.3120.254
동0.7750.9771.0000.2380.4330.4390.4330.0000.3980.261
호0.3600.7780.2381.0000.5930.5330.5930.0000.1850.374
임대면적0.4510.6830.4330.5931.0000.9721.0000.0000.6640.520
전용면적0.4210.6890.4390.5330.9721.0000.9720.0000.6860.749
공용면적0.4510.6830.4330.5931.0000.9721.0000.0000.6640.520
임대구분0.0000.0000.0000.0000.0000.0000.0001.0000.9220.880
보증금0.5020.3120.3980.1850.6640.6860.6640.9221.0000.992
월임대료0.3670.2540.2610.3740.5200.7490.5200.8800.9921.000
2023-12-13T01:50:24.322030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동임대구분임대대상명
동1.0000.0000.567
임대구분0.0001.0000.000
임대대상명0.5670.0001.000
2023-12-13T01:50:24.423520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번호임대면적전용면적공용면적보증금월임대료임대대상명동임대구분
순번1.0000.3660.0070.3960.0070.1100.1170.5320.9230.000
호0.3661.0000.1270.1320.1270.0270.0400.3560.1550.000
임대면적0.0070.1271.0000.6281.0000.1810.2230.5470.3660.000
전용면적0.3960.1320.6281.0000.6280.2750.3120.5120.3720.000
공용면적0.0070.1271.0000.6281.0000.1810.2230.5470.3660.000
보증금0.1100.0270.1810.2750.1811.0000.9950.4170.2530.864
월임대료0.1170.0400.2230.3120.2230.9951.0000.2670.1600.791
임대대상명0.5320.3560.5470.5120.5470.4170.2671.0000.5670.000
동0.9230.1550.3660.3720.3660.2530.1600.5671.0000.000
임대구분0.0000.0000.0000.0000.0000.8640.7910.0000.0001.000

Missing values

2023-12-13T01:50:20.882783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:50:21.061177image/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

임대대상명순번동호임대면적전용면적공용면적임대구분보증금월임대료
0성남동(누리보듬)110120146.05721.27814.995수급자191100038070
1성남동(누리보듬)210120146.05721.27814.995제외1차459000056980
2성남동(누리보듬)310120146.05721.27814.995제외2차726900075900
3성남동(누리보듬)410120146.05721.27814.995일반10841000101130
4성남동(누리보듬)510120246.05721.27814.995수급자191100038070
5성남동(누리보듬)610120246.05721.27814.995제외1차459000056980
6성남동(누리보듬)710120246.05721.27814.995제외2차726900075900
7성남동(누리보듬)810120246.05721.27814.995일반10841000101130
8성남동(누리보듬)910120346.05721.27814.995수급자191100038070
9성남동(누리보듬)1010120346.05721.27814.995제외1차459000056980
임대대상명순번동호임대면적전용면적공용면적임대구분보증금월임대료
1362인동(누리보듬)96710471238.53821.29512.695제외2차776000076990
1363인동(누리보듬)96810471238.53821.29512.695일반11627000102300
1364인동(누리보듬)96910471338.53821.29512.695수급자196000039030
1365인동(누리보듬)97010471338.53821.29512.695제외1차486000058010
1366인동(누리보듬)97110471338.53821.29512.695제외2차776000076990
1367인동(누리보듬)97210471338.53821.29512.695일반11627000102300
1368인동(누리보듬)97310471438.53821.29512.695수급자196000039030
1369인동(누리보듬)97410471438.53821.29512.695제외1차486000058010
1370인동(누리보듬)97510471438.53821.29512.695제외2차776000076990
1371인동(누리보듬)97610471438.53821.29512.695일반11627000102300