Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory937.5 KiB
Average record size in memory96.0 B

Variable types

Categorical2
Numeric8

Dataset

Description대전도시공사 영구임대아파트는 저소득층 주거공급 및 취약계층 주거복지를 위해 제공되는 영구임대아파트 임대현황 자료 입니다.
URLhttps://www.data.go.kr/data/15103607/fileData.do

Alerts

순번 is highly overall correlated with High correlation
is highly overall correlated with 순번High correlation
임대면적 is highly overall correlated with 전용면적 and 2 other fieldsHigh correlation
전용면적 is highly overall correlated with 임대면적 and 3 other fieldsHigh correlation
공용면적 is highly overall correlated with 임대면적 and 3 other fieldsHigh correlation
보증금 is highly overall correlated with 월임대료 and 1 other fieldsHigh correlation
월임대료 is highly overall correlated with 임대면적 and 3 other fieldsHigh correlation
임대대상명 is highly overall correlated with 전용면적 and 1 other fieldsHigh correlation
임대구분 is highly overall correlated with 보증금 High correlation

Reproduction

Analysis started2023-12-12 14:14:53.462882
Analysis finished2023-12-12 14:15:02.821704
Duration9.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

임대대상명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
한마음
5076 
보라
2463 
송강마을
1856 
오류동(누리보듬)
605 

Length

Max length9
Median length3
Mean length3.3023
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row송강마을
2nd row보라
3rd row보라
4th row한마음
5th row한마음

Common Values

ValueCountFrequency (%)
한마음 5076
50.8%
보라 2463
24.6%
송강마을 1856
 
18.6%
오류동(누리보듬) 605
 
6.0%

Length

2023-12-12T23:15:02.923376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:15:03.060625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한마음 5076
50.8%
보라 2463
24.6%
송강마을 1856
 
18.6%
오류동(누리보듬 605
 
6.0%

순번
Real number (ℝ)

HIGH CORRELATION 

Distinct5982
Distinct (%)59.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2509.2606
Minimum1
Maximum7072
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:15:03.200041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile177
Q1906.75
median2084.5
Q33632.25
95-th percentile6382.05
Maximum7072
Range7071
Interquartile range (IQR)2725.5

Descriptive statistics

Standard deviation1921.7584
Coefficient of variation (CV)0.76586639
Kurtosis-0.49602491
Mean2509.2606
Median Absolute Deviation (MAD)1286.5
Skewness0.73711722
Sum25092606
Variance3693155.2
MonotonicityNot monotonic
2023-12-12T23:15:03.358227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
846 4
 
< 0.1%
383 4
 
< 0.1%
223 4
 
< 0.1%
284 4
 
< 0.1%
354 4
 
< 0.1%
528 4
 
< 0.1%
160 4
 
< 0.1%
427 4
 
< 0.1%
743 4
 
< 0.1%
688 4
 
< 0.1%
Other values (5972) 9960
99.6%
ValueCountFrequency (%)
1 2
< 0.1%
2 1
 
< 0.1%
3 4
< 0.1%
4 2
< 0.1%
5 1
 
< 0.1%
6 4
< 0.1%
7 3
< 0.1%
8 3
< 0.1%
9 3
< 0.1%
10 4
< 0.1%
ValueCountFrequency (%)
7072 1
< 0.1%
7071 1
< 0.1%
7069 1
< 0.1%
7068 1
< 0.1%
7067 1
< 0.1%
7065 1
< 0.1%
7064 1
< 0.1%
7063 1
< 0.1%
7061 1
< 0.1%
7060 1
< 0.1%


Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.8116
Minimum101
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:15:03.519371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1101
median103
Q3104
95-th percentile106
Maximum106
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6115102
Coefficient of variation (CV)0.015674401
Kurtosis-0.84737701
Mean102.8116
Median Absolute Deviation (MAD)1
Skewness0.54126768
Sum1028116
Variance2.5969651
MonotonicityNot monotonic
2023-12-12T23:15:03.685220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
101 2786
27.9%
102 2197
22.0%
103 1811
18.1%
104 1396
14.0%
105 941
 
9.4%
106 869
 
8.7%
ValueCountFrequency (%)
101 2786
27.9%
102 2197
22.0%
103 1811
18.1%
104 1396
14.0%
105 941
 
9.4%
106 869
 
8.7%
ValueCountFrequency (%)
106 869
 
8.7%
105 941
 
9.4%
104 1396
14.0%
103 1811
18.1%
102 2197
22.0%
101 2786
27.9%


Real number (ℝ)

Distinct348
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean819.7134
Minimum101
Maximum1709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:15:03.847669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile116
Q1419
median810
Q31206
95-th percentile1505
Maximum1709
Range1608
Interquartile range (IQR)787

Descriptive statistics

Standard deviation430.78251
Coefficient of variation (CV)0.5255282
Kurtosis-1.1773449
Mean819.7134
Median Absolute Deviation (MAD)394
Skewness-0.00015479761
Sum8197134
Variance185573.57
MonotonicityNot monotonic
2023-12-12T23:15:04.058643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
507 49
 
0.5%
706 49
 
0.5%
1204 49
 
0.5%
708 48
 
0.5%
1305 47
 
0.5%
1304 47
 
0.5%
1402 47
 
0.5%
1507 47
 
0.5%
1502 47
 
0.5%
407 47
 
0.5%
Other values (338) 9523
95.2%
ValueCountFrequency (%)
101 35
0.4%
102 31
0.3%
103 40
0.4%
104 28
0.3%
105 39
0.4%
106 36
0.4%
107 38
0.4%
108 45
0.4%
109 41
0.4%
110 37
0.4%
ValueCountFrequency (%)
1709 3
< 0.1%
1708 3
< 0.1%
1707 3
< 0.1%
1706 3
< 0.1%
1705 2
< 0.1%
1704 3
< 0.1%
1703 3
< 0.1%
1702 2
< 0.1%
1701 4
< 0.1%
1609 4
< 0.1%

임대면적
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.892215
Minimum40.93
Maximum84.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:15:04.210354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40.93
5-th percentile40.93
Q141.69
median41.69
Q348.98
95-th percentile60.32
Maximum84.52
Range43.59
Interquartile range (IQR)7.29

Descriptive statistics

Standard deviation6.3415253
Coefficient of variation (CV)0.13818303
Kurtosis5.8872821
Mean45.892215
Median Absolute Deviation (MAD)0.76
Skewness1.9827261
Sum458922.15
Variance40.214943
MonotonicityNot monotonic
2023-12-12T23:15:04.324466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
41.69 3617
36.2%
48.98 1856
18.6%
40.93 1691
16.9%
48.36 841
 
8.4%
60.32 612
 
6.1%
46.52 441
 
4.4%
58.55 329
 
3.3%
46.535 232
 
2.3%
42.055 226
 
2.3%
65.031 41
 
0.4%
Other values (5) 114
 
1.1%
ValueCountFrequency (%)
40.93 1691
16.9%
41.69 3617
36.2%
42.055 226
 
2.3%
46.52 441
 
4.4%
46.535 232
 
2.3%
48.36 841
 
8.4%
48.98 1856
18.6%
52.662 28
 
0.3%
58.198 40
 
0.4%
58.55 329
 
3.3%
ValueCountFrequency (%)
84.52 38
 
0.4%
83.38 6
 
0.1%
81.86 2
 
< 0.1%
65.031 41
 
0.4%
60.32 612
 
6.1%
58.55 329
 
3.3%
58.198 40
 
0.4%
52.662 28
 
0.3%
48.98 1856
18.6%
48.36 841
8.4%

전용면적
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.206081
Minimum19.804
Maximum57.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:15:04.449203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19.804
5-th percentile27.406
Q128.95
median28.95
Q333.87
95-th percentile39.9
Maximum57.9
Range38.096
Interquartile range (IQR)4.92

Descriptive statistics

Standard deviation4.50902
Coefficient of variation (CV)0.14449171
Kurtosis1.3228635
Mean31.206081
Median Absolute Deviation (MAD)0.72
Skewness0.47550892
Sum312060.81
Variance20.331261
MonotonicityNot monotonic
2023-12-12T23:15:04.576033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
28.95 3617
36.2%
33.87 1856
18.6%
28.23 1691
16.9%
35.1 841
 
8.4%
39.9 612
 
6.1%
32.82 441
 
4.4%
41.82 329
 
3.3%
21.914 232
 
2.3%
19.804 226
 
2.3%
30.624 41
 
0.4%
Other values (5) 114
 
1.1%
ValueCountFrequency (%)
19.804 226
 
2.3%
21.914 232
 
2.3%
24.799 28
 
0.3%
27.406 40
 
0.4%
28.23 1691
16.9%
28.95 3617
36.2%
30.624 41
 
0.4%
32.82 441
 
4.4%
33.87 1856
18.6%
35.1 841
 
8.4%
ValueCountFrequency (%)
57.9 6
 
0.1%
56.46 2
 
< 0.1%
41.82 329
 
3.3%
39.9 612
 
6.1%
39.801 38
 
0.4%
35.1 841
 
8.4%
33.87 1856
18.6%
32.82 441
 
4.4%
30.624 41
 
0.4%
28.95 3617
36.2%

공용면적
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.958853
Minimum12.049
Maximum25.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:15:04.698627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.049
5-th percentile12.7
Q112.74
median12.74
Q315.11
95-th percentile20.42
Maximum25.48
Range13.431
Interquartile range (IQR)2.37

Descriptive statistics

Standard deviation2.1501018
Coefficient of variation (CV)0.15403142
Kurtosis4.8979199
Mean13.958853
Median Absolute Deviation (MAD)0.04
Skewness2.2265704
Sum139588.53
Variance4.6229379
MonotonicityNot monotonic
2023-12-12T23:15:04.819891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
12.74 3617
36.2%
15.11 1856
18.6%
12.7 1691
16.9%
13.26 841
 
8.4%
20.42 612
 
6.1%
13.7 441
 
4.4%
16.73 329
 
3.3%
13.333 232
 
2.3%
12.049 226
 
2.3%
18.632 41
 
0.4%
Other values (5) 114
 
1.1%
ValueCountFrequency (%)
12.049 226
 
2.3%
12.7 1691
16.9%
12.74 3617
36.2%
13.26 841
 
8.4%
13.333 232
 
2.3%
13.7 441
 
4.4%
15.088 28
 
0.3%
15.11 1856
18.6%
16.674 40
 
0.4%
16.73 329
 
3.3%
ValueCountFrequency (%)
25.48 6
 
0.1%
25.4 2
 
< 0.1%
24.215 38
 
0.4%
20.42 612
 
6.1%
18.632 41
 
0.4%
16.73 329
 
3.3%
16.674 40
 
0.4%
15.11 1856
18.6%
15.088 28
 
0.3%
13.7 441
 
4.4%

임대구분
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
제외2차
2522 
제외1차
2506 
수급자
2488 
일반
2484 

Length

Max length4
Median length4
Mean length3.2544
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제외2차 2522
25.2%
제외1차 2506
25.1%
수급자 2488
24.9%
일반 2484
24.8%

Length

2023-12-12T23:15:04.971485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:15:05.086791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제외2차 2522
25.2%
제외1차 2506
25.1%
수급자 2488
24.9%
일반 2484
24.8%

보증금
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3635518
Minimum1859000
Maximum23224000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:15:05.215662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1859000
5-th percentile1988000
Q12384000
median2733000
Q34187000
95-th percentile7467000
Maximum23224000
Range21365000
Interquartile range (IQR)1803000

Descriptive statistics

Standard deviation2021852.1
Coefficient of variation (CV)0.55613867
Kurtosis14.574275
Mean3635518
Median Absolute Deviation (MAD)745000
Skewness2.9476599
Sum3.635518 × 1010
Variance4.0878861 × 1012
MonotonicityNot monotonic
2023-12-12T23:15:05.359093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1988000 914
 
9.1%
4187000 911
 
9.1%
2384000 907
 
9.1%
2733000 885
 
8.8%
5433000 476
 
4.8%
3909000 472
 
4.7%
7467000 456
 
4.6%
2326000 452
 
4.5%
2323000 431
 
4.3%
2661000 428
 
4.3%
Other values (48) 3668
36.7%
ValueCountFrequency (%)
1859000 60
 
0.6%
1939000 416
4.2%
1988000 914
9.1%
2058000 59
 
0.6%
2256000 118
 
1.2%
2323000 431
4.3%
2326000 452
4.5%
2328000 9
 
0.1%
2384000 907
9.1%
2409000 205
 
2.1%
ValueCountFrequency (%)
23224000 8
 
0.1%
17869000 10
 
0.1%
15991000 7
 
0.1%
15429000 10
 
0.1%
14470000 6
 
0.1%
12787000 57
0.6%
11871000 12
 
0.1%
11555000 57
0.6%
10623000 11
 
0.1%
9613000 8
 
0.1%

월임대료
Real number (ℝ)

HIGH CORRELATION 

Distinct128
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62681.069
Minimum37030
Maximum213150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:15:05.732313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37030
5-th percentile44200
Q149200
median57900
Q369300
95-th percentile100900
Maximum213150
Range176120
Interquartile range (IQR)20100

Descriptive statistics

Standard deviation17789.582
Coefficient of variation (CV)0.28381108
Kurtosis6.1975114
Mean62681.069
Median Absolute Deviation (MAD)9600
Skewness1.770731
Sum6.2681069 × 108
Variance3.1646922 × 108
MonotonicityNot monotonic
2023-12-12T23:15:06.153748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69300 571
 
5.7%
48900 541
 
5.4%
50600 539
 
5.4%
52800 522
 
5.2%
100900 456
 
4.6%
84300 299
 
3.0%
69600 290
 
2.9%
57100 275
 
2.8%
51300 264
 
2.6%
49200 262
 
2.6%
Other values (118) 5981
59.8%
ValueCountFrequency (%)
37030 60
 
0.6%
40100 55
 
0.5%
40500 25
 
0.2%
40970 59
 
0.6%
42100 53
 
0.5%
42600 29
 
0.3%
42900 59
 
0.6%
43800 57
 
0.6%
44200 157
1.6%
45700 63
0.6%
ValueCountFrequency (%)
213150 8
 
0.1%
164400 10
 
0.1%
157650 10
 
0.1%
147310 7
 
0.1%
133470 6
 
0.1%
121540 12
 
0.1%
119800 2
 
< 0.1%
118140 57
0.6%
116030 9
 
0.1%
116000 1
 
< 0.1%

Interactions

2023-12-12T23:15:01.442344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:55.207240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:56.219259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:57.182468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:58.200893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:59.040997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:59.729986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:00.407808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:01.589596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:55.372151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:56.350497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:57.276238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:58.328655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:59.132451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:59.813389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:00.769578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:01.711749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:55.502234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:56.452487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:57.389067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:58.416454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:59.222902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:59.890828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:00.845695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:01.849594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:55.611374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:56.582788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:57.525917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:58.541910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:59.318745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:59.983760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:00.939226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:01.973495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:55.726563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:56.700332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:57.659090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:58.631984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:59.399841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:00.070309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:01.017181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:02.142338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:55.860629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:56.820353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:57.820980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:58.739868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:59.480767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:00.162668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:01.108593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:02.269682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:55.977900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:56.934125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:57.961666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:58.859821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:59.567603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:00.251344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:01.223762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:02.378224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:56.096550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:57.034197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:58.067338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:58.945826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:59.646727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:00.325879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:01.313855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:15:06.422954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
임대대상명순번임대면적전용면적공용면적임대구분보증금월임대료
임대대상명1.0000.6210.5080.2810.6200.8360.7810.0000.6340.558
순번0.6211.0000.9070.5180.6080.6580.7090.0000.2390.300
0.5080.9071.0000.0920.6750.7240.6840.0000.2490.389
0.2810.5180.0921.0000.1860.1950.1640.0000.1620.170
임대면적0.6200.6080.6750.1861.0000.9670.9800.0000.6130.714
전용면적0.8360.6580.7240.1950.9671.0000.9300.0000.5270.596
공용면적0.7810.7090.6840.1640.9800.9301.0000.0000.5930.648
임대구분0.0000.0000.0000.0000.0000.0000.0001.0000.6720.595
보증금0.6340.2390.2490.1620.6130.5270.5930.6721.0000.982
월임대료0.5580.3000.3890.1700.7140.5960.6480.5950.9821.000
2023-12-12T23:15:06.700825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
임대구분임대대상명
임대구분1.0000.000
임대대상명0.0001.000
2023-12-12T23:15:06.836439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번임대면적전용면적공용면적보증금월임대료임대대상명임대구분
순번1.0000.9430.163-0.0180.1280.085-0.125-0.1270.4230.000
0.9431.000-0.0240.1510.2540.243-0.032-0.0130.3510.000
0.163-0.0241.0000.000-0.038-0.0020.0100.0790.1710.000
임대면적-0.0180.1510.0001.0000.8370.9510.4760.6380.4790.000
전용면적0.1280.254-0.0380.8371.0000.8650.3250.5060.7530.000
공용면적0.0850.243-0.0020.9510.8651.0000.4250.5980.6710.000
보증금-0.125-0.0320.0100.4760.3250.4251.0000.9100.4450.503
월임대료-0.127-0.0130.0790.6380.5060.5980.9101.0000.3790.427
임대대상명0.4230.3510.1710.4790.7530.6710.4450.3791.0000.000
임대구분0.0000.0000.0000.0000.0000.0000.5030.4270.0001.000

Missing values

2023-12-12T23:15:02.572335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:15:02.747627image/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

임대대상명순번임대면적전용면적공용면적임대구분보증금월임대료
11905송강마을1358102140448.9833.8715.11제외1차390900067800
886보라887101120240.9328.2312.7제외2차266100051300
3067보라306810530258.5541.8216.73일반605300097500
10167한마음6692106110541.6928.9512.74일반418700069300
4634한마음1159101151048.3635.113.26제외2차331600058700
61보라6210111640.9328.2312.7제외1차232300042600
12189송강마을164210360148.9833.8715.11제외1차390900069600
1445보라144610240340.9328.2312.7제외1차232300047800
9301한마음5826105150360.3239.920.42제외1차328900063800
7590한마음411510451341.6928.9512.74제외2차273300052800
임대대상명순번임대면적전용면적공용면적임대구분보증금월임대료
10383한마음6908106131941.6928.9512.74일반418700069300
9814한마음633910661741.6928.9512.74제외2차273300052800
5312한마음183710290141.6928.9512.74수급자198800048900
590보라59110180840.9328.2312.7제외2차266100051300
13976오류동(누리보듬)789101160165.03130.62418.632수급자287500057260
4810한마음133510221541.6928.9512.74제외2차273300047900
10130한마음6655106101641.6928.9512.74제외2차273300052800
13216오류동(누리보듬)2910140846.53521.91413.333수급자205800040970
12035송강마을148810320248.9833.8715.11일반7467000100900
21보라2210110640.9328.2312.7제외1차232300042600