Overview

Dataset statistics

Number of variables15
Number of observations424
Missing cells2488
Missing cells (%)39.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.2 KiB
Average record size in memory133.3 B

Variable types

Text1
Categorical1
Numeric12
Unsupported1

Dataset

Description한국주택공사에서 관리하는 행복주택 공급유형별 공가(6개월 이상) 현황정보로 단지명, 공급계층, 대상주택수등의 자료를 제공합니다.
Author한국토지주택공사
URLhttps://www.data.go.kr/data/15064531/fileData.do

Alerts

20171231 is highly overall correlated with 20180331High correlation
20180331 is highly overall correlated with 20171231High correlation
20180930 is highly overall correlated with 20181231 and 1 other fieldsHigh correlation
20181231 is highly overall correlated with 20180930 and 1 other fieldsHigh correlation
20190331 is highly overall correlated with 20180930 and 2 other fieldsHigh correlation
20190630 is highly overall correlated with 20190331 and 1 other fieldsHigh correlation
20190930 is highly overall correlated with 20190630 and 1 other fieldsHigh correlation
20191231 is highly overall correlated with 20190930 and 1 other fieldsHigh correlation
20200331 is highly overall correlated with 20191231 and 1 other fieldsHigh correlation
20200630 is highly overall correlated with 20200331High correlation
20161231 has 424 (100.0%) missing valuesMissing
20171231 has 328 (77.4%) missing valuesMissing
20180331 has 316 (74.5%) missing valuesMissing
20180630 has 304 (71.7%) missing valuesMissing
20180930 has 304 (71.7%) missing valuesMissing
20181231 has 240 (56.6%) missing valuesMissing
20190331 has 188 (44.3%) missing valuesMissing
20190630 has 156 (36.8%) missing valuesMissing
20190930 has 116 (27.4%) missing valuesMissing
20191231 has 80 (18.9%) missing valuesMissing
20200331 has 32 (7.5%) missing valuesMissing
20161231 is an unsupported type, check if it needs cleaning or further analysisUnsupported
대상주택수 has 14 (3.3%) zerosZeros
20171231 has 85 (20.0%) zerosZeros
20180331 has 94 (22.2%) zerosZeros
20180630 has 81 (19.1%) zerosZeros
20180930 has 57 (13.4%) zerosZeros
20181231 has 127 (30.0%) zerosZeros
20190331 has 167 (39.4%) zerosZeros
20190630 has 181 (42.7%) zerosZeros
20190930 has 195 (46.0%) zerosZeros
20191231 has 215 (50.7%) zerosZeros
20200331 has 204 (48.1%) zerosZeros
20200630 has 189 (44.6%) zerosZeros

Reproduction

Analysis started2023-12-11 23:18:25.469975
Analysis finished2023-12-11 23:18:41.027417
Duration15.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct106
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2023-12-12T08:18:41.178135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length12.349057
Min length4

Characters and Unicode

Total characters5236
Distinct characters177
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

Unique0 ?
Unique (%)0.0%

Sample

1st row서울삼전 1단지
2nd row서울삼전 1단지
3rd row서울삼전 1단지
4th row서울삼전 1단지
5th row서울가좌 행복주택
ValueCountFrequency (%)
행복주택 216
 
22.6%
lh아파트 12
 
1.3%
a-1 12
 
1.3%
a3bl 12
 
1.3%
행복주택리츠 12
 
1.3%
lh천년나무 8
 
0.8%
a-3bl 8
 
0.8%
a-6 8
 
0.8%
3단지 8
 
0.8%
a-4bl 8
 
0.8%
Other values (159) 652
68.2%
2023-12-12T08:18:41.531411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
532
 
10.2%
372
 
7.1%
308
 
5.9%
304
 
5.8%
288
 
5.5%
A 172
 
3.3%
1 164
 
3.1%
L 152
 
2.9%
- 152
 
2.9%
2 144
 
2.8%
Other values (167) 2648
50.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3344
63.9%
Space Separator 532
 
10.2%
Uppercase Letter 520
 
9.9%
Decimal Number 508
 
9.7%
Dash Punctuation 152
 
2.9%
Close Punctuation 84
 
1.6%
Open Punctuation 84
 
1.6%
Lowercase Letter 12
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
372
 
11.1%
308
 
9.2%
304
 
9.1%
288
 
8.6%
96
 
2.9%
84
 
2.5%
64
 
1.9%
56
 
1.7%
52
 
1.6%
52
 
1.6%
Other values (145) 1668
49.9%
Decimal Number
ValueCountFrequency (%)
1 164
32.3%
2 144
28.3%
3 68
13.4%
4 32
 
6.3%
5 28
 
5.5%
6 24
 
4.7%
0 20
 
3.9%
7 16
 
3.1%
8 8
 
1.6%
9 4
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
A 172
33.1%
L 152
29.2%
B 120
23.1%
H 56
 
10.8%
C 16
 
3.1%
S 4
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
a 8
66.7%
c 4
33.3%
Space Separator
ValueCountFrequency (%)
532
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 152
100.0%
Close Punctuation
ValueCountFrequency (%)
) 84
100.0%
Open Punctuation
ValueCountFrequency (%)
( 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3344
63.9%
Common 1360
26.0%
Latin 532
 
10.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
372
 
11.1%
308
 
9.2%
304
 
9.1%
288
 
8.6%
96
 
2.9%
84
 
2.5%
64
 
1.9%
56
 
1.7%
52
 
1.6%
52
 
1.6%
Other values (145) 1668
49.9%
Common
ValueCountFrequency (%)
532
39.1%
1 164
 
12.1%
- 152
 
11.2%
2 144
 
10.6%
) 84
 
6.2%
( 84
 
6.2%
3 68
 
5.0%
4 32
 
2.4%
5 28
 
2.1%
6 24
 
1.8%
Other values (4) 48
 
3.5%
Latin
ValueCountFrequency (%)
A 172
32.3%
L 152
28.6%
B 120
22.6%
H 56
 
10.5%
C 16
 
3.0%
a 8
 
1.5%
S 4
 
0.8%
c 4
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3344
63.9%
ASCII 1892
36.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
532
28.1%
A 172
 
9.1%
1 164
 
8.7%
L 152
 
8.0%
- 152
 
8.0%
2 144
 
7.6%
B 120
 
6.3%
) 84
 
4.4%
( 84
 
4.4%
3 68
 
3.6%
Other values (12) 220
11.6%
Hangul
ValueCountFrequency (%)
372
 
11.1%
308
 
9.2%
304
 
9.1%
288
 
8.6%
96
 
2.9%
84
 
2.5%
64
 
1.9%
56
 
1.7%
52
 
1.6%
52
 
1.6%
Other values (145) 1668
49.9%

공급계층
Categorical

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
고령자
106 
대학생
106 
신혼부부 한부모가족
106 
청년 계층
106 

Length

Max length10
Median length7.5
Mean length5.25
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row고령자
2nd row대학생
3rd row신혼부부 한부모가족
4th row청년 계층
5th row고령자

Common Values

ValueCountFrequency (%)
고령자 106
25.0%
대학생 106
25.0%
신혼부부 한부모가족 106
25.0%
청년 계층 106
25.0%

Length

2023-12-12T08:18:41.664618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:18:41.772745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고령자 106
16.7%
대학생 106
16.7%
신혼부부 106
16.7%
한부모가족 106
16.7%
청년 106
16.7%
계층 106
16.7%

대상주택수
Real number (ℝ)

ZEROS 

Distinct194
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.01415
Minimum0
Maximum780
Zeros14
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-12T08:18:41.888322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q129
median71.5
Q3157
95-th percentile370.5
Maximum780
Range780
Interquartile range (IQR)128

Descriptive statistics

Standard deviation121.82879
Coefficient of variation (CV)1.068541
Kurtosis4.43622
Mean114.01415
Median Absolute Deviation (MAD)51.5
Skewness1.9009885
Sum48342
Variance14842.255
MonotonicityNot monotonic
2023-12-12T08:18:42.042014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
3.3%
20 10
 
2.4%
24 9
 
2.1%
36 8
 
1.9%
40 7
 
1.7%
60 7
 
1.7%
10 7
 
1.7%
4 6
 
1.4%
90 6
 
1.4%
102 6
 
1.4%
Other values (184) 344
81.1%
ValueCountFrequency (%)
0 14
3.3%
1 1
 
0.2%
2 3
 
0.7%
3 2
 
0.5%
4 6
1.4%
5 2
 
0.5%
6 3
 
0.7%
7 4
 
0.9%
8 3
 
0.7%
10 7
1.7%
ValueCountFrequency (%)
780 1
0.2%
702 1
0.2%
574 1
0.2%
570 1
0.2%
517 1
0.2%
506 1
0.2%
501 2
0.5%
491 1
0.2%
448 1
0.2%
436 1
0.2%

20161231
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing424
Missing (%)100.0%
Memory size3.9 KiB

20171231
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)9.4%
Missing328
Missing (%)77.4%
Infinite0
Infinite (%)0.0%
Mean2.9166667
Minimum0
Maximum83
Zeros85
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-12T08:18:42.181999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9.25
Maximum83
Range83
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.228093
Coefficient of variation (CV)4.5353463
Kurtosis28.655696
Mean2.9166667
Median Absolute Deviation (MAD)0
Skewness5.297364
Sum280
Variance174.98246
MonotonicityNot monotonic
2023-12-12T08:18:42.289671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 85
 
20.0%
83 2
 
0.5%
1 2
 
0.5%
3 2
 
0.5%
42 1
 
0.2%
4 1
 
0.2%
9 1
 
0.2%
41 1
 
0.2%
10 1
 
0.2%
(Missing) 328
77.4%
ValueCountFrequency (%)
0 85
20.0%
1 2
 
0.5%
3 2
 
0.5%
4 1
 
0.2%
9 1
 
0.2%
10 1
 
0.2%
41 1
 
0.2%
42 1
 
0.2%
83 2
 
0.5%
ValueCountFrequency (%)
83 2
 
0.5%
42 1
 
0.2%
41 1
 
0.2%
10 1
 
0.2%
9 1
 
0.2%
4 1
 
0.2%
3 2
 
0.5%
1 2
 
0.5%
0 85
20.0%

20180331
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)10.2%
Missing316
Missing (%)74.5%
Infinite0
Infinite (%)0.0%
Mean2.5555556
Minimum0
Maximum83
Zeros94
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-12T08:18:42.409552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.95
Maximum83
Range83
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.135784
Coefficient of variation (CV)4.7487851
Kurtosis31.584386
Mean2.5555556
Median Absolute Deviation (MAD)0
Skewness5.557702
Sum276
Variance147.27726
MonotonicityNot monotonic
2023-12-12T08:18:42.538961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 94
 
22.2%
1 4
 
0.9%
2 2
 
0.5%
43 1
 
0.2%
76 1
 
0.2%
4 1
 
0.2%
42 1
 
0.2%
7 1
 
0.2%
10 1
 
0.2%
83 1
 
0.2%
(Missing) 316
74.5%
ValueCountFrequency (%)
0 94
22.2%
1 4
 
0.9%
2 2
 
0.5%
3 1
 
0.2%
4 1
 
0.2%
7 1
 
0.2%
10 1
 
0.2%
42 1
 
0.2%
43 1
 
0.2%
76 1
 
0.2%
ValueCountFrequency (%)
83 1
 
0.2%
76 1
 
0.2%
43 1
 
0.2%
42 1
 
0.2%
10 1
 
0.2%
7 1
 
0.2%
4 1
 
0.2%
3 1
 
0.2%
2 2
0.5%
1 4
0.9%

20180630
Real number (ℝ)

MISSING  ZEROS 

Distinct29
Distinct (%)24.2%
Missing304
Missing (%)71.7%
Infinite0
Infinite (%)0.0%
Mean8.075
Minimum0
Maximum135
Zeros81
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-12T08:18:42.664164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.25
95-th percentile45.15
Maximum135
Range135
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation20.115282
Coefficient of variation (CV)2.4910566
Kurtosis16.366784
Mean8.075
Median Absolute Deviation (MAD)0
Skewness3.6806623
Sum969
Variance404.62458
MonotonicityNot monotonic
2023-12-12T08:18:42.832663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 81
 
19.1%
1 6
 
1.4%
39 2
 
0.5%
4 2
 
0.5%
2 2
 
0.5%
12 2
 
0.5%
8 2
 
0.5%
16 2
 
0.5%
90 1
 
0.2%
26 1
 
0.2%
Other values (19) 19
 
4.5%
(Missing) 304
71.7%
ValueCountFrequency (%)
0 81
19.1%
1 6
 
1.4%
2 2
 
0.5%
3 1
 
0.2%
4 2
 
0.5%
5 1
 
0.2%
6 1
 
0.2%
7 1
 
0.2%
8 2
 
0.5%
12 2
 
0.5%
ValueCountFrequency (%)
135 1
0.2%
90 1
0.2%
85 1
0.2%
61 1
0.2%
52 1
0.2%
48 1
0.2%
45 1
0.2%
40 1
0.2%
39 2
0.5%
35 1
0.2%

20180930
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct30
Distinct (%)25.0%
Missing304
Missing (%)71.7%
Infinite0
Infinite (%)0.0%
Mean7.55
Minimum0
Maximum75
Zeros57
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-12T08:18:42.990377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37.25
95-th percentile44.2
Maximum75
Range75
Interquartile range (IQR)7.25

Descriptive statistics

Standard deviation14.712854
Coefficient of variation (CV)1.9487224
Kurtosis8.2632877
Mean7.55
Median Absolute Deviation (MAD)1
Skewness2.8376803
Sum906
Variance216.46807
MonotonicityNot monotonic
2023-12-12T08:18:43.144485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 57
 
13.4%
1 9
 
2.1%
4 7
 
1.7%
3 7
 
1.7%
6 4
 
0.9%
16 4
 
0.9%
12 3
 
0.7%
7 3
 
0.7%
11 2
 
0.5%
14 2
 
0.5%
Other values (20) 22
 
5.2%
(Missing) 304
71.7%
ValueCountFrequency (%)
0 57
13.4%
1 9
 
2.1%
2 1
 
0.2%
3 7
 
1.7%
4 7
 
1.7%
5 2
 
0.5%
6 4
 
0.9%
7 3
 
0.7%
8 2
 
0.5%
9 1
 
0.2%
ValueCountFrequency (%)
75 1
0.2%
73 1
0.2%
57 1
0.2%
54 1
0.2%
52 1
0.2%
48 1
0.2%
44 1
0.2%
39 1
0.2%
34 1
0.2%
29 1
0.2%

20181231
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct27
Distinct (%)14.7%
Missing240
Missing (%)56.6%
Infinite0
Infinite (%)0.0%
Mean3.8967391
Minimum0
Maximum82
Zeros127
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-12T08:18:43.294549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile21.55
Maximum82
Range82
Interquartile range (IQR)1

Descriptive statistics

Standard deviation11.177905
Coefficient of variation (CV)2.868528
Kurtosis23.279877
Mean3.8967391
Median Absolute Deviation (MAD)0
Skewness4.4696336
Sum717
Variance124.94556
MonotonicityNot monotonic
2023-12-12T08:18:43.452975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 127
30.0%
1 12
 
2.8%
2 7
 
1.7%
4 5
 
1.2%
11 4
 
0.9%
15 3
 
0.7%
3 3
 
0.7%
6 2
 
0.5%
16 2
 
0.5%
5 2
 
0.5%
Other values (17) 17
 
4.0%
(Missing) 240
56.6%
ValueCountFrequency (%)
0 127
30.0%
1 12
 
2.8%
2 7
 
1.7%
3 3
 
0.7%
4 5
 
1.2%
5 2
 
0.5%
6 2
 
0.5%
7 1
 
0.2%
8 1
 
0.2%
9 1
 
0.2%
ValueCountFrequency (%)
82 1
0.2%
73 1
0.2%
52 1
0.2%
46 1
0.2%
43 1
0.2%
32 1
0.2%
30 1
0.2%
27 1
0.2%
26 1
0.2%
22 1
0.2%

20190331
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct27
Distinct (%)11.4%
Missing188
Missing (%)44.3%
Infinite0
Infinite (%)0.0%
Mean3.3940678
Minimum0
Maximum78
Zeros167
Zeros (%)39.4%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-12T08:18:43.590188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile18.5
Maximum78
Range78
Interquartile range (IQR)1

Descriptive statistics

Standard deviation9.9958186
Coefficient of variation (CV)2.9450851
Kurtosis26.534335
Mean3.3940678
Median Absolute Deviation (MAD)0
Skewness4.762915
Sum801
Variance99.91639
MonotonicityNot monotonic
2023-12-12T08:18:43.742660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 167
39.4%
1 15
 
3.5%
3 7
 
1.7%
2 5
 
1.2%
4 4
 
0.9%
20 3
 
0.7%
6 3
 
0.7%
7 3
 
0.7%
49 2
 
0.5%
14 2
 
0.5%
Other values (17) 25
 
5.9%
(Missing) 188
44.3%
ValueCountFrequency (%)
0 167
39.4%
1 15
 
3.5%
2 5
 
1.2%
3 7
 
1.7%
4 4
 
0.9%
5 2
 
0.5%
6 3
 
0.7%
7 3
 
0.7%
8 2
 
0.5%
9 2
 
0.5%
ValueCountFrequency (%)
78 1
 
0.2%
70 1
 
0.2%
54 1
 
0.2%
49 2
0.5%
30 1
 
0.2%
29 1
 
0.2%
25 1
 
0.2%
23 1
 
0.2%
20 3
0.7%
18 2
0.5%

20190630
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct34
Distinct (%)12.7%
Missing156
Missing (%)36.8%
Infinite0
Infinite (%)0.0%
Mean4.0783582
Minimum0
Maximum85
Zeros181
Zeros (%)42.7%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-12T08:18:43.887289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile23
Maximum85
Range85
Interquartile range (IQR)2

Descriptive statistics

Standard deviation10.856806
Coefficient of variation (CV)2.6620531
Kurtosis22.934034
Mean4.0783582
Median Absolute Deviation (MAD)0
Skewness4.3163712
Sum1093
Variance117.87024
MonotonicityNot monotonic
2023-12-12T08:18:44.044117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 181
42.7%
1 17
 
4.0%
2 9
 
2.1%
4 7
 
1.7%
3 6
 
1.4%
6 5
 
1.2%
7 3
 
0.7%
12 3
 
0.7%
8 3
 
0.7%
16 3
 
0.7%
Other values (24) 31
 
7.3%
(Missing) 156
36.8%
ValueCountFrequency (%)
0 181
42.7%
1 17
 
4.0%
2 9
 
2.1%
3 6
 
1.4%
4 7
 
1.7%
6 5
 
1.2%
7 3
 
0.7%
8 3
 
0.7%
9 1
 
0.2%
10 1
 
0.2%
ValueCountFrequency (%)
85 1
0.2%
76 1
0.2%
64 1
0.2%
45 1
0.2%
44 2
0.5%
35 1
0.2%
33 1
0.2%
29 1
0.2%
28 1
0.2%
26 1
0.2%

20190930
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct32
Distinct (%)10.4%
Missing116
Missing (%)27.4%
Infinite0
Infinite (%)0.0%
Mean3.6525974
Minimum0
Maximum56
Zeros195
Zeros (%)46.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-12T08:18:44.182669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile24.3
Maximum56
Range56
Interquartile range (IQR)2

Descriptive statistics

Standard deviation8.6949437
Coefficient of variation (CV)2.3804824
Kurtosis13.190288
Mean3.6525974
Median Absolute Deviation (MAD)0
Skewness3.4089038
Sum1125
Variance75.602045
MonotonicityNot monotonic
2023-12-12T08:18:44.336162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 195
46.0%
1 31
 
7.3%
2 9
 
2.1%
4 8
 
1.9%
6 7
 
1.7%
7 6
 
1.4%
3 6
 
1.4%
25 4
 
0.9%
5 3
 
0.7%
12 3
 
0.7%
Other values (22) 36
 
8.5%
(Missing) 116
27.4%
ValueCountFrequency (%)
0 195
46.0%
1 31
 
7.3%
2 9
 
2.1%
3 6
 
1.4%
4 8
 
1.9%
5 3
 
0.7%
6 7
 
1.7%
7 6
 
1.4%
8 2
 
0.5%
9 3
 
0.7%
ValueCountFrequency (%)
56 1
 
0.2%
55 1
 
0.2%
49 1
 
0.2%
45 1
 
0.2%
38 1
 
0.2%
35 1
 
0.2%
33 1
 
0.2%
30 1
 
0.2%
27 3
0.7%
26 1
 
0.2%

20191231
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct36
Distinct (%)10.5%
Missing80
Missing (%)18.9%
Infinite0
Infinite (%)0.0%
Mean4.6075581
Minimum0
Maximum135
Zeros215
Zeros (%)50.7%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-12T08:18:44.489775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile21.85
Maximum135
Range135
Interquartile range (IQR)3

Descriptive statistics

Standard deviation13.374404
Coefficient of variation (CV)2.9027099
Kurtosis45.999537
Mean4.6075581
Median Absolute Deviation (MAD)0
Skewness5.9128113
Sum1585
Variance178.87469
MonotonicityNot monotonic
2023-12-12T08:18:44.647651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 215
50.7%
1 29
 
6.8%
3 20
 
4.7%
4 9
 
2.1%
2 8
 
1.9%
16 6
 
1.4%
11 4
 
0.9%
6 4
 
0.9%
7 3
 
0.7%
5 3
 
0.7%
Other values (26) 43
 
10.1%
(Missing) 80
 
18.9%
ValueCountFrequency (%)
0 215
50.7%
1 29
 
6.8%
2 8
 
1.9%
3 20
 
4.7%
4 9
 
2.1%
5 3
 
0.7%
6 4
 
0.9%
7 3
 
0.7%
8 2
 
0.5%
9 3
 
0.7%
ValueCountFrequency (%)
135 1
0.2%
123 1
0.2%
59 1
0.2%
58 1
0.2%
55 2
0.5%
49 1
0.2%
47 1
0.2%
39 1
0.2%
38 1
0.2%
37 1
0.2%

20200331
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct53
Distinct (%)13.5%
Missing32
Missing (%)7.5%
Infinite0
Infinite (%)0.0%
Mean8.2346939
Minimum0
Maximum253
Zeros204
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-12T08:18:44.840209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile39.9
Maximum253
Range253
Interquartile range (IQR)5

Descriptive statistics

Standard deviation23.270807
Coefficient of variation (CV)2.8259468
Kurtosis50.723738
Mean8.2346939
Median Absolute Deviation (MAD)0
Skewness6.2075655
Sum3228
Variance541.53046
MonotonicityNot monotonic
2023-12-12T08:18:45.021096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 204
48.1%
1 34
 
8.0%
2 21
 
5.0%
4 14
 
3.3%
5 12
 
2.8%
3 11
 
2.6%
6 8
 
1.9%
9 5
 
1.2%
7 5
 
1.2%
13 5
 
1.2%
Other values (43) 73
 
17.2%
(Missing) 32
 
7.5%
ValueCountFrequency (%)
0 204
48.1%
1 34
 
8.0%
2 21
 
5.0%
3 11
 
2.6%
4 14
 
3.3%
5 12
 
2.8%
6 8
 
1.9%
7 5
 
1.2%
8 3
 
0.7%
9 5
 
1.2%
ValueCountFrequency (%)
253 1
0.2%
213 1
0.2%
143 1
0.2%
123 1
0.2%
102 1
0.2%
82 1
0.2%
79 1
0.2%
71 1
0.2%
70 1
0.2%
65 1
0.2%

20200630
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8089623
Minimum0
Maximum271
Zeros189
Zeros (%)44.6%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-12T08:18:45.207899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q310
95-th percentile43.7
Maximum271
Range271
Interquartile range (IQR)10

Descriptive statistics

Standard deviation23.365405
Coefficient of variation (CV)2.3820466
Kurtosis46.081745
Mean9.8089623
Median Absolute Deviation (MAD)1
Skewness5.6175989
Sum4159
Variance545.94214
MonotonicityNot monotonic
2023-12-12T08:18:45.382300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 189
44.6%
1 38
 
9.0%
2 21
 
5.0%
4 20
 
4.7%
3 13
 
3.1%
6 9
 
2.1%
8 9
 
2.1%
10 9
 
2.1%
5 7
 
1.7%
7 7
 
1.7%
Other values (47) 102
24.1%
ValueCountFrequency (%)
0 189
44.6%
1 38
 
9.0%
2 21
 
5.0%
3 13
 
3.1%
4 20
 
4.7%
5 7
 
1.7%
6 9
 
2.1%
7 7
 
1.7%
8 9
 
2.1%
9 4
 
0.9%
ValueCountFrequency (%)
271 1
0.2%
175 1
0.2%
126 1
0.2%
116 1
0.2%
114 1
0.2%
104 1
0.2%
93 1
0.2%
85 1
0.2%
82 1
0.2%
74 1
0.2%

Interactions

2023-12-12T08:18:39.329735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:26.051228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:27.470692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:28.605202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:30.128213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:31.458018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:32.687734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:33.712853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:34.763829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:35.944583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:37.105894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:38.289449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:39.426754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:26.152203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:27.562463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:28.704163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:30.253482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:31.569104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:32.800372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:33.819926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:34.845309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:36.040715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:37.221559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:38.406147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:39.505308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:26.315003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:27.643688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:28.818225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:30.362328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:31.678182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:32.899084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:33.893063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:34.917448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:36.110619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:37.325025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:38.484727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:39.583950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:26.423218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:27.748300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:29.181741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:30.452302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:31.810754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:32.986470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:33.971409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:34.999681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:36.209502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:37.411506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:38.555056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:39.669173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:26.548043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:27.856683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:29.295092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:30.548415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:31.931153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:33.063687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:34.075129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:35.338852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:36.311875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:37.495667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:38.638673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:39.777190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:26.681988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:27.950863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:29.419296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:30.655992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:32.040993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:33.138191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:34.176845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:35.423401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:36.411536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:37.578152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:38.719153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:39.852024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:26.809454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:28.065020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:29.512765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:30.772679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:32.122940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:33.216570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:34.268508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:35.495786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:36.499281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:37.662458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:38.796919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:39.938065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:26.933623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:28.168375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:29.633707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:30.915861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:32.225102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:33.315601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:34.356933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:35.572186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:36.589293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:37.768180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:38.903954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:40.007224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:27.020555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:28.258062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:29.711364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:31.014939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:32.309566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:33.386482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:34.430027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:35.633927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:36.666337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:37.892563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:38.997339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:40.087358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:27.116858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:28.351271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:29.808107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:31.111108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:32.409609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:33.462345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:34.515393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:35.704798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:36.790569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:37.981758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:39.075294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:40.165778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:27.233762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:28.441655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:29.904875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:31.240829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:32.495368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:33.550074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:34.603686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:35.790492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:36.898029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:38.081738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:39.169245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:40.237609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:27.338773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:28.516040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:29.989792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:31.362419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:32.590065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:33.640707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:34.686219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:35.866729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:37.001281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:38.182597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:39.251988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:18:45.493761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공급계층대상주택수2017123120180331201806302018093020181231201903312019063020190930201912312020033120200630
공급계층1.0000.3820.0000.2190.0000.1420.3160.1450.1670.1290.0990.1770.016
대상주택수0.3821.0000.0760.0000.0000.6330.3400.1210.3730.0000.3190.6590.642
201712310.0000.0761.0000.9040.7220.8360.6330.7050.5440.3280.1780.0000.147
201803310.2190.0000.9041.0000.7390.8970.8150.3970.5190.3340.0850.0000.000
201806300.0000.0000.7220.7391.0000.6820.4480.0000.2080.2380.0000.0000.000
201809300.1420.6330.8360.8970.6821.0000.8110.6040.6350.4770.5090.0000.327
201812310.3160.3400.6330.8150.4480.8111.0000.8170.6380.5230.3810.2650.076
201903310.1450.1210.7050.3970.0000.6040.8171.0000.8490.7230.4370.3760.573
201906300.1670.3730.5440.5190.2080.6350.6380.8491.0000.8940.6930.3950.715
201909300.1290.0000.3280.3340.2380.4770.5230.7230.8941.0000.7770.3550.373
201912310.0990.3190.1780.0850.0000.5090.3810.4370.6930.7771.0000.7620.666
202003310.1770.6590.0000.0000.0000.0000.2650.3760.3950.3550.7621.0000.911
202006300.0160.6420.1470.0000.0000.3270.0760.5730.7150.3730.6660.9111.000
2023-12-12T08:18:45.671447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대상주택수2017123120180331201806302018093020181231201903312019063020190930201912312020033120200630공급계층
대상주택수1.0000.148-0.003-0.0640.0410.0450.2450.2610.1280.2380.4180.4620.235
201712310.1481.0000.8980.3230.2490.1900.1650.2020.1540.2200.2700.2760.000
20180331-0.0030.8981.0000.3720.2300.1430.1250.1390.0760.1490.2230.2360.086
20180630-0.0640.3230.3721.0000.4070.1660.1460.1720.1950.2010.2090.1180.000
201809300.0410.2490.2300.4071.0000.7080.5520.4470.3540.4010.3730.2800.086
201812310.0450.1900.1430.1660.7081.0000.7890.4570.1960.2050.1910.1680.143
201903310.2450.1650.1250.1460.5520.7891.0000.7140.3810.2680.3180.3280.099
201906300.2610.2020.1390.1720.4470.4570.7141.0000.5910.4330.3400.3680.100
201909300.1280.1540.0760.1950.3540.1960.3810.5911.0000.6510.4320.3410.082
201912310.2380.2200.1490.2010.4010.2050.2680.4330.6511.0000.6780.4850.068
202003310.4180.2700.2230.2090.3730.1910.3180.3400.4320.6781.0000.7400.079
202006300.4620.2760.2360.1180.2800.1680.3280.3680.3410.4850.7401.0000.009
공급계층0.2350.0000.0860.0000.0860.1430.0990.1000.0820.0680.0790.0091.000

Missing values

2023-12-12T08:18:40.359632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:18:40.532737image/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.
2023-12-12T08:18:40.899847image/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

단지명공급계층대상주택수201612312017123120180331201806302018093020181231201903312019063020190930201912312020033120200630
0서울삼전 1단지고령자4<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>0
1서울삼전 1단지대학생5<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>0
2서울삼전 1단지신혼부부 한부모가족17<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1
3서울삼전 1단지청년 계층11<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>0
4서울가좌 행복주택고령자36<NA>00000000111
5서울가좌 행복주택대학생223<NA>00000110000
6서울가좌 행복주택신혼부부 한부모가족25<NA>00000002000
7서울가좌 행복주택청년 계층42<NA>00000000000
8인천주안역 행복주택고령자14<NA>00000000011
9인천주안역 행복주택대학생56<NA>00000000001
단지명공급계층대상주택수201612312017123120180331201806302018093020181231201903312019063020190930201912312020033120200630
414파주법원 행복주택신혼부부 한부모가족24<NA><NA><NA><NA><NA><NA><NA><NA><NA>000
415파주법원 행복주택청년 계층35<NA><NA><NA><NA><NA><NA><NA><NA><NA>000
416군산신역세권 A-2BL (군산내흥7)고령자36<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>0
417군산신역세권 A-2BL (군산내흥7)대학생14<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>0
418군산신역세권 A-2BL (군산내흥7)신혼부부 한부모가족188<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>0
419군산신역세권 A-2BL (군산내흥7)청년 계층126<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>0
420인천영종 A-49블록 행복주택고령자65<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>00
421인천영종 A-49블록 행복주택대학생53<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>00
422인천영종 A-49블록 행복주택신혼부부 한부모가족125<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>00
423인천영종 A-49블록 행복주택청년 계층182<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>00