Overview

Dataset statistics

Number of variables7
Number of observations1861
Missing cells30
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory105.5 KiB
Average record size in memory58.1 B

Variable types

Categorical1
Text4
Numeric2

Dataset

Description강원특별자치도 공동주택 정보(공동주택명, 소재지도로명주소, 동수, 층수, 세대수, 사용승인일 등) 데이터를 제공합니다.
Author강원특별자치도
URLhttps://www.data.go.kr/data/15033675/fileData.do

Alerts

동수 is highly overall correlated with 세대수High correlation
세대수 is highly overall correlated with 동수High correlation
공동주택명 has 21 (1.1%) missing valuesMissing

Reproduction

Analysis started2024-03-15 01:16:46.943003
Analysis finished2024-03-15 01:16:50.085657
Duration3.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구명
Categorical

Distinct18
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size14.7 KiB
원주시
280 
강릉시
249 
춘천시
227 
삼척시
146 
속초시
124 
Other values (13)
835 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
원주시 280
15.0%
강릉시 249
13.4%
춘천시 227
12.2%
삼척시 146
7.8%
속초시 124
6.7%
동해시 119
6.4%
홍천군 117
 
6.3%
평창군 109
 
5.9%
태백시 109
 
5.9%
철원군 87
 
4.7%
Other values (8) 294
15.8%

Length

2024-03-15T10:16:50.309397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
원주시 280
15.0%
강릉시 249
13.4%
춘천시 227
12.2%
삼척시 146
7.8%
속초시 124
6.7%
동해시 119
6.4%
홍천군 117
 
6.3%
평창군 109
 
5.9%
태백시 109
 
5.9%
철원군 87
 
4.7%
Other values (8) 294
15.8%

공동주택명
Text

MISSING 

Distinct1726
Distinct (%)93.8%
Missing21
Missing (%)1.1%
Memory size14.7 KiB
2024-03-15T10:16:51.236171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length6.1402174
Min length2

Characters and Unicode

Total characters11298
Distinct characters443
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1638 ?
Unique (%)89.0%

Sample

1st row공무원 (A동)
2nd row봉의
3rd row소망
4th row에리트
5th row소양
ValueCountFrequency (%)
아파트 16
 
0.7%
속초 14
 
0.7%
코아루 10
 
0.5%
2차 10
 
0.5%
롯데캐슬 9
 
0.4%
1차 8
 
0.4%
부영 8
 
0.4%
원주 7
 
0.3%
가동 7
 
0.3%
동해 7
 
0.3%
Other values (1781) 2039
95.5%
2024-03-15T10:16:52.626752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 576
 
5.1%
트 530
 
4.7%
파 522
 
4.6%
차 332
 
2.9%
295
 
2.6%
동 269
 
2.4%
주 226
 
2.0%
연 225
 
2.0%
립 219
 
1.9%
빌 219
 
1.9%
Other values (433) 7885
69.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9976
88.3%
Decimal Number 579
 
5.1%
Space Separator 295
 
2.6%
Open Punctuation 130
 
1.2%
Close Punctuation 130
 
1.2%
Uppercase Letter 116
 
1.0%
Other Punctuation 29
 
0.3%
Lowercase Letter 24
 
0.2%
Dash Punctuation 8
 
0.1%
Final Punctuation 7
 
0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 576
 
5.8%
트 530
 
5.3%
파 522
 
5.2%
차 332
 
3.3%
동 269
 
2.7%
주 226
 
2.3%
연 225
 
2.3%
립 219
 
2.2%
빌 219
 
2.2%
대 203
 
2.0%
Other values (384) 6655
66.7%
Uppercase Letter
ValueCountFrequency (%)
L 23
19.8%
B 22
19.0%
A 19
16.4%
H 18
15.5%
C 9
 
7.8%
K 4
 
3.4%
S 4
 
3.4%
E 4
 
3.4%
I 2
 
1.7%
P 2
 
1.7%
Other values (8) 9
 
7.8%
Decimal Number
ValueCountFrequency (%)
2 165
28.5%
1 157
27.1%
3 80
13.8%
4 52
 
9.0%
8 45
 
7.8%
5 29
 
5.0%
6 19
 
3.3%
0 14
 
2.4%
7 13
 
2.2%
9 5
 
0.9%
Lowercase Letter
ValueCountFrequency (%)
e 10
41.7%
t 4
 
16.7%
l 2
 
8.3%
k 2
 
8.3%
h 2
 
8.3%
a 1
 
4.2%
o 1
 
4.2%
i 1
 
4.2%
r 1
 
4.2%
Other Punctuation
ValueCountFrequency (%)
, 14
48.3%
. 6
20.7%
/ 6
20.7%
: 2
 
6.9%
' 1
 
3.4%
Space Separator
ValueCountFrequency (%)
295
100.0%
Open Punctuation
ValueCountFrequency (%)
( 130
100.0%
Close Punctuation
ValueCountFrequency (%)
) 130
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 7
100.0%
Format
ValueCountFrequency (%)
­ 2
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9978
88.3%
Common 1180
 
10.4%
Latin 140
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 576
 
5.8%
트 530
 
5.3%
파 522
 
5.2%
차 332
 
3.3%
동 269
 
2.7%
주 226
 
2.3%
연 225
 
2.3%
립 219
 
2.2%
빌 219
 
2.2%
대 203
 
2.0%
Other values (385) 6657
66.7%
Latin
ValueCountFrequency (%)
L 23
16.4%
B 22
15.7%
A 19
13.6%
H 18
12.9%
e 10
7.1%
C 9
 
6.4%
K 4
 
2.9%
t 4
 
2.9%
S 4
 
2.9%
E 4
 
2.9%
Other values (17) 23
16.4%
Common
ValueCountFrequency (%)
295
25.0%
2 165
14.0%
1 157
13.3%
( 130
11.0%
) 130
11.0%
3 80
 
6.8%
4 52
 
4.4%
8 45
 
3.8%
5 29
 
2.5%
6 19
 
1.6%
Other values (11) 78
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9976
88.3%
ASCII 1311
 
11.6%
Punctuation 7
 
0.1%
None 4
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 576
 
5.8%
트 530
 
5.3%
파 522
 
5.2%
차 332
 
3.3%
동 269
 
2.7%
주 226
 
2.3%
연 225
 
2.3%
립 219
 
2.2%
빌 219
 
2.2%
대 203
 
2.0%
Other values (384) 6655
66.7%
ASCII
ValueCountFrequency (%)
295
22.5%
2 165
12.6%
1 157
12.0%
( 130
9.9%
) 130
9.9%
3 80
 
6.1%
4 52
 
4.0%
8 45
 
3.4%
5 29
 
2.2%
L 23
 
1.8%
Other values (36) 205
15.6%
Punctuation
ValueCountFrequency (%)
’ 7
100.0%
None
ValueCountFrequency (%)
­ 2
50.0%
㈜ 2
50.0%

소재지도로명주소
Text

Distinct1803
Distinct (%)97.0%
Missing3
Missing (%)0.2%
Memory size14.7 KiB
2024-03-15T10:16:53.813624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length21
Mean length10.310011
Min length4

Characters and Unicode

Total characters19156
Distinct characters342
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

Unique1757 ?
Unique (%)94.6%

Sample

1st row공지로 361
2nd row후석로 441-40
3rd row후만로 39
4th row삭주로 89-7
5th row충열로 80
ValueCountFrequency (%)
홍천읍 96
 
2.1%
영월읍 57
 
1.3%
동송읍 37
 
0.8%
15 36
 
0.8%
대관령면 36
 
0.8%
양양읍 35
 
0.8%
갈말읍 35
 
0.8%
횡성읍 34
 
0.8%
7 30
 
0.7%
10 28
 
0.6%
Other values (1883) 4099
90.6%
2024-03-15T10:16:55.350592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2721
 
14.2%
1 1327
 
6.9%
로 1121
 
5.9%
길 1056
 
5.5%
2 954
 
5.0%
3 729
 
3.8%
4 595
 
3.1%
5 531
 
2.8%
읍 494
 
2.6%
- 454
 
2.4%
Other values (332) 9174
47.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9716
50.7%
Decimal Number 6132
32.0%
Space Separator 2721
 
14.2%
Dash Punctuation 454
 
2.4%
Close Punctuation 61
 
0.3%
Open Punctuation 59
 
0.3%
Other Punctuation 7
 
< 0.1%
Uppercase Letter 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 1121
 
11.5%
길 1056
 
10.9%
읍 494
 
5.1%
번 380
 
3.9%
면 254
 
2.6%
천 218
 
2.2%
동 169
 
1.7%
대 164
 
1.7%
양 153
 
1.6%
원 145
 
1.5%
Other values (311) 5562
57.2%
Decimal Number
ValueCountFrequency (%)
1 1327
21.6%
2 954
15.6%
3 729
11.9%
4 595
9.7%
5 531
8.7%
6 431
 
7.0%
7 426
 
6.9%
0 404
 
6.6%
8 371
 
6.1%
9 364
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
B 1
16.7%
V 1
16.7%
P 1
16.7%
I 1
16.7%
C 1
16.7%
A 1
16.7%
Space Separator
ValueCountFrequency (%)
2721
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 454
100.0%
Close Punctuation
ValueCountFrequency (%)
) 61
100.0%
Open Punctuation
ValueCountFrequency (%)
( 59
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9716
50.7%
Common 9434
49.2%
Latin 6
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 1121
 
11.5%
길 1056
 
10.9%
읍 494
 
5.1%
번 380
 
3.9%
면 254
 
2.6%
천 218
 
2.2%
동 169
 
1.7%
대 164
 
1.7%
양 153
 
1.6%
원 145
 
1.5%
Other values (311) 5562
57.2%
Common
ValueCountFrequency (%)
2721
28.8%
1 1327
14.1%
2 954
 
10.1%
3 729
 
7.7%
4 595
 
6.3%
5 531
 
5.6%
- 454
 
4.8%
6 431
 
4.6%
7 426
 
4.5%
0 404
 
4.3%
Other values (5) 862
 
9.1%
Latin
ValueCountFrequency (%)
B 1
16.7%
V 1
16.7%
P 1
16.7%
I 1
16.7%
C 1
16.7%
A 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9716
50.7%
ASCII 9440
49.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2721
28.8%
1 1327
14.1%
2 954
 
10.1%
3 729
 
7.7%
4 595
 
6.3%
5 531
 
5.6%
- 454
 
4.8%
6 431
 
4.6%
7 426
 
4.5%
0 404
 
4.3%
Other values (11) 868
 
9.2%
Hangul
ValueCountFrequency (%)
로 1121
 
11.5%
길 1056
 
10.9%
읍 494
 
5.1%
번 380
 
3.9%
면 254
 
2.6%
천 218
 
2.2%
동 169
 
1.7%
대 164
 
1.7%
양 153
 
1.6%
원 145
 
1.5%
Other values (311) 5562
57.2%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4476088
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2024-03-15T10:16:55.598038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile11
Maximum40
Range39
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.5775691
Coefficient of variation (CV)1.0376958
Kurtosis11.129044
Mean3.4476088
Median Absolute Deviation (MAD)1
Skewness2.576697
Sum6416
Variance12.799001
MonotonicityNot monotonic
2024-03-15T10:16:56.017616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 775
41.6%
2 293
 
15.7%
3 177
 
9.5%
4 129
 
6.9%
5 119
 
6.4%
6 82
 
4.4%
7 76
 
4.1%
8 49
 
2.6%
9 34
 
1.8%
10 32
 
1.7%
Other values (15) 95
 
5.1%
ValueCountFrequency (%)
1 775
41.6%
2 293
 
15.7%
3 177
 
9.5%
4 129
 
6.9%
5 119
 
6.4%
6 82
 
4.4%
7 76
 
4.1%
8 49
 
2.6%
9 34
 
1.8%
10 32
 
1.7%
ValueCountFrequency (%)
40 1
 
0.1%
28 1
 
0.1%
26 1
 
0.1%
22 2
 
0.1%
21 2
 
0.1%
20 1
 
0.1%
19 4
0.2%
18 2
 
0.1%
17 8
0.4%
16 7
0.4%

층수
Text

Distinct58
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size14.7 KiB
2024-03-15T10:16:56.738265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.4755508
Min length1

Characters and Unicode

Total characters2746
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)1.3%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5
ValueCountFrequency (%)
5 377
20.3%
15 372
20.0%
3 284
15.3%
4 215
11.6%
20 79
 
4.2%
10 63
 
3.4%
6 55
 
3.0%
18 48
 
2.6%
13 42
 
2.3%
14 39
 
2.1%
Other values (48) 287
15.4%
2024-03-15T10:16:57.777000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 774
28.2%
1 692
25.2%
3 355
12.9%
4 266
 
9.7%
2 243
 
8.8%
0 152
 
5.5%
8 85
 
3.1%
6 67
 
2.4%
9 58
 
2.1%
7 35
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2727
99.3%
Math Symbol 19
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 774
28.4%
1 692
25.4%
3 355
13.0%
4 266
 
9.8%
2 243
 
8.9%
0 152
 
5.6%
8 85
 
3.1%
6 67
 
2.5%
9 58
 
2.1%
7 35
 
1.3%
Math Symbol
ValueCountFrequency (%)
~ 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2746
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 774
28.2%
1 692
25.2%
3 355
12.9%
4 266
 
9.7%
2 243
 
8.8%
0 152
 
5.5%
8 85
 
3.1%
6 67
 
2.4%
9 58
 
2.1%
7 35
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2746
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 774
28.2%
1 692
25.2%
3 355
12.9%
4 266
 
9.7%
2 243
 
8.8%
0 152
 
5.5%
8 85
 
3.1%
6 67
 
2.4%
9 58
 
2.1%
7 35
 
1.3%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct550
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.42558
Minimum0
Maximum2835
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2024-03-15T10:16:58.143893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q124
median93
Q3300
95-th percentile747
Maximum2835
Range2835
Interquartile range (IQR)276

Descriptive statistics

Standard deviation276.2086
Coefficient of variation (CV)1.3002606
Kurtosis10.910488
Mean212.42558
Median Absolute Deviation (MAD)77
Skewness2.5617962
Sum395324
Variance76291.193
MonotonicityNot monotonic
2024-03-15T10:16:58.520193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 76
 
4.1%
24 61
 
3.3%
18 57
 
3.1%
16 57
 
3.1%
30 55
 
3.0%
12 46
 
2.5%
20 38
 
2.0%
50 33
 
1.8%
15 29
 
1.6%
40 27
 
1.5%
Other values (540) 1382
74.3%
ValueCountFrequency (%)
0 1
 
0.1%
4 2
 
0.1%
5 1
 
0.1%
6 6
 
0.3%
7 7
 
0.4%
8 23
1.2%
9 14
 
0.8%
10 8
 
0.4%
11 6
 
0.3%
12 46
2.5%
ValueCountFrequency (%)
2835 1
0.1%
2305 1
0.1%
2180 1
0.1%
1792 1
0.1%
1745 1
0.1%
1728 1
0.1%
1620 1
0.1%
1556 1
0.1%
1538 1
0.1%
1431 1
0.1%

사용승인일
Text

Distinct1587
Distinct (%)85.6%
Missing6
Missing (%)0.3%
Memory size14.7 KiB
2024-03-15T10:16:59.482497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9978437
Min length6

Characters and Unicode

Total characters18546
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1367 ?
Unique (%)73.7%

Sample

1st row1971-11-16
2nd row1976-12-20
3rd row1980-05-20
4th row1981-05-29
5th row1982-05-23
ValueCountFrequency (%)
1983-12-30 12
 
0.6%
2003-04-25 5
 
0.3%
1984-12-30 5
 
0.3%
1991-10-19 5
 
0.3%
1985-12-17 4
 
0.2%
1990-04-28 4
 
0.2%
1991-12-30 3
 
0.2%
1992-11-17 3
 
0.2%
1995-01-14 3
 
0.2%
1982-11-30 3
 
0.2%
Other values (1577) 1808
97.5%
2024-03-15T10:17:01.044811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 3708
20.0%
1 3431
18.5%
0 3115
16.8%
9 2388
12.9%
2 2277
12.3%
8 979
 
5.3%
3 644
 
3.5%
4 506
 
2.7%
7 500
 
2.7%
5 499
 
2.7%
Other values (6) 499
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14832
80.0%
Dash Punctuation 3708
 
20.0%
Other Letter 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3431
23.1%
0 3115
21.0%
9 2388
16.1%
2 2277
15.4%
8 979
 
6.6%
3 644
 
4.3%
4 506
 
3.4%
7 500
 
3.4%
5 499
 
3.4%
6 493
 
3.3%
Other Letter
ValueCountFrequency (%)
사 2
33.3%
동 1
16.7%
별 1
16.7%
용 1
16.7%
검 1
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 3708
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18540
> 99.9%
Hangul 6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
- 3708
20.0%
1 3431
18.5%
0 3115
16.8%
9 2388
12.9%
2 2277
12.3%
8 979
 
5.3%
3 644
 
3.5%
4 506
 
2.7%
7 500
 
2.7%
5 499
 
2.7%
Hangul
ValueCountFrequency (%)
사 2
33.3%
동 1
16.7%
별 1
16.7%
용 1
16.7%
검 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18540
> 99.9%
Hangul 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 3708
20.0%
1 3431
18.5%
0 3115
16.8%
9 2388
12.9%
2 2277
12.3%
8 979
 
5.3%
3 644
 
3.5%
4 506
 
2.7%
7 500
 
2.7%
5 499
 
2.7%
Hangul
ValueCountFrequency (%)
사 2
33.3%
동 1
16.7%
별 1
16.7%
용 1
16.7%
검 1
16.7%

Interactions

2024-03-15T10:16:48.534677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:16:47.968961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:16:48.810219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:16:48.270554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T10:17:01.317913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명동수층수세대수
시군구명1.0000.2800.4080.327
동수0.2801.0000.6850.744
층수0.4080.6851.0000.781
세대수0.3270.7440.7811.000
2024-03-15T10:17:01.568473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동수세대수시군구명
동수1.0000.7870.121
세대수0.7871.0000.131
시군구명0.1210.1311.000

Missing values

2024-03-15T10:16:49.158173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T10:16:49.633314image/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.
2024-03-15T10:16:49.941257image/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

시군구명공동주택명소재지도로명주소동수층수세대수사용승인일
0춘천시공무원 (A동)공지로 36115301971-11-16
1춘천시봉의후석로 441-40652001976-12-20
2춘천시소망후만로 3915401980-05-20
3춘천시에리트삭주로 89-7852601981-05-29
4춘천시소양충열로 80252521982-05-23
5춘천시세경1차백령로 202(후평동)451901984-12-12
6춘천시후평주공4차후만로98번길 91757081985-10-19
7춘천시세경2차춘천로281번길 36-7452001986-04-24
8춘천시개나리수풍골길13번길 2725901988-06-22
9춘천시동부시장 현대아파트동부시장길 816471988-09-19
시군구명공동주택명소재지도로명주소동수층수세대수사용승인일
1851양양군남애리조트빌라현남면 남애리 454-113181992-08-25
1852양양군동아빌라양양읍 연창리 109-114161992-12-30
1853양양군대성그린빌라양양읍 구교리 202-714161995-04-17
1854양양군태산연립양양읍 구교리 203-214151995-05-10
1855양양군그린아트빌라양양읍 구교리 202-81461995-06-30
1856양양군하조대빌라현북면 하광정리 12413181995-06-30
1857양양군금성빌라양양읍 연창리 23624181997-02-05
1858양양군태화그린맨션양양읍 서문리 268-214191997-09-02
1859양양군신풍힐타운양양읍 구교리 203-115122012-11-19
1860양양군맥산하이츠빌양양읍 구교리 102-116152019-05-23