Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells7
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory566.4 KiB
Average record size in memory58.0 B

Variable types

Text3
Categorical1
Numeric2

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15819/S/1/datasetView.do

Alerts

금액 is highly skewed (γ1 = 59.29922898)Skewed

Reproduction

Analysis started2024-05-11 02:38:03.619360
Analysis finished2024-05-11 02:38:08.296441
Duration4.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

Distinct2068
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:38:08.794249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length7.2439
Min length2

Characters and Unicode

Total characters72439
Distinct characters428
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique176 ?
Unique (%)1.8%

Sample

1st row역삼아이파크
2nd row당산롯데캐슬프레스티지 아파트
3rd row옥수삼성
4th row가양3단지(강변)
5th row뚝섬중앙하이츠빌
ValueCountFrequency (%)
아파트 121
 
1.2%
래미안 26
 
0.2%
신림현대 21
 
0.2%
북한산 20
 
0.2%
마포펜트라우스 20
 
0.2%
힐스테이트 18
 
0.2%
개봉동현대아파트 18
 
0.2%
목동2단지 17
 
0.2%
중계그린 16
 
0.2%
목동14단지 16
 
0.2%
Other values (2124) 10227
97.2%
2024-05-11T02:38:10.296535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
파 2307
 
3.2%
아 2238
 
3.1%
지 2125
 
2.9%
트 2093
 
2.9%
대 1754
 
2.4%
단 1677
 
2.3%
동 1498
 
2.1%
신 1399
 
1.9%
차 1399
 
1.9%
이 1283
 
1.8%
Other values (418) 54666
75.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66366
91.6%
Decimal Number 3928
 
5.4%
Uppercase Letter 757
 
1.0%
Space Separator 584
 
0.8%
Lowercase Letter 275
 
0.4%
Open Punctuation 138
 
0.2%
Close Punctuation 138
 
0.2%
Dash Punctuation 123
 
0.2%
Other Punctuation 116
 
0.2%
Math Symbol 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
파 2307
 
3.5%
아 2238
 
3.4%
지 2125
 
3.2%
트 2093
 
3.2%
대 1754
 
2.6%
단 1677
 
2.5%
동 1498
 
2.3%
신 1399
 
2.1%
차 1399
 
2.1%
이 1283
 
1.9%
Other values (372) 48593
73.2%
Uppercase Letter
ValueCountFrequency (%)
S 152
20.1%
K 108
14.3%
C 72
9.5%
H 63
8.3%
L 56
 
7.4%
D 42
 
5.5%
M 42
 
5.5%
E 42
 
5.5%
I 37
 
4.9%
A 35
 
4.6%
Other values (7) 108
14.3%
Lowercase Letter
ValueCountFrequency (%)
e 179
65.1%
l 28
 
10.2%
i 24
 
8.7%
v 16
 
5.8%
w 7
 
2.5%
s 6
 
2.2%
k 5
 
1.8%
a 3
 
1.1%
g 3
 
1.1%
c 2
 
0.7%
Decimal Number
ValueCountFrequency (%)
1 1168
29.7%
2 1111
28.3%
3 518
13.2%
4 283
 
7.2%
5 238
 
6.1%
6 164
 
4.2%
7 138
 
3.5%
9 126
 
3.2%
8 104
 
2.6%
0 78
 
2.0%
Other Punctuation
ValueCountFrequency (%)
, 101
87.1%
. 15
 
12.9%
Space Separator
ValueCountFrequency (%)
584
100.0%
Open Punctuation
ValueCountFrequency (%)
( 138
100.0%
Close Punctuation
ValueCountFrequency (%)
) 138
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 123
100.0%
Math Symbol
ValueCountFrequency (%)
~ 9
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66366
91.6%
Common 5036
 
7.0%
Latin 1037
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
파 2307
 
3.5%
아 2238
 
3.4%
지 2125
 
3.2%
트 2093
 
3.2%
대 1754
 
2.6%
단 1677
 
2.5%
동 1498
 
2.3%
신 1399
 
2.1%
차 1399
 
2.1%
이 1283
 
1.9%
Other values (372) 48593
73.2%
Latin
ValueCountFrequency (%)
e 179
17.3%
S 152
14.7%
K 108
10.4%
C 72
 
6.9%
H 63
 
6.1%
L 56
 
5.4%
D 42
 
4.1%
M 42
 
4.1%
E 42
 
4.1%
I 37
 
3.6%
Other values (19) 244
23.5%
Common
ValueCountFrequency (%)
1 1168
23.2%
2 1111
22.1%
584
11.6%
3 518
10.3%
4 283
 
5.6%
5 238
 
4.7%
6 164
 
3.3%
( 138
 
2.7%
7 138
 
2.7%
) 138
 
2.7%
Other values (7) 556
11.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66366
91.6%
ASCII 6068
 
8.4%
Number Forms 5
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
파 2307
 
3.5%
아 2238
 
3.4%
지 2125
 
3.2%
트 2093
 
3.2%
대 1754
 
2.6%
단 1677
 
2.5%
동 1498
 
2.3%
신 1399
 
2.1%
차 1399
 
2.1%
이 1283
 
1.9%
Other values (372) 48593
73.2%
ASCII
ValueCountFrequency (%)
1 1168
19.2%
2 1111
18.3%
584
 
9.6%
3 518
 
8.5%
4 283
 
4.7%
5 238
 
3.9%
e 179
 
2.9%
6 164
 
2.7%
S 152
 
2.5%
( 138
 
2.3%
Other values (35) 1533
25.3%
Number Forms
ValueCountFrequency (%)
Ⅰ 5
100.0%

아파트코드
Text

Distinct2074
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:38:11.656453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters90000
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique176 ?
Unique (%)1.8%

Sample

1st rowA13508009
2nd rowA10026797
3rd rowA13375902
4th rowA15780704
5th rowA13384303
ValueCountFrequency (%)
a15101508 21
 
0.2%
a12179004 20
 
0.2%
a15209207 18
 
0.2%
a15875102 17
 
0.2%
a10027207 16
 
0.2%
a15603206 16
 
0.2%
a13583507 16
 
0.2%
a13986306 16
 
0.2%
a15609306 16
 
0.2%
a15679109 16
 
0.2%
Other values (2064) 9828
98.3%
2024-05-11T02:38:13.215391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18388
20.4%
1 17272
19.2%
A 9993
11.1%
3 8761
9.7%
2 8155
9.1%
5 6342
 
7.0%
8 5744
 
6.4%
7 5008
 
5.6%
4 3826
 
4.3%
6 3452
 
3.8%
Other values (2) 3059
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80000
88.9%
Uppercase Letter 10000
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18388
23.0%
1 17272
21.6%
3 8761
11.0%
2 8155
10.2%
5 6342
 
7.9%
8 5744
 
7.2%
7 5008
 
6.3%
4 3826
 
4.8%
6 3452
 
4.3%
9 3052
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
A 9993
99.9%
B 7
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 80000
88.9%
Latin 10000
 
11.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18388
23.0%
1 17272
21.6%
3 8761
11.0%
2 8155
10.2%
5 6342
 
7.9%
8 5744
 
7.2%
7 5008
 
6.3%
4 3826
 
4.8%
6 3452
 
4.3%
9 3052
 
3.8%
Latin
ValueCountFrequency (%)
A 9993
99.9%
B 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18388
20.4%
1 17272
19.2%
A 9993
11.1%
3 8761
9.7%
2 8155
9.1%
5 6342
 
7.0%
8 5744
 
6.4%
7 5008
 
5.6%
4 3826
 
4.3%
6 3452
 
3.8%
Other values (2) 3059
 
3.4%

비용명
Categorical

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
연체료수익
3522 
광고료수익
942 
잡수익
898 
승강기수익
876 
주차장수익
798 
Other values (10)
2964 

Length

Max length9
Median length5
Mean length4.989
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row연체료수익
2nd row승강기수익
3rd row광고료수익
4th row연체료수익
5th row잡수익

Common Values

ValueCountFrequency (%)
연체료수익 3522
35.2%
광고료수익 942
 
9.4%
잡수익 898
 
9.0%
승강기수익 876
 
8.8%
주차장수익 798
 
8.0%
기타운영수익 719
 
7.2%
이자수익 607
 
6.1%
고용안정사업수익 443
 
4.4%
검침수익 277
 
2.8%
임대료수익 229
 
2.3%
Other values (5) 689
 
6.9%

Length

2024-05-11T02:38:13.886202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
연체료수익 3522
35.2%
광고료수익 942
 
9.4%
잡수익 898
 
9.0%
승강기수익 876
 
8.8%
주차장수익 798
 
8.0%
기타운영수익 719
 
7.2%
이자수익 607
 
6.1%
고용안정사업수익 443
 
4.4%
검침수익 277
 
2.8%
임대료수익 229
 
2.3%
Other values (5) 689
 
6.9%

년월일
Real number (ℝ)

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190917
Minimum20190901
Maximum20190930
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:38:14.415910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190901
5-th percentile20190902
Q120190909
median20190918
Q320190925
95-th percentile20190930
Maximum20190930
Range29
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.4921863
Coefficient of variation (CV)4.7012161 × 10-7
Kurtosis-1.2962502
Mean20190917
Median Absolute Deviation (MAD)8
Skewness-0.23418929
Sum2.0190917 × 1011
Variance90.101601
MonotonicityNot monotonic
2024-05-11T02:38:14.959222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20190930 1093
 
10.9%
20190902 626
 
6.3%
20190927 520
 
5.2%
20190925 500
 
5.0%
20190917 455
 
4.5%
20190911 450
 
4.5%
20190916 447
 
4.5%
20190910 438
 
4.4%
20190903 429
 
4.3%
20190923 413
 
4.1%
Other values (20) 4629
46.3%
ValueCountFrequency (%)
20190901 242
 
2.4%
20190902 626
6.3%
20190903 429
4.3%
20190904 316
3.2%
20190905 360
3.6%
20190906 282
2.8%
20190907 74
 
0.7%
20190908 71
 
0.7%
20190909 292
2.9%
20190910 438
4.4%
ValueCountFrequency (%)
20190930 1093
10.9%
20190929 243
 
2.4%
20190928 151
 
1.5%
20190927 520
5.2%
20190926 398
 
4.0%
20190925 500
5.0%
20190924 392
 
3.9%
20190923 413
 
4.1%
20190922 100
 
1.0%
20190921 361
 
3.6%

금액
Real number (ℝ)

SKEWED 

Distinct3624
Distinct (%)36.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean283481.42
Minimum-3878499
Maximum2.28 × 108
Zeros15
Zeros (%)0.1%
Negative35
Negative (%)0.4%
Memory size166.0 KiB
2024-05-11T02:38:15.525957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3878499
5-th percentile190.95
Q13070
median30000
Q3100000
95-th percentile1018385.9
Maximum2.28 × 108
Range2.318785 × 108
Interquartile range (IQR)96930

Descriptive statistics

Standard deviation2809149.6
Coefficient of variation (CV)9.9094665
Kurtosis4475.5399
Mean283481.42
Median Absolute Deviation (MAD)28864.5
Skewness59.299229
Sum2.8348142 × 109
Variance7.8913214 × 1012
MonotonicityNot monotonic
2024-05-11T02:38:16.249015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30000 569
 
5.7%
50000 556
 
5.6%
100000 440
 
4.4%
70000 151
 
1.5%
60000 138
 
1.4%
20000 131
 
1.3%
40000 127
 
1.3%
150000 123
 
1.2%
80000 114
 
1.1%
200000 102
 
1.0%
Other values (3614) 7549
75.5%
ValueCountFrequency (%)
-3878499 1
< 0.1%
-900000 1
< 0.1%
-627000 1
< 0.1%
-600000 1
< 0.1%
-584000 1
< 0.1%
-480000 1
< 0.1%
-396000 1
< 0.1%
-370000 1
< 0.1%
-339680 1
< 0.1%
-300000 1
< 0.1%
ValueCountFrequency (%)
228000000 1
< 0.1%
92727273 1
< 0.1%
65520000 1
< 0.1%
35916860 1
< 0.1%
33250000 1
< 0.1%
31370080 1
< 0.1%
30000000 1
< 0.1%
27596480 1
< 0.1%
25737950 1
< 0.1%
21968000 1
< 0.1%

내용
Text

Distinct5821
Distinct (%)58.3%
Missing7
Missing (%)0.1%
Memory size156.2 KiB
2024-05-11T02:38:17.111102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length74
Median length65
Mean length13.546683
Min length2

Characters and Unicode

Total characters135372
Distinct characters738
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5576 ?
Unique (%)55.8%

Sample

1st row관리비 연체료 수납
2nd row101-1902호 전출(10/26) 승강기 사용료
3rd row가락공판장 우편 투입 및 게시판광고
4th row관리비 연체료 수납
5th row바닥 깨진 부분 변상
ValueCountFrequency (%)
관리비 3673
 
14.4%
연체료 3536
 
13.9%
수납 3530
 
13.8%
9월분 334
 
1.3%
8월분 280
 
1.1%
승강기 278
 
1.1%
205
 
0.8%
9월 205
 
0.8%
입금 191
 
0.7%
사용료 184
 
0.7%
Other values (7129) 13108
51.4%
2024-05-11T02:38:18.852202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15671
 
11.6%
료 5397
 
4.0%
수 4985
 
3.7%
리 4969
 
3.7%
비 4508
 
3.3%
0 4295
 
3.2%
1 4207
 
3.1%
관 3972
 
2.9%
체 3716
 
2.7%
연 3602
 
2.7%
Other values (728) 80050
59.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89042
65.8%
Decimal Number 18832
 
13.9%
Space Separator 15671
 
11.6%
Close Punctuation 3121
 
2.3%
Open Punctuation 3109
 
2.3%
Other Punctuation 2300
 
1.7%
Dash Punctuation 2170
 
1.6%
Uppercase Letter 614
 
0.5%
Math Symbol 259
 
0.2%
Lowercase Letter 181
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
료 5397
 
6.1%
수 4985
 
5.6%
리 4969
 
5.6%
비 4508
 
5.1%
관 3972
 
4.5%
체 3716
 
4.2%
연 3602
 
4.0%
납 3587
 
4.0%
자 1893
 
2.1%
금 1819
 
2.0%
Other values (640) 50594
56.8%
Uppercase Letter
ValueCountFrequency (%)
N 71
 
11.6%
K 56
 
9.1%
T 44
 
7.2%
B 43
 
7.0%
S 39
 
6.4%
C 39
 
6.4%
L 39
 
6.4%
O 38
 
6.2%
G 36
 
5.9%
D 31
 
5.0%
Other values (15) 178
29.0%
Lowercase Letter
ValueCountFrequency (%)
o 53
29.3%
n 28
15.5%
x 20
 
11.0%
k 10
 
5.5%
e 9
 
5.0%
s 9
 
5.0%
t 8
 
4.4%
m 6
 
3.3%
b 4
 
2.2%
c 4
 
2.2%
Other values (13) 30
16.6%
Other Punctuation
ValueCountFrequency (%)
/ 706
30.7%
. 600
26.1%
, 592
25.7%
: 190
 
8.3%
* 110
 
4.8%
@ 25
 
1.1%
% 23
 
1.0%
? 21
 
0.9%
# 13
 
0.6%
' 9
 
0.4%
Other values (4) 11
 
0.5%
Decimal Number
ValueCountFrequency (%)
0 4295
22.8%
1 4207
22.3%
2 2129
11.3%
9 2081
11.1%
8 1358
 
7.2%
3 1341
 
7.1%
4 983
 
5.2%
5 938
 
5.0%
6 767
 
4.1%
7 733
 
3.9%
Math Symbol
ValueCountFrequency (%)
~ 211
81.5%
> 16
 
6.2%
+ 14
 
5.4%
< 9
 
3.5%
= 5
 
1.9%
× 2
 
0.8%
÷ 1
 
0.4%
→ 1
 
0.4%
Close Punctuation
ValueCountFrequency (%)
) 3064
98.2%
] 57
 
1.8%
Open Punctuation
ValueCountFrequency (%)
( 3052
98.2%
[ 57
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 2168
99.9%
－ 2
 
0.1%
Space Separator
ValueCountFrequency (%)
15671
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 73
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89035
65.8%
Common 45535
33.6%
Latin 795
 
0.6%
Han 7
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
료 5397
 
6.1%
수 4985
 
5.6%
리 4969
 
5.6%
비 4508
 
5.1%
관 3972
 
4.5%
체 3716
 
4.2%
연 3602
 
4.0%
납 3587
 
4.0%
자 1893
 
2.1%
금 1819
 
2.0%
Other values (635) 50587
56.8%
Latin
ValueCountFrequency (%)
N 71
 
8.9%
K 56
 
7.0%
o 53
 
6.7%
T 44
 
5.5%
B 43
 
5.4%
S 39
 
4.9%
C 39
 
4.9%
L 39
 
4.9%
O 38
 
4.8%
G 36
 
4.5%
Other values (38) 337
42.4%
Common
ValueCountFrequency (%)
15671
34.4%
0 4295
 
9.4%
1 4207
 
9.2%
) 3064
 
6.7%
( 3052
 
6.7%
- 2168
 
4.8%
2 2129
 
4.7%
9 2081
 
4.6%
8 1358
 
3.0%
3 1341
 
2.9%
Other values (30) 6169
 
13.5%
Han
ValueCountFrequency (%)
入 2
28.6%
人 2
28.6%
不 1
14.3%
分 1
14.3%
月 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89034
65.8%
ASCII 46324
34.2%
CJK 6
 
< 0.1%
None 5
 
< 0.1%
Compat Jamo 1
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%
Arrows 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15671
33.8%
0 4295
 
9.3%
1 4207
 
9.1%
) 3064
 
6.6%
( 3052
 
6.6%
- 2168
 
4.7%
2 2129
 
4.6%
9 2081
 
4.5%
8 1358
 
2.9%
3 1341
 
2.9%
Other values (74) 6958
15.0%
Hangul
ValueCountFrequency (%)
료 5397
 
6.1%
수 4985
 
5.6%
리 4969
 
5.6%
비 4508
 
5.1%
관 3972
 
4.5%
체 3716
 
4.2%
연 3602
 
4.0%
납 3587
 
4.0%
자 1893
 
2.1%
금 1819
 
2.0%
Other values (634) 50586
56.8%
CJK
ValueCountFrequency (%)
入 2
33.3%
人 2
33.3%
分 1
16.7%
月 1
16.7%
None
ValueCountFrequency (%)
－ 2
40.0%
× 2
40.0%
÷ 1
20.0%
Compat Jamo
ValueCountFrequency (%)
ㅡ 1
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
不 1
100.0%
Arrows
ValueCountFrequency (%)
→ 1
100.0%

Interactions

2024-05-11T02:38:06.764368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:38:05.975140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:38:07.131777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:38:06.389219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T02:38:19.259805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비용명년월일금액
비용명1.0000.4400.211
년월일0.4401.0000.022
금액0.2110.0221.000
2024-05-11T02:38:19.531653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월일금액비용명
년월일1.0000.0270.181
금액0.0271.0000.091
비용명0.1810.0911.000

Missing values

2024-05-11T02:38:07.586909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T02:38:08.095384image/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

아파트명아파트코드비용명년월일금액내용
23617역삼아이파크A13508009연체료수익2019092753590관리비 연체료 수납
1965당산롯데캐슬프레스티지 아파트A10026797승강기수익20190905120000101-1902호 전출(10/26) 승강기 사용료
19854옥수삼성A13375902광고료수익20190903150000가락공판장 우편 투입 및 게시판광고
57624가양3단지(강변)A15780704연체료수익201909014100관리비 연체료 수납
21113뚝섬중앙하이츠빌A13384303잡수익2019092730000바닥 깨진 부분 변상
39747월계사슴3단지A13984411연체료수익201909049460관리비 연체료 수납
25435도곡개포한신아파트A13527016연체료수익201909267240관리비 연체료 수납
37662수락리버시티 4단지A13974101광고료수익20190902120000게시판광고(1개월) - 태양광 별빛에너지
18563창동주공19단지A13290107고용안정사업수익20190920281660일자리안정자금 입금(7,8월 미화)
39211상계주공2단지A13983004광고료수익20190916100000우편함광고(스포애니)
아파트명아파트코드비용명년월일금액내용
13479래미안엘파인A13075402광고료수익2019093050000게시판광고(시몬스)
57737가양도시개발공사8단지(임대)A15780904잡수익20190905549450재활용 (임차인)
36970중계주공8단지A13922111주차장수익20190930111000009월분 주차비(801동107호외36대)
33161올림픽선수기자촌아파트A13805002주차장수익201909192219059/19 주차료 수익_카드사 입금액(8건)
31444서초네이처힐3단지A13778205연체료수익201909182850관리비 연체료 수납
4699LH강남힐스테이트A10027985기타운영수익2019091835000한기창 헬스등록
37009중계주공5단지A13922114공동주택지원금수익20190924100000어르신 무더위쉼터 지원금
550길음뉴타운11단지 롯데캐슬골든힐스아파트A10025753연체료수익201909151940관리비 연체료 수납
48296신림동부센트레빌A15102202기타운영수익201909184551965헬스통장잔고(9.18일부터 전산관리)
34305잠실리센츠A13822003연체료수익2019090747220관리비 연체료 수납