Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells15
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 = 71.99904449)Skewed

Reproduction

Analysis started2024-05-11 02:37:46.982648
Analysis finished2024-05-11 02:37:51.506214
Duration4.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

Distinct2064
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:37:52.015496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length7.1769
Min length2

Characters and Unicode

Total characters71769
Distinct characters429
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

Unique196 ?
Unique (%)2.0%

Sample

1st row방화1단지(장미)
2nd row신내5단지대림두산
3rd row신사미성
4th row중림삼성래미안아파트
5th row충무로진양
ValueCountFrequency (%)
아파트 122
 
1.2%
래미안 25
 
0.2%
잠실엘스 23
 
0.2%
상계주공7단지 19
 
0.2%
서초힐스 18
 
0.2%
벽산라이브파크 18
 
0.2%
고덕 18
 
0.2%
올림픽선수기자촌아파트 17
 
0.2%
잠실리센츠 17
 
0.2%
힐스테이트 17
 
0.2%
Other values (2120) 10220
97.2%
2024-05-11T02:37:53.371278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
파 2322
 
3.2%
아 2303
 
3.2%
트 2098
 
2.9%
지 2083
 
2.9%
대 1707
 
2.4%
단 1673
 
2.3%
동 1628
 
2.3%
신 1473
 
2.1%
차 1353
 
1.9%
성 1283
 
1.8%
Other values (419) 53846
75.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 65855
91.8%
Decimal Number 3824
 
5.3%
Uppercase Letter 780
 
1.1%
Space Separator 562
 
0.8%
Lowercase Letter 214
 
0.3%
Open Punctuation 137
 
0.2%
Close Punctuation 137
 
0.2%
Other Punctuation 130
 
0.2%
Dash Punctuation 115
 
0.2%
Letter Number 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
파 2322
 
3.5%
아 2303
 
3.5%
트 2098
 
3.2%
지 2083
 
3.2%
대 1707
 
2.6%
단 1673
 
2.5%
동 1628
 
2.5%
신 1473
 
2.2%
차 1353
 
2.1%
성 1283
 
1.9%
Other values (373) 47932
72.8%
Uppercase Letter
ValueCountFrequency (%)
S 147
18.8%
K 121
15.5%
C 106
13.6%
D 62
7.9%
M 62
7.9%
H 49
 
6.3%
L 40
 
5.1%
A 39
 
5.0%
E 37
 
4.7%
I 34
 
4.4%
Other values (7) 83
10.6%
Lowercase Letter
ValueCountFrequency (%)
e 142
66.4%
l 24
 
11.2%
i 18
 
8.4%
v 12
 
5.6%
a 3
 
1.4%
s 3
 
1.4%
w 3
 
1.4%
g 3
 
1.4%
h 3
 
1.4%
c 2
 
0.9%
Decimal Number
ValueCountFrequency (%)
1 1141
29.8%
2 1104
28.9%
3 493
12.9%
4 269
 
7.0%
5 213
 
5.6%
6 167
 
4.4%
7 141
 
3.7%
9 108
 
2.8%
8 107
 
2.8%
0 81
 
2.1%
Other Punctuation
ValueCountFrequency (%)
, 113
86.9%
. 17
 
13.1%
Space Separator
ValueCountFrequency (%)
562
100.0%
Open Punctuation
ValueCountFrequency (%)
( 137
100.0%
Close Punctuation
ValueCountFrequency (%)
) 137
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 115
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 9
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 65855
91.8%
Common 4911
 
6.8%
Latin 1003
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
파 2322
 
3.5%
아 2303
 
3.5%
트 2098
 
3.2%
지 2083
 
3.2%
대 1707
 
2.6%
단 1673
 
2.5%
동 1628
 
2.5%
신 1473
 
2.2%
차 1353
 
2.1%
성 1283
 
1.9%
Other values (373) 47932
72.8%
Latin
ValueCountFrequency (%)
S 147
14.7%
e 142
14.2%
K 121
12.1%
C 106
10.6%
D 62
 
6.2%
M 62
 
6.2%
H 49
 
4.9%
L 40
 
4.0%
A 39
 
3.9%
E 37
 
3.7%
Other values (19) 198
19.7%
Common
ValueCountFrequency (%)
1 1141
23.2%
2 1104
22.5%
562
11.4%
3 493
10.0%
4 269
 
5.5%
5 213
 
4.3%
6 167
 
3.4%
7 141
 
2.9%
( 137
 
2.8%
) 137
 
2.8%
Other values (7) 547
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 65855
91.8%
ASCII 5905
 
8.2%
Number Forms 9
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
파 2322
 
3.5%
아 2303
 
3.5%
트 2098
 
3.2%
지 2083
 
3.2%
대 1707
 
2.6%
단 1673
 
2.5%
동 1628
 
2.5%
신 1473
 
2.2%
차 1353
 
2.1%
성 1283
 
1.9%
Other values (373) 47932
72.8%
ASCII
ValueCountFrequency (%)
1 1141
19.3%
2 1104
18.7%
562
 
9.5%
3 493
 
8.3%
4 269
 
4.6%
5 213
 
3.6%
6 167
 
2.8%
S 147
 
2.5%
e 142
 
2.4%
7 141
 
2.4%
Other values (35) 1526
25.8%
Number Forms
ValueCountFrequency (%)
Ⅰ 9
100.0%

아파트코드
Text

Distinct2069
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:37:54.304687image/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

Unique196 ?
Unique (%)2.0%

Sample

1st rowA15722307
2nd rowA13184610
3rd rowA12208205
4th rowA10085902
5th rowA10086301
ValueCountFrequency (%)
a13822004 23
 
0.2%
a13982704 19
 
0.2%
a14272305 18
 
0.2%
a13778204 18
 
0.2%
a13822003 17
 
0.2%
a13805002 17
 
0.2%
a15279101 16
 
0.2%
a13527203 16
 
0.2%
a15875101 16
 
0.2%
a13201209 16
 
0.2%
Other values (2059) 9824
98.2%
2024-05-11T02:37:55.432087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18379
20.4%
1 17326
19.3%
A 9994
11.1%
3 9127
10.1%
2 8157
9.1%
5 6223
 
6.9%
8 5657
 
6.3%
7 4947
 
5.5%
4 3782
 
4.2%
6 3463
 
3.8%
Other values (2) 2945
 
3.3%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18379
23.0%
1 17326
21.7%
3 9127
11.4%
2 8157
10.2%
5 6223
 
7.8%
8 5657
 
7.1%
7 4947
 
6.2%
4 3782
 
4.7%
6 3463
 
4.3%
9 2939
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
A 9994
99.9%
B 6
 
0.1%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
0 18379
23.0%
1 17326
21.7%
3 9127
11.4%
2 8157
10.2%
5 6223
 
7.8%
8 5657
 
7.1%
7 4947
 
6.2%
4 3782
 
4.7%
6 3463
 
4.3%
9 2939
 
3.7%
Latin
ValueCountFrequency (%)
A 9994
99.9%
B 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18379
20.4%
1 17326
19.3%
A 9994
11.1%
3 9127
10.1%
2 8157
9.1%
5 6223
 
6.9%
8 5657
 
6.3%
7 4947
 
5.5%
4 3782
 
4.2%
6 3463
 
3.8%
Other values (2) 2945
 
3.3%

비용명
Categorical

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
연체료수익
3404 
광고료수익
1116 
승강기수익
1075 
잡수익
1056 
주차장수익
786 
Other values (10)
2563 

Length

Max length9
Median length5
Mean length5.0358
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
연체료수익 3404
34.0%
광고료수익 1116
 
11.2%
승강기수익 1075
 
10.8%
잡수익 1056
 
10.6%
주차장수익 786
 
7.9%
기타운영수익 771
 
7.7%
고용안정사업수익 514
 
5.1%
검침수익 268
 
2.7%
알뜰시장수익 218
 
2.2%
임대료수익 209
 
2.1%
Other values (5) 583
 
5.8%

Length

2024-05-11T02:37:55.895896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
연체료수익 3404
34.0%
광고료수익 1116
 
11.2%
승강기수익 1075
 
10.8%
잡수익 1056
 
10.6%
주차장수익 786
 
7.9%
기타운영수익 771
 
7.7%
고용안정사업수익 514
 
5.1%
검침수익 268
 
2.7%
알뜰시장수익 218
 
2.2%
임대료수익 209
 
2.1%
Other values (5) 583
 
5.8%

년월일
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20191018
Minimum20191001
Maximum20191031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:37:56.386516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20191001
5-th percentile20191002
Q120191010
median20191018
Q320191027
95-th percentile20191031
Maximum20191031
Range30
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.5744787
Coefficient of variation (CV)4.7419494 × 10-7
Kurtosis-1.1667267
Mean20191018
Median Absolute Deviation (MAD)8
Skewness-0.28139105
Sum2.0191018 × 1011
Variance91.670643
MonotonicityNot monotonic
2024-05-11T02:37:56.838752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20191031 1013
 
10.1%
20191025 568
 
5.7%
20191030 542
 
5.4%
20191015 504
 
5.0%
20191021 481
 
4.8%
20191001 467
 
4.7%
20191028 445
 
4.5%
20191010 444
 
4.4%
20191029 426
 
4.3%
20191018 398
 
4.0%
Other values (21) 4712
47.1%
ValueCountFrequency (%)
20191001 467
4.7%
20191002 339
3.4%
20191003 118
 
1.2%
20191004 390
3.9%
20191005 77
 
0.8%
20191006 81
 
0.8%
20191007 368
3.7%
20191008 334
3.3%
20191009 82
 
0.8%
20191010 444
4.4%
ValueCountFrequency (%)
20191031 1013
10.1%
20191030 542
5.4%
20191029 426
4.3%
20191028 445
4.5%
20191027 147
 
1.5%
20191026 118
 
1.2%
20191025 568
5.7%
20191024 358
 
3.6%
20191023 390
 
3.9%
20191022 361
 
3.6%

금액
Real number (ℝ)

SKEWED 

Distinct3140
Distinct (%)31.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean264766.41
Minimum-43836000
Maximum2.8095714 × 108
Zeros9
Zeros (%)0.1%
Negative48
Negative (%)0.5%
Memory size166.0 KiB
2024-05-11T02:37:57.298831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-43836000
5-th percentile168.95
Q13627.5
median30000
Q3100000
95-th percentile1040000
Maximum2.8095714 × 108
Range3.2479314 × 108
Interquartile range (IQR)96372.5

Descriptive statistics

Standard deviation3148795.6
Coefficient of variation (CV)11.892731
Kurtosis6343.0838
Mean264766.41
Median Absolute Deviation (MAD)29170
Skewness71.999044
Sum2.6476641 × 109
Variance9.9149138 × 1012
MonotonicityNot monotonic
2024-05-11T02:37:57.809825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30000 707
 
7.1%
50000 582
 
5.8%
100000 494
 
4.9%
40000 169
 
1.7%
60000 168
 
1.7%
70000 164
 
1.6%
150000 131
 
1.3%
20000 119
 
1.2%
200000 115
 
1.1%
130000 110
 
1.1%
Other values (3130) 7241
72.4%
ValueCountFrequency (%)
-43836000 1
< 0.1%
-37400000 1
< 0.1%
-11400000 1
< 0.1%
-820000 1
< 0.1%
-448380 1
< 0.1%
-445160 1
< 0.1%
-400000 2
< 0.1%
-385000 1
< 0.1%
-342912 1
< 0.1%
-334100 1
< 0.1%
ValueCountFrequency (%)
280957140 1
< 0.1%
52652330 1
< 0.1%
50000000 1
< 0.1%
42645178 1
< 0.1%
36363636 1
< 0.1%
32901610 1
< 0.1%
22442200 1
< 0.1%
21833300 1
< 0.1%
19910000 1
< 0.1%
17558166 1
< 0.1%

내용
Text

Distinct6004
Distinct (%)60.1%
Missing15
Missing (%)0.1%
Memory size156.2 KiB
2024-05-11T02:37:58.563704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length91
Median length78
Mean length14.108563
Min length2

Characters and Unicode

Total characters140874
Distinct characters743
Distinct categories14 ?
Distinct scripts4 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5775 ?
Unique (%)57.8%

Sample

1st row관리비 연체료 수납
2nd row관리비 연체료 수납
3rd row일일장(갈치)
4th row외부차량 주차수입7974 윤순언
5th row승강기수입 1652,912,1008호
ValueCountFrequency (%)
관리비 3511
 
13.6%
연체료 3414
 
13.2%
수납 3412
 
13.2%
10월분 392
 
1.5%
9월분 313
 
1.2%
승강기 280
 
1.1%
승강기사용료 254
 
1.0%
입금 232
 
0.9%
225
 
0.9%
사용료 211
 
0.8%
Other values (7594) 13664
52.7%
2024-05-11T02:37:59.931558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16024
 
11.4%
1 6161
 
4.4%
0 5789
 
4.1%
료 5483
 
3.9%
수 4808
 
3.4%
리 4739
 
3.4%
비 4293
 
3.0%
관 3723
 
2.6%
체 3584
 
2.5%
납 3526
 
2.5%
Other values (733) 82744
58.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89972
63.9%
Decimal Number 22268
 
15.8%
Space Separator 16024
 
11.4%
Close Punctuation 3187
 
2.3%
Open Punctuation 3186
 
2.3%
Other Punctuation 2656
 
1.9%
Dash Punctuation 2414
 
1.7%
Uppercase Letter 634
 
0.5%
Math Symbol 289
 
0.2%
Lowercase Letter 181
 
0.1%
Other values (4) 63
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
료 5483
 
6.1%
수 4808
 
5.3%
리 4739
 
5.3%
비 4293
 
4.8%
관 3723
 
4.1%
체 3584
 
4.0%
납 3526
 
3.9%
연 3486
 
3.9%
월 1830
 
2.0%
사 1735
 
1.9%
Other values (649) 52765
58.6%
Uppercase Letter
ValueCountFrequency (%)
N 84
13.2%
O 57
 
9.0%
B 52
 
8.2%
G 46
 
7.3%
T 41
 
6.5%
K 41
 
6.5%
C 39
 
6.2%
A 38
 
6.0%
L 35
 
5.5%
E 32
 
5.0%
Other values (14) 169
26.7%
Lowercase Letter
ValueCountFrequency (%)
o 52
28.7%
n 26
14.4%
x 18
 
9.9%
k 12
 
6.6%
s 12
 
6.6%
b 12
 
6.6%
e 7
 
3.9%
m 6
 
3.3%
g 5
 
2.8%
t 5
 
2.8%
Other values (11) 26
14.4%
Other Punctuation
ValueCountFrequency (%)
/ 832
31.3%
, 725
27.3%
. 637
24.0%
: 196
 
7.4%
* 132
 
5.0%
@ 48
 
1.8%
? 33
 
1.2%
% 16
 
0.6%
' 14
 
0.5%
# 13
 
0.5%
Other values (2) 10
 
0.4%
Decimal Number
ValueCountFrequency (%)
1 6161
27.7%
0 5789
26.0%
2 2640
11.9%
9 1749
 
7.9%
3 1483
 
6.7%
4 1160
 
5.2%
5 985
 
4.4%
6 813
 
3.7%
8 773
 
3.5%
7 715
 
3.2%
Math Symbol
ValueCountFrequency (%)
~ 249
86.2%
> 13
 
4.5%
+ 13
 
4.5%
< 7
 
2.4%
= 4
 
1.4%
× 2
 
0.7%
→ 1
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 3110
97.6%
] 77
 
2.4%
Open Punctuation
ValueCountFrequency (%)
( 3110
97.6%
[ 76
 
2.4%
Space Separator
ValueCountFrequency (%)
16024
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2414
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 59
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%
Other Symbol
ValueCountFrequency (%)
▶ 1
100.0%
Other Number
ValueCountFrequency (%)
② 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89969
63.9%
Common 50087
35.6%
Latin 815
 
0.6%
Han 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
료 5483
 
6.1%
수 4808
 
5.3%
리 4739
 
5.3%
비 4293
 
4.8%
관 3723
 
4.1%
체 3584
 
4.0%
납 3526
 
3.9%
연 3486
 
3.9%
월 1830
 
2.0%
사 1735
 
1.9%
Other values (646) 52762
58.6%
Latin
ValueCountFrequency (%)
N 84
 
10.3%
O 57
 
7.0%
o 52
 
6.4%
B 52
 
6.4%
G 46
 
5.6%
T 41
 
5.0%
K 41
 
5.0%
C 39
 
4.8%
A 38
 
4.7%
L 35
 
4.3%
Other values (35) 330
40.5%
Common
ValueCountFrequency (%)
16024
32.0%
1 6161
 
12.3%
0 5789
 
11.6%
) 3110
 
6.2%
( 3110
 
6.2%
2 2640
 
5.3%
- 2414
 
4.8%
9 1749
 
3.5%
3 1483
 
3.0%
4 1160
 
2.3%
Other values (29) 6447
12.9%
Han
ValueCountFrequency (%)
入 1
33.3%
人 1
33.3%
出 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89969
63.9%
ASCII 50897
36.1%
CJK 3
 
< 0.1%
None 2
 
< 0.1%
Geometric Shapes 1
 
< 0.1%
Enclosed Alphanum 1
 
< 0.1%
Arrows 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16024
31.5%
1 6161
 
12.1%
0 5789
 
11.4%
) 3110
 
6.1%
( 3110
 
6.1%
2 2640
 
5.2%
- 2414
 
4.7%
9 1749
 
3.4%
3 1483
 
2.9%
4 1160
 
2.3%
Other values (70) 7257
14.3%
Hangul
ValueCountFrequency (%)
료 5483
 
6.1%
수 4808
 
5.3%
리 4739
 
5.3%
비 4293
 
4.8%
관 3723
 
4.1%
체 3584
 
4.0%
납 3526
 
3.9%
연 3486
 
3.9%
월 1830
 
2.0%
사 1735
 
1.9%
Other values (646) 52762
58.6%
None
ValueCountFrequency (%)
× 2
100.0%
Geometric Shapes
ValueCountFrequency (%)
▶ 1
100.0%
CJK
ValueCountFrequency (%)
入 1
33.3%
人 1
33.3%
出 1
33.3%
Enclosed Alphanum
ValueCountFrequency (%)
② 1
100.0%
Arrows
ValueCountFrequency (%)
→ 1
100.0%

Interactions

2024-05-11T02:37:50.048379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:37:49.304160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:37:50.354146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:37:49.747592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T02:38:00.223023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비용명년월일금액
비용명1.0000.3510.109
년월일0.3511.0000.016
금액0.1090.0161.000
2024-05-11T02:38:00.463348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월일금액비용명
년월일1.0000.0170.138
금액0.0171.0000.065
비용명0.1380.0651.000

Missing values

2024-05-11T02:37:50.825274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T02:37:51.276375image/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
59283방화1단지(장미)A15722307연체료수익201910286520관리비 연체료 수납
16194신내5단지대림두산A13184610연체료수익201910102520관리비 연체료 수납
12075신사미성A12208205광고료수익2019102630000일일장(갈치)
5881중림삼성래미안아파트A10085902주차장수익2019100760000외부차량 주차수입7974 윤순언
5963충무로진양A10086301승강기수익201910111100000승강기수입 1652,912,1008호
44167보광삼성리버빌A14082302연체료수익20191001770관리비 연체료 수납
57174우리유앤미A15679103연체료수익20191030140관리비 연체료 수납
38965하계우방A13923102연체료수익201910296650관리비 연체료 수납
18019창동대우A13204204연체료수익20191011320관리비 연체료 수납
30478장위참누리A13614302광고료수익2019101630000싱싱마트-우편함
아파트명아파트코드비용명년월일금액내용
20716성수쌍용A13372102연체료수익201910181200관리비 연체료 수납
31118정릉1차e-편한세상A13676703승강기수익20191022100000승강기사용(106-1701)-공사
38147상계주공6단지A13920707연체료수익201910181640관리비 연체료 수납
62713목동2차우성임대A15807704연체료수익201910082850관리비 연체료 수납
18619방학명품ESA2단지A13272101주차장수익201910201181810/20일 kb카드외 주차신용카드매출 발생분
25481압구정미성2차A13511003연체료수익2019102315890관리비 연체료 수납
15843면목A13177701연체료수익201910071050관리비 연체료 수납
37459현대리버빌2차아파트A13887403주차장수익2019100120000308-1001
13181세양청마루A13003402연체료수익2019102829650관리비 연체료 수납
56132노량진우성A15605002주차장수익2019101040000외부주차이용료 10-9