Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells2
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 = 49.64890655)Skewed

Reproduction

Analysis started2024-05-11 02:40:28.272656
Analysis finished2024-05-11 02:40:33.022797
Duration4.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

Distinct2056
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:40:33.476005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length7.1898
Min length2

Characters and Unicode

Total characters71898
Distinct characters430
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

Unique212 ?
Unique (%)2.1%

Sample

1st row여의도목화
2nd row래미안위브
3rd row래미안장안2차
4th row쌍문동익파크
5th row고덕아이파크아파트
ValueCountFrequency (%)
아파트 122
 
1.2%
래미안 39
 
0.4%
신내 26
 
0.2%
입주자대표회의 23
 
0.2%
목동5단지 22
 
0.2%
잠실파크리오 21
 
0.2%
마포래미안푸르지오 20
 
0.2%
sk북한산시티아파트 20
 
0.2%
신월시영아파트 19
 
0.2%
상계주공7단지 19
 
0.2%
Other values (2107) 10228
96.9%
2024-05-11T02:40:34.584816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
파 2223
 
3.1%
아 2155
 
3.0%
지 2118
 
2.9%
트 2008
 
2.8%
대 1807
 
2.5%
단 1665
 
2.3%
동 1574
 
2.2%
신 1463
 
2.0%
차 1429
 
2.0%
성 1349
 
1.9%
Other values (420) 54107
75.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 65765
91.5%
Decimal Number 4022
 
5.6%
Uppercase Letter 666
 
0.9%
Space Separator 608
 
0.8%
Lowercase Letter 258
 
0.4%
Open Punctuation 162
 
0.2%
Close Punctuation 162
 
0.2%
Other Punctuation 131
 
0.2%
Dash Punctuation 111
 
0.2%
Math Symbol 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
파 2223
 
3.4%
아 2155
 
3.3%
지 2118
 
3.2%
트 2008
 
3.1%
대 1807
 
2.7%
단 1665
 
2.5%
동 1574
 
2.4%
신 1463
 
2.2%
차 1429
 
2.2%
성 1349
 
2.1%
Other values (374) 47974
72.9%
Uppercase Letter
ValueCountFrequency (%)
S 126
18.9%
K 102
15.3%
C 73
11.0%
D 50
 
7.5%
M 50
 
7.5%
H 48
 
7.2%
L 42
 
6.3%
I 27
 
4.1%
E 26
 
3.9%
A 25
 
3.8%
Other values (7) 97
14.6%
Lowercase Letter
ValueCountFrequency (%)
e 146
56.6%
l 26
 
10.1%
i 25
 
9.7%
v 16
 
6.2%
c 10
 
3.9%
w 8
 
3.1%
k 8
 
3.1%
s 7
 
2.7%
h 4
 
1.6%
g 4
 
1.6%
Decimal Number
ValueCountFrequency (%)
2 1189
29.6%
1 1180
29.3%
3 487
12.1%
4 274
 
6.8%
5 241
 
6.0%
6 190
 
4.7%
7 182
 
4.5%
9 118
 
2.9%
8 84
 
2.1%
0 77
 
1.9%
Other Punctuation
ValueCountFrequency (%)
, 114
87.0%
. 17
 
13.0%
Space Separator
ValueCountFrequency (%)
608
100.0%
Open Punctuation
ValueCountFrequency (%)
( 162
100.0%
Close Punctuation
ValueCountFrequency (%)
) 162
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 111
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 65765
91.5%
Common 5203
 
7.2%
Latin 930
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
파 2223
 
3.4%
아 2155
 
3.3%
지 2118
 
3.2%
트 2008
 
3.1%
대 1807
 
2.7%
단 1665
 
2.5%
동 1574
 
2.4%
신 1463
 
2.2%
차 1429
 
2.2%
성 1349
 
2.1%
Other values (374) 47974
72.9%
Latin
ValueCountFrequency (%)
e 146
15.7%
S 126
13.5%
K 102
11.0%
C 73
 
7.8%
D 50
 
5.4%
M 50
 
5.4%
H 48
 
5.2%
L 42
 
4.5%
I 27
 
2.9%
E 26
 
2.8%
Other values (19) 240
25.8%
Common
ValueCountFrequency (%)
2 1189
22.9%
1 1180
22.7%
608
11.7%
3 487
9.4%
4 274
 
5.3%
5 241
 
4.6%
6 190
 
3.7%
7 182
 
3.5%
( 162
 
3.1%
) 162
 
3.1%
Other values (7) 528
10.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 65765
91.5%
ASCII 6127
 
8.5%
Number Forms 6
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
파 2223
 
3.4%
아 2155
 
3.3%
지 2118
 
3.2%
트 2008
 
3.1%
대 1807
 
2.7%
단 1665
 
2.5%
동 1574
 
2.4%
신 1463
 
2.2%
차 1429
 
2.2%
성 1349
 
2.1%
Other values (374) 47974
72.9%
ASCII
ValueCountFrequency (%)
2 1189
19.4%
1 1180
19.3%
608
9.9%
3 487
 
7.9%
4 274
 
4.5%
5 241
 
3.9%
6 190
 
3.1%
7 182
 
3.0%
( 162
 
2.6%
) 162
 
2.6%
Other values (35) 1452
23.7%
Number Forms
ValueCountFrequency (%)
Ⅰ 6
100.0%

아파트코드
Text

Distinct2062
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:40:35.602011image/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

Unique214 ?
Unique (%)2.1%

Sample

1st rowA15088208
2nd rowA13003007
3rd rowA13010005
4th rowA13287503
5th rowA13408003
ValueCountFrequency (%)
a15805504 22
 
0.2%
a13824006 21
 
0.2%
a14272304 20
 
0.2%
a12175203 20
 
0.2%
a15884703 19
 
0.2%
a13982704 19
 
0.2%
a13010005 18
 
0.2%
a10027289 18
 
0.2%
a14021001 18
 
0.2%
a14383205 18
 
0.2%
Other values (2052) 9807
98.1%
2024-05-11T02:40:36.906188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18351
20.4%
1 17396
19.3%
A 9993
11.1%
3 9081
10.1%
2 7959
8.8%
5 6338
 
7.0%
8 5675
 
6.3%
7 5073
 
5.6%
4 3807
 
4.2%
6 3331
 
3.7%
Other values (2) 2996
 
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 18351
22.9%
1 17396
21.7%
3 9081
11.4%
2 7959
9.9%
5 6338
 
7.9%
8 5675
 
7.1%
7 5073
 
6.3%
4 3807
 
4.8%
6 3331
 
4.2%
9 2989
 
3.7%
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 18351
22.9%
1 17396
21.7%
3 9081
11.4%
2 7959
9.9%
5 6338
 
7.9%
8 5675
 
7.1%
7 5073
 
6.3%
4 3807
 
4.8%
6 3331
 
4.2%
9 2989
 
3.7%
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 18351
20.4%
1 17396
19.3%
A 9993
11.1%
3 9081
10.1%
2 7959
8.8%
5 6338
 
7.0%
8 5675
 
6.3%
7 5073
 
5.6%
4 3807
 
4.2%
6 3331
 
3.7%
Other values (2) 2996
 
3.3%

비용명
Categorical

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
연체료수익
4041 
광고료수익
1076 
잡수익
1074 
주차장수익
837 
승강기수익
823 
Other values (10)
2149 

Length

Max length9
Median length5
Mean length4.8612
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
연체료수익 4041
40.4%
광고료수익 1076
 
10.8%
잡수익 1074
 
10.7%
주차장수익 837
 
8.4%
승강기수익 823
 
8.2%
기타운영수익 729
 
7.3%
검침수익 283
 
2.8%
알뜰시장수익 258
 
2.6%
임대료수익 253
 
2.5%
부과차익 202
 
2.0%
Other values (5) 424
 
4.2%

Length

2024-05-11T02:40:37.558044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
연체료수익 4041
40.4%
광고료수익 1076
 
10.8%
잡수익 1074
 
10.7%
주차장수익 837
 
8.4%
승강기수익 823
 
8.2%
기타운영수익 729
 
7.3%
검침수익 283
 
2.8%
알뜰시장수익 258
 
2.6%
임대료수익 253
 
2.5%
부과차익 202
 
2.0%
Other values (5) 424
 
4.2%

년월일
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190118
Minimum20190101
Maximum20190131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:40:37.971106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190101
5-th percentile20190102
Q120190109
median20190120
Q320190127
95-th percentile20190131
Maximum20190131
Range30
Interquartile range (IQR)18

Descriptive statistics

Standard deviation9.8562318
Coefficient of variation (CV)4.8817108 × 10-7
Kurtosis-1.3116223
Mean20190118
Median Absolute Deviation (MAD)9
Skewness-0.25166336
Sum2.0190118 × 1011
Variance97.145306
MonotonicityNot monotonic
2024-05-11T02:40:38.438656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20190131 1083
 
10.8%
20190125 592
 
5.9%
20190130 532
 
5.3%
20190102 503
 
5.0%
20190128 442
 
4.4%
20190124 434
 
4.3%
20190110 423
 
4.2%
20190121 412
 
4.1%
20190129 410
 
4.1%
20190107 398
 
4.0%
Other values (21) 4771
47.7%
ValueCountFrequency (%)
20190101 241
2.4%
20190102 503
5.0%
20190103 351
3.5%
20190104 351
3.5%
20190105 100
 
1.0%
20190106 83
 
0.8%
20190107 398
4.0%
20190108 307
3.1%
20190109 290
2.9%
20190110 423
4.2%
ValueCountFrequency (%)
20190131 1083
10.8%
20190130 532
5.3%
20190129 410
 
4.1%
20190128 442
4.4%
20190127 190
 
1.9%
20190126 153
 
1.5%
20190125 592
5.9%
20190124 434
4.3%
20190123 386
 
3.9%
20190122 341
 
3.4%

금액
Real number (ℝ)

SKEWED 

Distinct3204
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239440.9
Minimum-1977890
Maximum1.6000614 × 108
Zeros14
Zeros (%)0.1%
Negative38
Negative (%)0.4%
Memory size166.0 KiB
2024-05-11T02:40:38.900047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1977890
5-th percentile140
Q12506
median26340
Q390000
95-th percentile821751.05
Maximum1.6000614 × 108
Range1.6198403 × 108
Interquartile range (IQR)87494

Descriptive statistics

Standard deviation2122514.8
Coefficient of variation (CV)8.8644621
Kurtosis3378.7929
Mean239440.9
Median Absolute Deviation (MAD)25030
Skewness49.648907
Sum2.394409 × 109
Variance4.5050691 × 1012
MonotonicityNot monotonic
2024-05-11T02:40:39.452439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30000 666
 
6.7%
50000 607
 
6.1%
100000 444
 
4.4%
60000 159
 
1.6%
40000 142
 
1.4%
80000 140
 
1.4%
70000 138
 
1.4%
20000 125
 
1.2%
200000 124
 
1.2%
150000 124
 
1.2%
Other values (3194) 7331
73.3%
ValueCountFrequency (%)
-1977890 1
< 0.1%
-1478710 1
< 0.1%
-720000 1
< 0.1%
-467840 1
< 0.1%
-360000 1
< 0.1%
-253000 1
< 0.1%
-240000 1
< 0.1%
-198230 1
< 0.1%
-160000 1
< 0.1%
-110840 1
< 0.1%
ValueCountFrequency (%)
160006136 1
< 0.1%
71000000 1
< 0.1%
41184000 1
< 0.1%
34084170 1
< 0.1%
34000000 1
< 0.1%
31217600 1
< 0.1%
30399278 1
< 0.1%
27500000 1
< 0.1%
27438180 1
< 0.1%
21000000 1
< 0.1%

내용
Text

Distinct5372
Distinct (%)53.7%
Missing2
Missing (%)< 0.1%
Memory size156.2 KiB
2024-05-11T02:40:40.254143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length92
Median length89
Mean length13.272454
Min length1

Characters and Unicode

Total characters132698
Distinct characters747
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5140 ?
Unique (%)51.4%

Sample

1st row1-411 승강기사용료
2nd row관리비 연체료 수납
3rd row독서실- 월4, 일2
4th row우편함 광고료(해나온-가락공판장)
5th row자판기 수입
ValueCountFrequency (%)
관리비 4189
 
16.2%
연체료 4053
 
15.7%
수납 4050
 
15.6%
1월분 350
 
1.4%
226
 
0.9%
승강기 225
 
0.9%
12월분 201
 
0.8%
1월 191
 
0.7%
게시판 177
 
0.7%
사용료 162
 
0.6%
Other values (6736) 12065
46.6%
2024-05-11T02:40:41.671450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15981
 
12.0%
1 6264
 
4.7%
료 5990
 
4.5%
수 5371
 
4.0%
비 4786
 
3.6%
리 4726
 
3.6%
관 4360
 
3.3%
체 4277
 
3.2%
납 4155
 
3.1%
연 4127
 
3.1%
Other values (737) 72661
54.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 85968
64.8%
Decimal Number 19522
 
14.7%
Space Separator 15981
 
12.0%
Close Punctuation 2707
 
2.0%
Open Punctuation 2703
 
2.0%
Other Punctuation 2546
 
1.9%
Dash Punctuation 2038
 
1.5%
Uppercase Letter 676
 
0.5%
Math Symbol 308
 
0.2%
Lowercase Letter 186
 
0.1%
Other values (3) 63
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
료 5990
 
7.0%
수 5371
 
6.2%
비 4786
 
5.6%
리 4726
 
5.5%
관 4360
 
5.1%
체 4277
 
5.0%
납 4155
 
4.8%
연 4127
 
4.8%
월 1538
 
1.8%
용 1526
 
1.8%
Other values (649) 45112
52.5%
Uppercase Letter
ValueCountFrequency (%)
N 79
11.7%
L 58
 
8.6%
G 53
 
7.8%
T 52
 
7.7%
K 44
 
6.5%
D 43
 
6.4%
B 41
 
6.1%
O 39
 
5.8%
S 38
 
5.6%
A 38
 
5.6%
Other values (14) 191
28.3%
Lowercase Letter
ValueCountFrequency (%)
o 64
34.4%
n 27
14.5%
s 12
 
6.5%
x 9
 
4.8%
k 8
 
4.3%
t 7
 
3.8%
l 7
 
3.8%
p 7
 
3.8%
r 6
 
3.2%
a 6
 
3.2%
Other values (12) 33
17.7%
Other Punctuation
ValueCountFrequency (%)
. 795
31.2%
/ 786
30.9%
, 574
22.5%
: 188
 
7.4%
* 67
 
2.6%
? 63
 
2.5%
% 19
 
0.7%
@ 15
 
0.6%
# 14
 
0.5%
' 12
 
0.5%
Other values (4) 13
 
0.5%
Decimal Number
ValueCountFrequency (%)
1 6264
32.1%
0 4069
20.8%
2 3037
15.6%
3 1367
 
7.0%
4 1025
 
5.3%
5 830
 
4.3%
9 826
 
4.2%
8 812
 
4.2%
6 683
 
3.5%
7 609
 
3.1%
Math Symbol
ValueCountFrequency (%)
~ 275
89.3%
+ 11
 
3.6%
> 8
 
2.6%
< 5
 
1.6%
= 3
 
1.0%
⇒ 2
 
0.6%
→ 2
 
0.6%
× 1
 
0.3%
∼ 1
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 2651
97.9%
] 56
 
2.1%
Open Punctuation
ValueCountFrequency (%)
( 2645
97.9%
[ 58
 
2.1%
Space Separator
ValueCountFrequency (%)
15981
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2038
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 61
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%
Other Number
ValueCountFrequency (%)
① 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 85966
64.8%
Common 45868
34.6%
Latin 862
 
0.6%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
료 5990
 
7.0%
수 5371
 
6.2%
비 4786
 
5.6%
리 4726
 
5.5%
관 4360
 
5.1%
체 4277
 
5.0%
납 4155
 
4.8%
연 4127
 
4.8%
월 1538
 
1.8%
용 1526
 
1.8%
Other values (647) 45110
52.5%
Latin
ValueCountFrequency (%)
N 79
 
9.2%
o 64
 
7.4%
L 58
 
6.7%
G 53
 
6.1%
T 52
 
6.0%
K 44
 
5.1%
D 43
 
5.0%
B 41
 
4.8%
O 39
 
4.5%
S 38
 
4.4%
Other values (36) 351
40.7%
Common
ValueCountFrequency (%)
15981
34.8%
1 6264
 
13.7%
0 4069
 
8.9%
2 3037
 
6.6%
) 2651
 
5.8%
( 2645
 
5.8%
- 2038
 
4.4%
3 1367
 
3.0%
4 1025
 
2.2%
5 830
 
1.8%
Other values (32) 5961
 
13.0%
Han
ValueCountFrequency (%)
外 1
50.0%
出 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 85965
64.8%
ASCII 46723
35.2%
Arrows 4
 
< 0.1%
CJK 2
 
< 0.1%
None 1
 
< 0.1%
Compat Jamo 1
 
< 0.1%
Math Operators 1
 
< 0.1%
Enclosed Alphanum 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15981
34.2%
1 6264
 
13.4%
0 4069
 
8.7%
2 3037
 
6.5%
) 2651
 
5.7%
( 2645
 
5.7%
- 2038
 
4.4%
3 1367
 
2.9%
4 1025
 
2.2%
5 830
 
1.8%
Other values (73) 6816
14.6%
Hangul
ValueCountFrequency (%)
료 5990
 
7.0%
수 5371
 
6.2%
비 4786
 
5.6%
리 4726
 
5.5%
관 4360
 
5.1%
체 4277
 
5.0%
납 4155
 
4.8%
연 4127
 
4.8%
월 1538
 
1.8%
용 1526
 
1.8%
Other values (646) 45109
52.5%
Arrows
ValueCountFrequency (%)
⇒ 2
50.0%
→ 2
50.0%
CJK
ValueCountFrequency (%)
外 1
50.0%
出 1
50.0%
None
ValueCountFrequency (%)
× 1
100.0%
Compat Jamo
ValueCountFrequency (%)
ㄱ 1
100.0%
Math Operators
ValueCountFrequency (%)
∼ 1
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
① 1
100.0%

Interactions

2024-05-11T02:40:31.477705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:40:30.700232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:40:32.020347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:40:31.019869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T02:40:42.098509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비용명년월일금액
비용명1.0000.2960.116
년월일0.2961.0000.000
금액0.1160.0001.000
2024-05-11T02:40:42.374503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월일금액비용명
년월일1.0000.0620.115
금액0.0621.0000.049
비용명0.1150.0491.000

Missing values

2024-05-11T02:40:32.545714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T02:40:32.860491image/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
44730여의도목화A15088208승강기수익20190108800001-411 승강기사용료
11143래미안위브A13003007연체료수익201901224080관리비 연체료 수납
11860래미안장안2차A13010005기타운영수익20190123166000독서실- 월4, 일2
16796쌍문동익파크A13287503광고료수익2019012450000우편함 광고료(해나온-가락공판장)
20252고덕아이파크아파트A13408003기타운영수익2019011120000자판기 수입
12764전농동아A13085901잡수익2019013130001-601음식물카드 (재고33개)
19361성수동아그린A13384304연체료수익201901286300관리비 연체료 수납
16394방학벽산2차A13283405연체료수익201901104820관리비 연체료 수납
29763서초현대1,2차A13788002잡수익2019012362018년4분가 부가가치세 납부
25508돈암동한신한진아파트A13606004검침수익2019011411670181월분 전기검침수당(한진)
아파트명아파트코드비용명년월일금액내용
38623동부이촌동우성A14003001검침수익201901021044901월(12월 검침분) 한전검침 수입
41251구의대림아크로리버A14320002연체료수익201901317600관리비 연체료 수납
27299삼선힐스테이트A13672102연체료수익20190101160관리비 연체료 수납
54667방화11단지A15785005연체료수익20190122430관리비 연체료 수납
12175휘경 미소지움아파트A13077702임대료수익20190131250000SK텔레콤 104동 임대료(18.04.27~19.04.26) 9/12
30552풍납쌍용A13804002연체료수익20190127180관리비 연체료 수납
7212마포강변힐스테이트A12112002연체료수익201901022480관리비 연체료 수납
12544장안래미안1차A13084101연체료수익2019012911590관리비 연체료 수납
56305삼성쉐르빌1 아파트A15807603승강기수익20190114100000A1704호전출1/15일 전출 승강기 수입
16233도봉서원제2A13275302주차장수익2019012750000104-509호 외부주차장수입