Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells4
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 = 64.15362208)Skewed

Reproduction

Analysis started2024-05-11 02:35:08.018550
Analysis finished2024-05-11 02:35:12.442262
Duration4.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

Distinct2143
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:35:12.882988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length7.2798
Min length2

Characters and Unicode

Total characters72798
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

Unique196 ?
Unique (%)2.0%

Sample

1st row목동롯데캐슬위너
2nd row중계라이프신동아청구아파트
3rd row둔촌현대4차
4th row신길남서울
5th row쌍문한양1차
ValueCountFrequency (%)
아파트 127
 
1.2%
래미안 30
 
0.3%
북한산 26
 
0.2%
목동7단지 23
 
0.2%
아이파크 22
 
0.2%
힐스테이트 20
 
0.2%
서초더샵포레 19
 
0.2%
관리사무소 19
 
0.2%
은마 19
 
0.2%
트리마제 18
 
0.2%
Other values (2204) 10335
97.0%
2024-05-11T02:35:13.979943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 2448
 
3.4%
파 2351
 
3.2%
트 2216
 
3.0%
지 2059
 
2.8%
대 1672
 
2.3%
단 1624
 
2.2%
동 1582
 
2.2%
신 1425
 
2.0%
차 1401
 
1.9%
이 1376
 
1.9%
Other values (418) 54644
75.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66386
91.2%
Decimal Number 3896
 
5.4%
Uppercase Letter 844
 
1.2%
Space Separator 722
 
1.0%
Lowercase Letter 309
 
0.4%
Open Punctuation 171
 
0.2%
Close Punctuation 171
 
0.2%
Other Punctuation 151
 
0.2%
Dash Punctuation 136
 
0.2%
Letter Number 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 2448
 
3.7%
파 2351
 
3.5%
트 2216
 
3.3%
지 2059
 
3.1%
대 1672
 
2.5%
단 1624
 
2.4%
동 1582
 
2.4%
신 1425
 
2.1%
차 1401
 
2.1%
이 1376
 
2.1%
Other values (374) 48232
72.7%
Uppercase Letter
ValueCountFrequency (%)
S 140
16.6%
K 117
13.9%
C 97
11.5%
D 84
10.0%
M 84
10.0%
I 50
 
5.9%
H 43
 
5.1%
L 41
 
4.9%
E 34
 
4.0%
A 33
 
3.9%
Other values (7) 121
14.3%
Decimal Number
ValueCountFrequency (%)
1 1197
30.7%
2 1064
27.3%
3 482
12.4%
4 303
 
7.8%
5 227
 
5.8%
6 195
 
5.0%
7 143
 
3.7%
9 109
 
2.8%
8 96
 
2.5%
0 80
 
2.1%
Lowercase Letter
ValueCountFrequency (%)
e 198
64.1%
l 28
 
9.1%
i 20
 
6.5%
s 20
 
6.5%
v 14
 
4.5%
k 14
 
4.5%
h 7
 
2.3%
w 6
 
1.9%
c 2
 
0.6%
Other Punctuation
ValueCountFrequency (%)
, 131
86.8%
. 20
 
13.2%
Space Separator
ValueCountFrequency (%)
722
100.0%
Open Punctuation
ValueCountFrequency (%)
( 171
100.0%
Close Punctuation
ValueCountFrequency (%)
) 171
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 136
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 6
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66386
91.2%
Common 5253
 
7.2%
Latin 1159
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 2448
 
3.7%
파 2351
 
3.5%
트 2216
 
3.3%
지 2059
 
3.1%
대 1672
 
2.5%
단 1624
 
2.4%
동 1582
 
2.4%
신 1425
 
2.1%
차 1401
 
2.1%
이 1376
 
2.1%
Other values (374) 48232
72.7%
Latin
ValueCountFrequency (%)
e 198
17.1%
S 140
12.1%
K 117
10.1%
C 97
 
8.4%
D 84
 
7.2%
M 84
 
7.2%
I 50
 
4.3%
H 43
 
3.7%
L 41
 
3.5%
E 34
 
2.9%
Other values (17) 271
23.4%
Common
ValueCountFrequency (%)
1 1197
22.8%
2 1064
20.3%
722
13.7%
3 482
9.2%
4 303
 
5.8%
5 227
 
4.3%
6 195
 
3.7%
( 171
 
3.3%
) 171
 
3.3%
7 143
 
2.7%
Other values (7) 578
11.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66386
91.2%
ASCII 6406
 
8.8%
Number Forms 6
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 2448
 
3.7%
파 2351
 
3.5%
트 2216
 
3.3%
지 2059
 
3.1%
대 1672
 
2.5%
단 1624
 
2.4%
동 1582
 
2.4%
신 1425
 
2.1%
차 1401
 
2.1%
이 1376
 
2.1%
Other values (374) 48232
72.7%
ASCII
ValueCountFrequency (%)
1 1197
18.7%
2 1064
16.6%
722
11.3%
3 482
 
7.5%
4 303
 
4.7%
5 227
 
3.5%
e 198
 
3.1%
6 195
 
3.0%
( 171
 
2.7%
) 171
 
2.7%
Other values (33) 1676
26.2%
Number Forms
ValueCountFrequency (%)
Ⅰ 6
100.0%

아파트코드
Text

Distinct2150
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:35:15.090015image/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

Unique197 ?
Unique (%)2.0%

Sample

1st rowA15805303
2nd rowA13986111
3rd rowA13481802
4th rowA15085805
5th rowA13203303
ValueCountFrequency (%)
a15805115 23
 
0.2%
a13718001 19
 
0.2%
a13583507 19
 
0.2%
a13527203 18
 
0.2%
a10026988 18
 
0.2%
a13822003 17
 
0.2%
a13824006 17
 
0.2%
a13982704 17
 
0.2%
a10045002 16
 
0.2%
a12179004 16
 
0.2%
Other values (2140) 9820
98.2%
2024-05-11T02:35:16.392541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18594
20.7%
1 17084
19.0%
A 9986
11.1%
3 8838
9.8%
2 8344
9.3%
5 6419
 
7.1%
8 5667
 
6.3%
7 4828
 
5.4%
4 3790
 
4.2%
6 3401
 
3.8%
Other values (2) 3049
 
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 18594
23.2%
1 17084
21.4%
3 8838
11.0%
2 8344
10.4%
5 6419
 
8.0%
8 5667
 
7.1%
7 4828
 
6.0%
4 3790
 
4.7%
6 3401
 
4.3%
9 3035
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
A 9986
99.9%
B 14
 
0.1%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
0 18594
23.2%
1 17084
21.4%
3 8838
11.0%
2 8344
10.4%
5 6419
 
8.0%
8 5667
 
7.1%
7 4828
 
6.0%
4 3790
 
4.7%
6 3401
 
4.3%
9 3035
 
3.8%
Latin
ValueCountFrequency (%)
A 9986
99.9%
B 14
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18594
20.7%
1 17084
19.0%
A 9986
11.1%
3 8838
9.8%
2 8344
9.3%
5 6419
 
7.1%
8 5667
 
6.3%
7 4828
 
5.4%
4 3790
 
4.2%
6 3401
 
3.8%
Other values (2) 3049
 
3.4%

비용명
Categorical

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
연체료수익
3707 
잡수익
1024 
승강기수익
979 
광고료수익
875 
주차장수익
844 
Other values (10)
2571 

Length

Max length9
Median length5
Mean length5.0354
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row고용안정사업수익
2nd row알뜰시장수익
3rd row잡수익
4th row연체료수익
5th row연체료수익

Common Values

ValueCountFrequency (%)
연체료수익 3707
37.1%
잡수익 1024
 
10.2%
승강기수익 979
 
9.8%
광고료수익 875
 
8.8%
주차장수익 844
 
8.4%
기타운영수익 654
 
6.5%
고용안정사업수익 553
 
5.5%
검침수익 287
 
2.9%
알뜰시장수익 254
 
2.5%
임대료수익 214
 
2.1%
Other values (5) 609
 
6.1%

Length

2024-05-11T02:35:16.858114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
연체료수익 3707
37.1%
잡수익 1024
 
10.2%
승강기수익 979
 
9.8%
광고료수익 875
 
8.8%
주차장수익 844
 
8.4%
기타운영수익 654
 
6.5%
고용안정사업수익 553
 
5.5%
검침수익 287
 
2.9%
알뜰시장수익 254
 
2.5%
임대료수익 214
 
2.1%
Other values (5) 609
 
6.1%

년월일
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20200718
Minimum20200701
Maximum20200731
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:35:17.226391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200701
5-th percentile20200701
Q120200709
median20200720
Q320200727
95-th percentile20200731
Maximum20200731
Range30
Interquartile range (IQR)18

Descriptive statistics

Standard deviation9.6699415
Coefficient of variation (CV)4.7869297 × 10-7
Kurtosis-1.2355782
Mean20200718
Median Absolute Deviation (MAD)8
Skewness-0.22671102
Sum2.0200718 × 1011
Variance93.507768
MonotonicityNot monotonic
2024-05-11T02:35:17.604641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20200731 924
 
9.2%
20200715 535
 
5.3%
20200724 510
 
5.1%
20200701 503
 
5.0%
20200727 502
 
5.0%
20200730 499
 
5.0%
20200720 485
 
4.9%
20200710 437
 
4.4%
20200717 393
 
3.9%
20200706 390
 
3.9%
Other values (21) 4822
48.2%
ValueCountFrequency (%)
20200701 503
5.0%
20200702 337
3.4%
20200703 325
3.2%
20200704 80
 
0.8%
20200705 57
 
0.6%
20200706 390
3.9%
20200707 320
3.2%
20200708 294
2.9%
20200709 272
2.7%
20200710 437
4.4%
ValueCountFrequency (%)
20200731 924
9.2%
20200730 499
5.0%
20200729 376
3.8%
20200728 388
3.9%
20200727 502
5.0%
20200726 176
 
1.8%
20200725 170
 
1.7%
20200724 510
5.1%
20200723 324
 
3.2%
20200722 272
 
2.7%

금액
Real number (ℝ)

SKEWED 

Distinct3134
Distinct (%)31.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean252989.83
Minimum-10250000
Maximum2.0520481 × 108
Zeros14
Zeros (%)0.1%
Negative48
Negative (%)0.5%
Memory size166.0 KiB
2024-05-11T02:35:18.038816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-10250000
5-th percentile90
Q12537.5
median30000
Q3100000
95-th percentile890500
Maximum2.0520481 × 108
Range2.1545481 × 108
Interquartile range (IQR)97462.5

Descriptive statistics

Standard deviation2410015.1
Coefficient of variation (CV)9.5261343
Kurtosis5266.6955
Mean252989.83
Median Absolute Deviation (MAD)29360
Skewness64.153622
Sum2.5298983 × 109
Variance5.8081726 × 1012
MonotonicityNot monotonic
2024-05-11T02:35:18.532907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30000 537
 
5.4%
50000 514
 
5.1%
100000 488
 
4.9%
70000 160
 
1.6%
60000 158
 
1.6%
150000 149
 
1.5%
80000 141
 
1.4%
90000 136
 
1.4%
20000 113
 
1.1%
200000 107
 
1.1%
Other values (3124) 7497
75.0%
ValueCountFrequency (%)
-10250000 1
< 0.1%
-4487000 1
< 0.1%
-2700000 1
< 0.1%
-2693000 1
< 0.1%
-1553862 1
< 0.1%
-727650 1
< 0.1%
-450000 1
< 0.1%
-394580 1
< 0.1%
-300000 1
< 0.1%
-272540 1
< 0.1%
ValueCountFrequency (%)
205204809 1
< 0.1%
43968100 1
< 0.1%
39780000 1
< 0.1%
38208218 1
< 0.1%
36812400 1
< 0.1%
23884312 1
< 0.1%
23675120 1
< 0.1%
22151000 1
< 0.1%
20962210 1
< 0.1%
20585826 1
< 0.1%

내용
Text

Distinct5630
Distinct (%)56.3%
Missing4
Missing (%)< 0.1%
Memory size156.2 KiB
2024-05-11T02:35:19.246612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length93
Median length83
Mean length13.734994
Min length2

Characters and Unicode

Total characters137295
Distinct characters733
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5376 ?
Unique (%)53.8%

Sample

1st row6월분 일자리안정자금 입금(지원센터)
2nd row7월분 알뜰시장 수입 입금
3rd row분산상가7월 관리비- 성우부동산475-8949
4th row관리비 연체료 수납
5th row관리비 연체료 수납
ValueCountFrequency (%)
관리비 3850
 
14.7%
연체료 3716
 
14.2%
수납 3713
 
14.2%
6월분 353
 
1.3%
7월분 330
 
1.3%
승강기 251
 
1.0%
입금 240
 
0.9%
224
 
0.9%
승강기사용료 200
 
0.8%
사용료 180
 
0.7%
Other values (7057) 13093
50.1%
2024-05-11T02:35:20.860291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16282
 
11.9%
료 5623
 
4.1%
리 5139
 
3.7%
수 5057
 
3.7%
0 4865
 
3.5%
비 4703
 
3.4%
관 4060
 
3.0%
체 3911
 
2.8%
1 3847
 
2.8%
납 3844
 
2.8%
Other values (723) 79964
58.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89407
65.1%
Decimal Number 19808
 
14.4%
Space Separator 16282
 
11.9%
Close Punctuation 3022
 
2.2%
Open Punctuation 3014
 
2.2%
Other Punctuation 2449
 
1.8%
Dash Punctuation 2192
 
1.6%
Uppercase Letter 615
 
0.4%
Math Symbol 333
 
0.2%
Lowercase Letter 117
 
0.1%
Other values (3) 56
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
료 5623
 
6.3%
리 5139
 
5.7%
수 5057
 
5.7%
비 4703
 
5.3%
관 4060
 
4.5%
체 3911
 
4.4%
납 3844
 
4.3%
연 3786
 
4.2%
월 1870
 
2.1%
금 1726
 
1.9%
Other values (635) 49688
55.6%
Uppercase Letter
ValueCountFrequency (%)
N 79
12.8%
T 55
 
8.9%
K 50
 
8.1%
B 48
 
7.8%
O 44
 
7.2%
L 36
 
5.9%
C 32
 
5.2%
G 31
 
5.0%
E 30
 
4.9%
S 30
 
4.9%
Other values (15) 180
29.3%
Lowercase Letter
ValueCountFrequency (%)
o 45
38.5%
n 10
 
8.5%
x 8
 
6.8%
k 8
 
6.8%
t 6
 
5.1%
f 5
 
4.3%
c 5
 
4.3%
e 4
 
3.4%
s 4
 
3.4%
r 4
 
3.4%
Other values (10) 18
 
15.4%
Other Punctuation
ValueCountFrequency (%)
/ 689
28.1%
. 651
26.6%
, 635
25.9%
: 198
 
8.1%
* 142
 
5.8%
@ 42
 
1.7%
? 30
 
1.2%
% 28
 
1.1%
# 11
 
0.4%
& 8
 
0.3%
Other values (4) 15
 
0.6%
Decimal Number
ValueCountFrequency (%)
0 4865
24.6%
1 3847
19.4%
2 2722
13.7%
7 1751
 
8.8%
6 1705
 
8.6%
3 1358
 
6.9%
4 1091
 
5.5%
5 1041
 
5.3%
8 802
 
4.0%
9 626
 
3.2%
Math Symbol
ValueCountFrequency (%)
~ 265
79.6%
> 19
 
5.7%
< 13
 
3.9%
+ 12
 
3.6%
× 11
 
3.3%
= 10
 
3.0%
⇒ 2
 
0.6%
→ 1
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 2955
97.8%
] 65
 
2.2%
} 2
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 2946
97.7%
[ 66
 
2.2%
{ 2
 
0.1%
Space Separator
ValueCountFrequency (%)
16282
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2192
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 54
100.0%
Other Symbol
ValueCountFrequency (%)
▲ 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89403
65.1%
Common 47156
34.3%
Latin 732
 
0.5%
Han 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
료 5623
 
6.3%
리 5139
 
5.7%
수 5057
 
5.7%
비 4703
 
5.3%
관 4060
 
4.5%
체 3911
 
4.4%
납 3844
 
4.3%
연 3786
 
4.2%
월 1870
 
2.1%
금 1726
 
1.9%
Other values (633) 49684
55.6%
Latin
ValueCountFrequency (%)
N 79
 
10.8%
T 55
 
7.5%
K 50
 
6.8%
B 48
 
6.6%
o 45
 
6.1%
O 44
 
6.0%
L 36
 
4.9%
C 32
 
4.4%
G 31
 
4.2%
E 30
 
4.1%
Other values (35) 282
38.5%
Common
ValueCountFrequency (%)
16282
34.5%
0 4865
 
10.3%
1 3847
 
8.2%
) 2955
 
6.3%
( 2946
 
6.2%
2 2722
 
5.8%
- 2192
 
4.6%
7 1751
 
3.7%
6 1705
 
3.6%
3 1358
 
2.9%
Other values (33) 6533
13.9%
Han
ValueCountFrequency (%)
入 3
75.0%
不 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89403
65.1%
ASCII 47872
34.9%
None 12
 
< 0.1%
CJK 3
 
< 0.1%
Arrows 3
 
< 0.1%
Geometric Shapes 1
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16282
34.0%
0 4865
 
10.2%
1 3847
 
8.0%
) 2955
 
6.2%
( 2946
 
6.2%
2 2722
 
5.7%
- 2192
 
4.6%
7 1751
 
3.7%
6 1705
 
3.6%
3 1358
 
2.8%
Other values (73) 7249
15.1%
Hangul
ValueCountFrequency (%)
료 5623
 
6.3%
리 5139
 
5.7%
수 5057
 
5.7%
비 4703
 
5.3%
관 4060
 
4.5%
체 3911
 
4.4%
납 3844
 
4.3%
연 3786
 
4.2%
월 1870
 
2.1%
금 1726
 
1.9%
Other values (633) 49684
55.6%
None
ValueCountFrequency (%)
× 11
91.7%
＠ 1
 
8.3%
CJK
ValueCountFrequency (%)
入 3
100.0%
Arrows
ValueCountFrequency (%)
⇒ 2
66.7%
→ 1
33.3%
Geometric Shapes
ValueCountFrequency (%)
▲ 1
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
不 1
100.0%

Interactions

2024-05-11T02:35:11.071498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:35:10.468588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:35:11.433600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:35:10.824445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T02:35:21.225238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비용명년월일금액
비용명1.0000.3930.120
년월일0.3931.0000.000
금액0.1200.0001.000
2024-05-11T02:35:21.659266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월일금액비용명
년월일1.000-0.0060.156
금액-0.0061.0000.068
비용명0.1560.0681.000

Missing values

2024-05-11T02:35:11.786789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T02:35:12.249542image/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
60199목동롯데캐슬위너A15805303고용안정사업수익20200715900006월분 일자리안정자금 입금(지원센터)
41029중계라이프신동아청구아파트A13986111알뜰시장수익202007033775007월분 알뜰시장 수입 입금
23764둔촌현대4차A13481802잡수익2020072020000분산상가7월 관리비- 성우부동산475-8949
48138신길남서울A15085805연체료수익202007204460관리비 연체료 수납
17897쌍문한양1차A13203303연체료수익202007318773관리비 연체료 수납
22666성내성안청구A13403002주차장수익20200720280000외부주차수입/차정권 등4명/8월분 주차료
27305삼성홍실A13586602연체료수익202007156800관리비 연체료 수납
57892마곡수명산파크4단지A15728006검침수익20200724382140한전 검침 수익
4639돈암코오롱하늘채아파트A10027227기타운영수익2020073117466507월분 독서실 이용료 부과액
4289경희궁자이2단지 아파트A10027118잡수익202007205431기업은행 결산이자 발생
아파트명아파트코드비용명년월일금액내용
37635중계주공8단지A13922111부과차익2020072026906월부과차익
2550반포래미안아이파크A10026051광고료수익20200724100000윤남진 펜싱클럽
11895창전현대홈타운A12188202이자수익20200723813체크카드입금(기업은행)
33128양재우성A13789203승강기수익2020070250000101-705 승강기 사용료
37654중계주공5단지A13922114검침수익20200702100103620.06월분 전기검침수당
27936푸른마을아파트A13594203연체료수익202007311960관리비 연체료 수납
4385금천롯데캐슬골드파크1차아파트A10027188연체료수익202007152980관리비 연체료 수납
5649북한산 푸르지오 아파트A10027816연체료수익202007261090관리비 연체료 수납
62307은평뉴타운상림마을6단지A41279904연체료수익2020072712310관리비 연체료 수납
13991휘경주공1단지A13009002연체료수익20200714710관리비 연체료 수납