Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells6
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 = 31.00940936)Skewed

Reproduction

Analysis started2024-05-11 02:35:42.802143
Analysis finished2024-05-11 02:35:47.570634
Duration4.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

Distinct2131
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:35:48.030010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length7.2339
Min length2

Characters and Unicode

Total characters72339
Distinct characters431
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

Unique211 ?
Unique (%)2.1%

Sample

1st row길음뉴타운푸르지오아파트2,3단지
2nd row수유극동아파트
3rd row답십리대림
4th row신정학마을3단지
5th row하계미성
ValueCountFrequency (%)
아파트 118
 
1.1%
래미안 48
 
0.5%
신내 24
 
0.2%
고덕 23
 
0.2%
2단지 21
 
0.2%
아이파크 20
 
0.2%
힐스테이트 20
 
0.2%
마포래미안푸르지오 20
 
0.2%
잠실동트리지움 18
 
0.2%
잠실리센츠 17
 
0.2%
Other values (2194) 10317
96.9%
2024-05-11T02:35:49.193298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 2437
 
3.4%
파 2330
 
3.2%
트 2216
 
3.1%
지 1979
 
2.7%
대 1783
 
2.5%
단 1586
 
2.2%
동 1545
 
2.1%
신 1454
 
2.0%
이 1382
 
1.9%
차 1370
 
1.9%
Other values (421) 54257
75.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66190
91.5%
Decimal Number 3780
 
5.2%
Space Separator 746
 
1.0%
Uppercase Letter 737
 
1.0%
Lowercase Letter 322
 
0.4%
Other Punctuation 146
 
0.2%
Open Punctuation 144
 
0.2%
Close Punctuation 144
 
0.2%
Dash Punctuation 118
 
0.2%
Letter Number 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 2437
 
3.7%
파 2330
 
3.5%
트 2216
 
3.3%
지 1979
 
3.0%
대 1783
 
2.7%
단 1586
 
2.4%
동 1545
 
2.3%
신 1454
 
2.2%
이 1382
 
2.1%
차 1370
 
2.1%
Other values (375) 48108
72.7%
Uppercase Letter
ValueCountFrequency (%)
S 134
18.2%
K 99
13.4%
C 94
12.8%
D 70
9.5%
M 70
9.5%
H 54
7.3%
L 49
 
6.6%
I 32
 
4.3%
G 26
 
3.5%
E 26
 
3.5%
Other values (7) 83
11.3%
Lowercase Letter
ValueCountFrequency (%)
e 176
54.7%
l 42
 
13.0%
i 30
 
9.3%
v 22
 
6.8%
s 17
 
5.3%
k 14
 
4.3%
w 6
 
1.9%
h 5
 
1.6%
c 4
 
1.2%
a 3
 
0.9%
Decimal Number
ValueCountFrequency (%)
2 1091
28.9%
1 1087
28.8%
3 479
12.7%
4 250
 
6.6%
5 242
 
6.4%
6 191
 
5.1%
7 141
 
3.7%
9 113
 
3.0%
0 95
 
2.5%
8 91
 
2.4%
Other Punctuation
ValueCountFrequency (%)
, 127
87.0%
. 19
 
13.0%
Space Separator
ValueCountFrequency (%)
746
100.0%
Open Punctuation
ValueCountFrequency (%)
( 144
100.0%
Close Punctuation
ValueCountFrequency (%)
) 144
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 118
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 7
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66190
91.5%
Common 5083
 
7.0%
Latin 1066
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 2437
 
3.7%
파 2330
 
3.5%
트 2216
 
3.3%
지 1979
 
3.0%
대 1783
 
2.7%
단 1586
 
2.4%
동 1545
 
2.3%
신 1454
 
2.2%
이 1382
 
2.1%
차 1370
 
2.1%
Other values (375) 48108
72.7%
Latin
ValueCountFrequency (%)
e 176
16.5%
S 134
12.6%
K 99
9.3%
C 94
 
8.8%
D 70
 
6.6%
M 70
 
6.6%
H 54
 
5.1%
L 49
 
4.6%
l 42
 
3.9%
I 32
 
3.0%
Other values (19) 246
23.1%
Common
ValueCountFrequency (%)
2 1091
21.5%
1 1087
21.4%
746
14.7%
3 479
9.4%
4 250
 
4.9%
5 242
 
4.8%
6 191
 
3.8%
( 144
 
2.8%
) 144
 
2.8%
7 141
 
2.8%
Other values (7) 568
11.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66190
91.5%
ASCII 6142
 
8.5%
Number Forms 7
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 2437
 
3.7%
파 2330
 
3.5%
트 2216
 
3.3%
지 1979
 
3.0%
대 1783
 
2.7%
단 1586
 
2.4%
동 1545
 
2.3%
신 1454
 
2.2%
이 1382
 
2.1%
차 1370
 
2.1%
Other values (375) 48108
72.7%
ASCII
ValueCountFrequency (%)
2 1091
17.8%
1 1087
17.7%
746
12.1%
3 479
 
7.8%
4 250
 
4.1%
5 242
 
3.9%
6 191
 
3.1%
e 176
 
2.9%
( 144
 
2.3%
) 144
 
2.3%
Other values (35) 1592
25.9%
Number Forms
ValueCountFrequency (%)
Ⅰ 7
100.0%

아파트코드
Text

Distinct2135
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:35:49.934354image/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

Unique212 ?
Unique (%)2.1%

Sample

1st rowA13611007
2nd rowA14278101
3rd rowA13080801
4th rowA15886501
5th rowA13923104
ValueCountFrequency (%)
a12175203 20
 
0.2%
a13822002 18
 
0.2%
a13822003 17
 
0.2%
a14272304 17
 
0.2%
a13822004 16
 
0.2%
a14272305 16
 
0.2%
a12085303 16
 
0.2%
a13986306 15
 
0.1%
a11034001 15
 
0.1%
a15609306 15
 
0.1%
Other values (2125) 9835
98.4%
2024-05-11T02:35:50.985562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18555
20.6%
1 17317
19.2%
A 9987
11.1%
3 8862
9.8%
2 8253
9.2%
5 6282
 
7.0%
8 5598
 
6.2%
7 4927
 
5.5%
4 3740
 
4.2%
6 3518
 
3.9%
Other values (2) 2961
 
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 18555
23.2%
1 17317
21.6%
3 8862
11.1%
2 8253
10.3%
5 6282
 
7.9%
8 5598
 
7.0%
7 4927
 
6.2%
4 3740
 
4.7%
6 3518
 
4.4%
9 2948
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
A 9987
99.9%
B 13
 
0.1%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
0 18555
23.2%
1 17317
21.6%
3 8862
11.1%
2 8253
10.3%
5 6282
 
7.9%
8 5598
 
7.0%
7 4927
 
6.2%
4 3740
 
4.7%
6 3518
 
4.4%
9 2948
 
3.7%
Latin
ValueCountFrequency (%)
A 9987
99.9%
B 13
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18555
20.6%
1 17317
19.2%
A 9987
11.1%
3 8862
9.8%
2 8253
9.2%
5 6282
 
7.0%
8 5598
 
6.2%
7 4927
 
5.5%
4 3740
 
4.2%
6 3518
 
3.9%
Other values (2) 2961
 
3.3%

비용명
Categorical

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
연체료수익
3899 
승강기수익
1012 
주차장수익
893 
잡수익
866 
광고료수익
801 
Other values (10)
2529 

Length

Max length9
Median length5
Mean length5.0613
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
연체료수익 3899
39.0%
승강기수익 1012
 
10.1%
주차장수익 893
 
8.9%
잡수익 866
 
8.7%
광고료수익 801
 
8.0%
기타운영수익 628
 
6.3%
고용안정사업수익 524
 
5.2%
검침수익 313
 
3.1%
알뜰시장수익 248
 
2.5%
부과차익 217
 
2.2%
Other values (5) 599
 
6.0%

Length

2024-05-11T02:35:51.396985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
연체료수익 3899
39.0%
승강기수익 1012
 
10.1%
주차장수익 893
 
8.9%
잡수익 866
 
8.7%
광고료수익 801
 
8.0%
기타운영수익 628
 
6.3%
고용안정사업수익 524
 
5.2%
검침수익 313
 
3.1%
알뜰시장수익 248
 
2.5%
부과차익 217
 
2.2%
Other values (5) 599
 
6.0%

년월일
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20200518
Minimum20200501
Maximum20200531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:35:51.881874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200501
5-th percentile20200503
Q120200510
median20200519
Q320200526
95-th percentile20200531
Maximum20200531
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.2029323
Coefficient of variation (CV)4.5557903 × 10-7
Kurtosis-1.274196
Mean20200518
Median Absolute Deviation (MAD)8
Skewness-0.17593659
Sum2.0200518 × 1011
Variance84.693962
MonotonicityNot monotonic
2024-05-11T02:35:52.312676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20200529 669
 
6.7%
20200531 662
 
6.6%
20200504 630
 
6.3%
20200525 526
 
5.3%
20200511 497
 
5.0%
20200515 452
 
4.5%
20200520 446
 
4.5%
20200506 439
 
4.4%
20200527 435
 
4.3%
20200528 430
 
4.3%
Other values (21) 4814
48.1%
ValueCountFrequency (%)
20200501 235
 
2.4%
20200502 131
 
1.3%
20200503 140
 
1.4%
20200504 630
6.3%
20200505 127
 
1.3%
20200506 439
4.4%
20200507 347
3.5%
20200508 362
3.6%
20200509 73
 
0.7%
20200510 67
 
0.7%
ValueCountFrequency (%)
20200531 662
6.6%
20200530 179
 
1.8%
20200529 669
6.7%
20200528 430
4.3%
20200527 435
4.3%
20200526 392
3.9%
20200525 526
5.3%
20200524 129
 
1.3%
20200523 111
 
1.1%
20200522 413
4.1%

금액
Real number (ℝ)

SKEWED 

Distinct3315
Distinct (%)33.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean270978.58
Minimum-30000000
Maximum1 × 108
Zeros17
Zeros (%)0.2%
Negative44
Negative (%)0.4%
Memory size166.0 KiB
2024-05-11T02:35:52.740265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-30000000
5-th percentile140
Q12780
median30000
Q3100000
95-th percentile1170000
Maximum1 × 108
Range1.3 × 108
Interquartile range (IQR)97220

Descriptive statistics

Standard deviation1655333.1
Coefficient of variation (CV)6.1087232
Kurtosis1590.9031
Mean270978.58
Median Absolute Deviation (MAD)29210
Skewness31.009409
Sum2.7097858 × 109
Variance2.7401278 × 1012
MonotonicityNot monotonic
2024-05-11T02:35:53.181014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30000 548
 
5.5%
50000 510
 
5.1%
100000 453
 
4.5%
70000 151
 
1.5%
150000 141
 
1.4%
60000 132
 
1.3%
200000 124
 
1.2%
80000 104
 
1.0%
20000 102
 
1.0%
40000 94
 
0.9%
Other values (3305) 7641
76.4%
ValueCountFrequency (%)
-30000000 1
< 0.1%
-5454545 1
< 0.1%
-3600000 1
< 0.1%
-3354545 1
< 0.1%
-2359822 1
< 0.1%
-1910000 1
< 0.1%
-871500 1
< 0.1%
-590000 1
< 0.1%
-504000 1
< 0.1%
-468000 1
< 0.1%
ValueCountFrequency (%)
100000000 1
< 0.1%
60399180 1
< 0.1%
46187874 1
< 0.1%
26460000 1
< 0.1%
25302500 1
< 0.1%
25102800 1
< 0.1%
22229045 1
< 0.1%
20219546 1
< 0.1%
19777930 1
< 0.1%
16885680 1
< 0.1%

내용
Text

Distinct5453
Distinct (%)54.6%
Missing6
Missing (%)0.1%
Memory size156.2 KiB
2024-05-11T02:35:53.959145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length86
Median length75
Mean length13.751551
Min length2

Characters and Unicode

Total characters137433
Distinct characters728
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

Unique5227 ?
Unique (%)52.3%

Sample

1st row213-1002 승강기사용료
2nd row재활용 3월분
3rd row외부주차비 1731,3067(5/10~8/9),0633(5/17~8/16)
4th row관리비 연체료 수납
5th row에어로빅(오전반)-권해숙
ValueCountFrequency (%)
관리비 4046
 
15.3%
수납 3906
 
14.8%
연체료 3904
 
14.8%
5월분 349
 
1.3%
4월분 286
 
1.1%
승강기 248
 
0.9%
입금 236
 
0.9%
승강기사용료 234
 
0.9%
222
 
0.8%
5월 207
 
0.8%
Other values (6851) 12731
48.3%
2024-05-11T02:35:55.356209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16465
 
12.0%
료 5833
 
4.2%
리 5254
 
3.8%
수 5236
 
3.8%
비 4878
 
3.5%
0 4749
 
3.5%
관 4281
 
3.1%
체 4084
 
3.0%
연 3948
 
2.9%
납 3944
 
2.9%
Other values (718) 78761
57.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89942
65.4%
Decimal Number 19486
 
14.2%
Space Separator 16465
 
12.0%
Close Punctuation 2932
 
2.1%
Open Punctuation 2915
 
2.1%
Other Punctuation 2573
 
1.9%
Dash Punctuation 2051
 
1.5%
Uppercase Letter 556
 
0.4%
Math Symbol 327
 
0.2%
Lowercase Letter 133
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
료 5833
 
6.5%
리 5254
 
5.8%
수 5236
 
5.8%
비 4878
 
5.4%
관 4281
 
4.8%
체 4084
 
4.5%
연 3948
 
4.4%
납 3944
 
4.4%
월 1980
 
2.2%
금 1776
 
2.0%
Other values (632) 48728
54.2%
Uppercase Letter
ValueCountFrequency (%)
N 69
12.4%
A 43
 
7.7%
T 43
 
7.7%
C 41
 
7.4%
K 33
 
5.9%
O 33
 
5.9%
B 33
 
5.9%
S 32
 
5.8%
L 32
 
5.8%
E 28
 
5.0%
Other values (14) 169
30.4%
Lowercase Letter
ValueCountFrequency (%)
o 43
32.3%
x 21
15.8%
n 18
13.5%
k 9
 
6.8%
a 6
 
4.5%
e 6
 
4.5%
t 5
 
3.8%
s 5
 
3.8%
v 3
 
2.3%
u 2
 
1.5%
Other values (13) 15
 
11.3%
Other Punctuation
ValueCountFrequency (%)
, 797
31.0%
/ 689
26.8%
. 634
24.6%
: 193
 
7.5%
* 161
 
6.3%
@ 42
 
1.6%
% 18
 
0.7%
? 14
 
0.5%
& 7
 
0.3%
# 7
 
0.3%
Other values (5) 11
 
0.4%
Decimal Number
ValueCountFrequency (%)
0 4749
24.4%
1 3898
20.0%
2 2613
13.4%
5 2076
10.7%
4 1934
9.9%
3 1501
 
7.7%
6 886
 
4.5%
8 649
 
3.3%
7 607
 
3.1%
9 573
 
2.9%
Math Symbol
ValueCountFrequency (%)
~ 248
75.8%
+ 25
 
7.6%
> 16
 
4.9%
× 14
 
4.3%
< 11
 
3.4%
= 11
 
3.4%
÷ 2
 
0.6%
Close Punctuation
ValueCountFrequency (%)
) 2867
97.8%
] 65
 
2.2%
Open Punctuation
ValueCountFrequency (%)
( 2852
97.8%
[ 63
 
2.2%
Space Separator
ValueCountFrequency (%)
16465
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2051
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89942
65.4%
Common 46802
34.1%
Latin 689
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
료 5833
 
6.5%
리 5254
 
5.8%
수 5236
 
5.8%
비 4878
 
5.4%
관 4281
 
4.8%
체 4084
 
4.5%
연 3948
 
4.4%
납 3944
 
4.4%
월 1980
 
2.2%
금 1776
 
2.0%
Other values (632) 48728
54.2%
Latin
ValueCountFrequency (%)
N 69
 
10.0%
A 43
 
6.2%
T 43
 
6.2%
o 43
 
6.2%
C 41
 
6.0%
K 33
 
4.8%
O 33
 
4.8%
B 33
 
4.8%
S 32
 
4.6%
L 32
 
4.6%
Other values (37) 287
41.7%
Common
ValueCountFrequency (%)
16465
35.2%
0 4749
 
10.1%
1 3898
 
8.3%
) 2867
 
6.1%
( 2852
 
6.1%
2 2613
 
5.6%
5 2076
 
4.4%
- 2051
 
4.4%
4 1934
 
4.1%
3 1501
 
3.2%
Other values (29) 5796
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89942
65.4%
ASCII 47474
34.5%
None 17
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16465
34.7%
0 4749
 
10.0%
1 3898
 
8.2%
) 2867
 
6.0%
( 2852
 
6.0%
2 2613
 
5.5%
5 2076
 
4.4%
- 2051
 
4.3%
4 1934
 
4.1%
3 1501
 
3.2%
Other values (73) 6468
 
13.6%
Hangul
ValueCountFrequency (%)
료 5833
 
6.5%
리 5254
 
5.8%
수 5236
 
5.8%
비 4878
 
5.4%
관 4281
 
4.8%
체 4084
 
4.5%
연 3948
 
4.4%
납 3944
 
4.4%
월 1980
 
2.2%
금 1776
 
2.0%
Other values (632) 48728
54.2%
None
ValueCountFrequency (%)
× 14
82.4%
÷ 2
 
11.8%
？ 1
 
5.9%

Interactions

2024-05-11T02:35:46.044857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:35:45.241590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:35:46.356997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:35:45.736835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T02:35:55.645028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비용명년월일금액
비용명1.0000.3700.136
년월일0.3701.0000.022
금액0.1360.0221.000
2024-05-11T02:35:56.175582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월일금액비용명
년월일1.0000.0700.148
금액0.0701.0000.067
비용명0.1480.0671.000

Missing values

2024-05-11T02:35:46.836621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T02:35:47.366369image/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
28874길음뉴타운푸르지오아파트2,3단지A13611007승강기수익20200519100000213-1002 승강기사용료
43701수유극동아파트A14278101잡수익20200518250000재활용 3월분
14577답십리대림A13080801주차장수익20200511540000외부주차비 1731,3067(5/10~8/9),0633(5/17~8/16)
61166신정학마을3단지A15886501연체료수익202005271010관리비 연체료 수납
37590하계미성A13923104임대료수익2020052760000에어로빅(오전반)-권해숙
58342방화5단지A15785613연체료수익202005162440관리비 연체료 수납
51132고척우성꿈동산A15283702재활용품수익20200526816005월분 재활용품 판매대금(대웅자원)
8447홍은두산A12071001검침수익20200518126420검침수당청구
27019래미안도곡카운티A13585404광고료수익2020050740000게시판광고료-개인별맞춤수학
14621삼익A13082501잡수익20200515350101-1303호음식물카드(1개)
아파트명아파트코드비용명년월일금액내용
16515신내건영2차아파트A13185607알뜰시장수익2020052630000일일장 - 귀금속
60504목동1단지A15875101연체료수익20200524330관리비 연체료 수납
35331송파삼성래미안A13877501알뜰시장수익20200531400000알뜰장 발전기금
37675공릉한보아파트A13924003연체료수익202005071220관리비 연체료 수납
24214아크로힐스논현A13501006연체료수익202005301030관리비 연체료 수납
53584상도래미안1차제2A15603007주차장수익2020050830000세대주차료(806호)
31377서초이오빌A13770611연체료수익20200515500관리비 연체료 수납
4081금천롯데캐슬골드파크1차아파트A10027188연체료수익2020053030관리비 연체료 수납
30654잠원롯데캐슬갤럭시1차A13703011연체료수익202005035070관리비 연체료 수납
25545수서까치마을A13522007고용안정사업수익2020052549310일자리안정자금지원 수입 : 경비 양성민 2/19 입사 2월분 130,000원/29*11