Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells10
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

Reproduction

Analysis started2024-05-11 02:39:33.346734
Analysis finished2024-05-11 02:39:37.807212
Duration4.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

Distinct2062
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:39:38.367300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length7.1805
Min length2

Characters and Unicode

Total characters71805
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

Unique167 ?
Unique (%)1.7%

Sample

1st row서울숲리버뷰자이아파트
2nd row강서힐스테이트아파트
3rd row창동성원
4th row신내성원
5th row상암휴먼시아1단지
ValueCountFrequency (%)
아파트 103
 
1.0%
래미안 37
 
0.4%
입주자대표회의 29
 
0.3%
상계주공7단지 24
 
0.2%
목동7단지 19
 
0.2%
목동14단지 19
 
0.2%
마포래미안푸르지오 19
 
0.2%
수서신동아 18
 
0.2%
sk북한산시티아파트 17
 
0.2%
신림현대 17
 
0.2%
Other values (2115) 10194
97.1%
2024-05-11T02:39:39.754854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 2185
 
3.0%
파 2157
 
3.0%
지 2116
 
2.9%
트 1979
 
2.8%
대 1780
 
2.5%
단 1704
 
2.4%
동 1622
 
2.3%
차 1500
 
2.1%
신 1443
 
2.0%
성 1316
 
1.8%
Other values (419) 54003
75.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 65650
91.4%
Decimal Number 4095
 
5.7%
Uppercase Letter 739
 
1.0%
Space Separator 541
 
0.8%
Lowercase Letter 261
 
0.4%
Other Punctuation 157
 
0.2%
Close Punctuation 119
 
0.2%
Open Punctuation 119
 
0.2%
Dash Punctuation 114
 
0.2%
Math Symbol 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 2185
 
3.3%
파 2157
 
3.3%
지 2116
 
3.2%
트 1979
 
3.0%
대 1780
 
2.7%
단 1704
 
2.6%
동 1622
 
2.5%
차 1500
 
2.3%
신 1443
 
2.2%
성 1316
 
2.0%
Other values (373) 47848
72.9%
Uppercase Letter
ValueCountFrequency (%)
S 139
18.8%
K 117
15.8%
C 82
11.1%
M 52
 
7.0%
D 52
 
7.0%
L 44
 
6.0%
H 42
 
5.7%
I 38
 
5.1%
E 36
 
4.9%
A 30
 
4.1%
Other values (7) 107
14.5%
Lowercase Letter
ValueCountFrequency (%)
e 157
60.2%
l 30
 
11.5%
i 21
 
8.0%
v 16
 
6.1%
s 8
 
3.1%
c 8
 
3.1%
h 7
 
2.7%
k 5
 
1.9%
g 3
 
1.1%
a 3
 
1.1%
Decimal Number
ValueCountFrequency (%)
1 1210
29.5%
2 1207
29.5%
3 502
12.3%
4 284
 
6.9%
5 250
 
6.1%
6 193
 
4.7%
7 142
 
3.5%
9 122
 
3.0%
8 97
 
2.4%
0 88
 
2.1%
Other Punctuation
ValueCountFrequency (%)
, 139
88.5%
. 18
 
11.5%
Space Separator
ValueCountFrequency (%)
541
100.0%
Close Punctuation
ValueCountFrequency (%)
) 119
100.0%
Open Punctuation
ValueCountFrequency (%)
( 119
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 114
100.0%
Math Symbol
ValueCountFrequency (%)
~ 9
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 65650
91.4%
Common 5154
 
7.2%
Latin 1001
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 2185
 
3.3%
파 2157
 
3.3%
지 2116
 
3.2%
트 1979
 
3.0%
대 1780
 
2.7%
단 1704
 
2.6%
동 1622
 
2.5%
차 1500
 
2.3%
신 1443
 
2.2%
성 1316
 
2.0%
Other values (373) 47848
72.9%
Latin
ValueCountFrequency (%)
e 157
15.7%
S 139
13.9%
K 117
11.7%
C 82
 
8.2%
M 52
 
5.2%
D 52
 
5.2%
L 44
 
4.4%
H 42
 
4.2%
I 38
 
3.8%
E 36
 
3.6%
Other values (19) 242
24.2%
Common
ValueCountFrequency (%)
1 1210
23.5%
2 1207
23.4%
541
10.5%
3 502
9.7%
4 284
 
5.5%
5 250
 
4.9%
6 193
 
3.7%
7 142
 
2.8%
, 139
 
2.7%
9 122
 
2.4%
Other values (7) 564
10.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 65650
91.4%
ASCII 6154
 
8.6%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 2185
 
3.3%
파 2157
 
3.3%
지 2116
 
3.2%
트 1979
 
3.0%
대 1780
 
2.7%
단 1704
 
2.6%
동 1622
 
2.5%
차 1500
 
2.3%
신 1443
 
2.2%
성 1316
 
2.0%
Other values (373) 47848
72.9%
ASCII
ValueCountFrequency (%)
1 1210
19.7%
2 1207
19.6%
541
 
8.8%
3 502
 
8.2%
4 284
 
4.6%
5 250
 
4.1%
6 193
 
3.1%
e 157
 
2.6%
7 142
 
2.3%
, 139
 
2.3%
Other values (35) 1529
24.8%
Number Forms
ValueCountFrequency (%)
Ⅰ 1
100.0%

아파트코드
Text

Distinct2067
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:39:41.037620image/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

Unique167 ?
Unique (%)1.7%

Sample

1st rowA10026207
2nd rowA15701007
3rd rowA13292701
4th rowA13187004
5th rowA12179502
ValueCountFrequency (%)
a13982704 24
 
0.2%
a15807606 19
 
0.2%
a12175203 19
 
0.2%
a15805115 19
 
0.2%
a13522006 18
 
0.2%
a13820201 17
 
0.2%
a12170603 17
 
0.2%
a14272304 17
 
0.2%
a15101508 17
 
0.2%
a12179004 17
 
0.2%
Other values (2057) 9816
98.2%
2024-05-11T02:39:42.673465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18351
20.4%
1 17320
19.2%
A 9990
11.1%
3 9114
10.1%
2 8059
9.0%
5 6288
 
7.0%
8 5797
 
6.4%
7 4923
 
5.5%
4 3805
 
4.2%
6 3378
 
3.8%
Other values (2) 2975
 
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 17320
21.6%
3 9114
11.4%
2 8059
10.1%
5 6288
 
7.9%
8 5797
 
7.2%
7 4923
 
6.2%
4 3805
 
4.8%
6 3378
 
4.2%
9 2965
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
A 9990
99.9%
B 10
 
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 17320
21.6%
3 9114
11.4%
2 8059
10.1%
5 6288
 
7.9%
8 5797
 
7.2%
7 4923
 
6.2%
4 3805
 
4.8%
6 3378
 
4.2%
9 2965
 
3.7%
Latin
ValueCountFrequency (%)
A 9990
99.9%
B 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18351
20.4%
1 17320
19.2%
A 9990
11.1%
3 9114
10.1%
2 8059
9.0%
5 6288
 
7.0%
8 5797
 
6.4%
7 4923
 
5.5%
4 3805
 
4.2%
6 3378
 
3.8%
Other values (2) 2975
 
3.3%

비용명
Categorical

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
연체료수익
3650 
잡수익
1091 
광고료수익
1074 
승강기수익
852 
주차장수익
748 
Other values (10)
2585 

Length

Max length9
Median length5
Mean length5.0286
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
연체료수익 3650
36.5%
잡수익 1091
 
10.9%
광고료수익 1074
 
10.7%
승강기수익 852
 
8.5%
주차장수익 748
 
7.5%
기타운영수익 702
 
7.0%
고용안정사업수익 516
 
5.2%
검침수익 295
 
2.9%
알뜰시장수익 244
 
2.4%
임대료수익 225
 
2.2%
Other values (5) 603
 
6.0%

Length

2024-05-11T02:39:43.350111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
연체료수익 3650
36.5%
잡수익 1091
 
10.9%
광고료수익 1074
 
10.7%
승강기수익 852
 
8.5%
주차장수익 748
 
7.5%
기타운영수익 702
 
7.0%
고용안정사업수익 516
 
5.2%
검침수익 295
 
2.9%
알뜰시장수익 244
 
2.4%
임대료수익 225
 
2.2%
Other values (5) 603
 
6.0%

년월일
Real number (ℝ)

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190416
Minimum20190401
Maximum20190430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:39:43.944877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190401
5-th percentile20190401
Q120190408
median20190417
Q320190425
95-th percentile20190430
Maximum20190430
Range29
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.7101594
Coefficient of variation (CV)4.8092913 × 10-7
Kurtosis-1.3344277
Mean20190416
Median Absolute Deviation (MAD)8
Skewness-0.11962034
Sum2.0190416 × 1011
Variance94.287195
MonotonicityNot monotonic
2024-05-11T02:39:44.413298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20190430 1047
 
10.5%
20190401 626
 
6.3%
20190429 569
 
5.7%
20190415 543
 
5.4%
20190425 505
 
5.1%
20190410 436
 
4.4%
20190402 428
 
4.3%
20190424 426
 
4.3%
20190422 390
 
3.9%
20190417 380
 
3.8%
Other values (20) 4650
46.5%
ValueCountFrequency (%)
20190401 626
6.3%
20190402 428
4.3%
20190403 357
3.6%
20190404 356
3.6%
20190405 363
3.6%
20190406 89
 
0.9%
20190407 65
 
0.7%
20190408 375
3.8%
20190409 284
2.8%
20190410 436
4.4%
ValueCountFrequency (%)
20190430 1047
10.5%
20190429 569
5.7%
20190428 150
 
1.5%
20190427 145
 
1.5%
20190426 359
 
3.6%
20190425 505
5.1%
20190424 426
4.3%
20190423 366
 
3.7%
20190422 390
 
3.9%
20190421 50
 
0.5%

금액
Real number (ℝ)

Distinct3396
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254715.84
Minimum-4000000
Maximum53220000
Zeros16
Zeros (%)0.2%
Negative46
Negative (%)0.5%
Memory size166.0 KiB
2024-05-11T02:39:44.995608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4000000
5-th percentile180
Q13930
median30000
Q3100000
95-th percentile1080000
Maximum53220000
Range57220000
Interquartile range (IQR)96070

Descriptive statistics

Standard deviation1301924.7
Coefficient of variation (CV)5.1112829
Kurtosis547.49118
Mean254715.84
Median Absolute Deviation (MAD)28970
Skewness19.444691
Sum2.5471584 × 109
Variance1.6950079 × 1012
MonotonicityNot monotonic
2024-05-11T02:39:45.455030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30000 632
 
6.3%
50000 593
 
5.9%
100000 425
 
4.2%
70000 164
 
1.6%
60000 155
 
1.6%
80000 135
 
1.4%
40000 133
 
1.3%
20000 112
 
1.1%
150000 110
 
1.1%
200000 98
 
1.0%
Other values (3386) 7443
74.4%
ValueCountFrequency (%)
-4000000 1
< 0.1%
-1660000 1
< 0.1%
-1536750 1
< 0.1%
-1410010 1
< 0.1%
-708000 1
< 0.1%
-686120 1
< 0.1%
-573818 1
< 0.1%
-400000 1
< 0.1%
-360000 1
< 0.1%
-330000 1
< 0.1%
ValueCountFrequency (%)
53220000 1
< 0.1%
42155664 1
< 0.1%
31353740 1
< 0.1%
30000000 1
< 0.1%
28000000 1
< 0.1%
27421800 1
< 0.1%
26250000 1
< 0.1%
25196400 1
< 0.1%
24775933 1
< 0.1%
20020000 1
< 0.1%

내용
Text

Distinct5741
Distinct (%)57.5%
Missing10
Missing (%)0.1%
Memory size156.2 KiB
2024-05-11T02:39:46.485328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length78
Median length63
Mean length13.613614
Min length2

Characters and Unicode

Total characters136000
Distinct characters752
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

Unique5512 ?
Unique (%)55.2%

Sample

1st row관리비 연체료 수납
2nd row103-1303전출승강기사용료(4/16)
3rd row케이티
4th row4월분 재활용수수료
5th row상가 주차비(4대)
ValueCountFrequency (%)
관리비 3778
 
14.7%
연체료 3661
 
14.2%
수납 3658
 
14.2%
4월분 301
 
1.2%
3월분 301
 
1.2%
승강기 235
 
0.9%
4월 212
 
0.8%
입금 208
 
0.8%
194
 
0.8%
사용료 187
 
0.7%
Other values (7122) 12996
50.5%
2024-05-11T02:39:47.957335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15855
 
11.7%
료 5552
 
4.1%
수 5062
 
3.7%
리 4955
 
3.6%
비 4628
 
3.4%
1 4243
 
3.1%
관 3965
 
2.9%
0 3941
 
2.9%
체 3844
 
2.8%
납 3780
 
2.8%
Other values (742) 80175
59.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89887
66.1%
Decimal Number 18475
 
13.6%
Space Separator 15855
 
11.7%
Close Punctuation 3010
 
2.2%
Open Punctuation 3008
 
2.2%
Other Punctuation 2503
 
1.8%
Dash Punctuation 2032
 
1.5%
Uppercase Letter 704
 
0.5%
Math Symbol 286
 
0.2%
Lowercase Letter 173
 
0.1%
Other values (3) 67
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
료 5552
 
6.2%
수 5062
 
5.6%
리 4955
 
5.5%
비 4628
 
5.1%
관 3965
 
4.4%
체 3844
 
4.3%
납 3780
 
4.2%
연 3735
 
4.2%
월 1962
 
2.2%
금 1757
 
2.0%
Other values (650) 50647
56.3%
Uppercase Letter
ValueCountFrequency (%)
N 85
12.1%
K 58
 
8.2%
O 56
 
8.0%
L 52
 
7.4%
D 49
 
7.0%
G 44
 
6.2%
B 42
 
6.0%
T 42
 
6.0%
S 40
 
5.7%
E 40
 
5.7%
Other values (16) 196
27.8%
Lowercase Letter
ValueCountFrequency (%)
o 56
32.4%
n 24
13.9%
x 18
 
10.4%
k 13
 
7.5%
s 9
 
5.2%
t 9
 
5.2%
e 6
 
3.5%
b 5
 
2.9%
g 5
 
2.9%
c 5
 
2.9%
Other values (12) 23
13.3%
Other Punctuation
ValueCountFrequency (%)
/ 714
28.5%
, 708
28.3%
. 659
26.3%
: 210
 
8.4%
* 122
 
4.9%
@ 26
 
1.0%
? 19
 
0.8%
% 15
 
0.6%
# 10
 
0.4%
' 9
 
0.4%
Other values (5) 11
 
0.4%
Decimal Number
ValueCountFrequency (%)
1 4243
23.0%
0 3941
21.3%
2 2348
12.7%
4 2223
12.0%
3 2065
11.2%
5 955
 
5.2%
9 901
 
4.9%
6 689
 
3.7%
8 574
 
3.1%
7 536
 
2.9%
Math Symbol
ValueCountFrequency (%)
~ 232
81.1%
+ 21
 
7.3%
> 17
 
5.9%
< 8
 
2.8%
× 3
 
1.0%
= 3
 
1.0%
∼ 1
 
0.3%
⇒ 1
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 2940
97.7%
] 68
 
2.3%
} 2
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 2940
97.7%
[ 67
 
2.2%
{ 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
15855
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2032
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 65
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89882
66.1%
Common 45236
33.3%
Latin 877
 
0.6%
Han 5
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
료 5552
 
6.2%
수 5062
 
5.6%
리 4955
 
5.5%
비 4628
 
5.1%
관 3965
 
4.4%
체 3844
 
4.3%
납 3780
 
4.2%
연 3735
 
4.2%
월 1962
 
2.2%
금 1757
 
2.0%
Other values (647) 50642
56.3%
Latin
ValueCountFrequency (%)
N 85
 
9.7%
K 58
 
6.6%
o 56
 
6.4%
O 56
 
6.4%
L 52
 
5.9%
D 49
 
5.6%
G 44
 
5.0%
B 42
 
4.8%
T 42
 
4.8%
S 40
 
4.6%
Other values (38) 353
40.3%
Common
ValueCountFrequency (%)
15855
35.0%
1 4243
 
9.4%
0 3941
 
8.7%
) 2940
 
6.5%
( 2940
 
6.5%
2 2348
 
5.2%
4 2223
 
4.9%
3 2065
 
4.6%
- 2032
 
4.5%
5 955
 
2.1%
Other values (34) 5694
 
12.6%
Han
ValueCountFrequency (%)
入 2
40.0%
分 2
40.0%
人 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89882
66.1%
ASCII 46106
33.9%
CJK 5
 
< 0.1%
None 4
 
< 0.1%
Math Operators 1
 
< 0.1%
CJK Compat 1
 
< 0.1%
Arrows 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15855
34.4%
1 4243
 
9.2%
0 3941
 
8.5%
) 2940
 
6.4%
( 2940
 
6.4%
2 2348
 
5.1%
4 2223
 
4.8%
3 2065
 
4.5%
- 2032
 
4.4%
5 955
 
2.1%
Other values (77) 6564
14.2%
Hangul
ValueCountFrequency (%)
료 5552
 
6.2%
수 5062
 
5.6%
리 4955
 
5.5%
비 4628
 
5.1%
관 3965
 
4.4%
체 3844
 
4.3%
납 3780
 
4.2%
연 3735
 
4.2%
월 1962
 
2.2%
금 1757
 
2.0%
Other values (647) 50642
56.3%
None
ValueCountFrequency (%)
× 3
75.0%
· 1
 
25.0%
CJK
ValueCountFrequency (%)
入 2
40.0%
分 2
40.0%
人 1
20.0%
Math Operators
ValueCountFrequency (%)
∼ 1
100.0%
CJK Compat
ValueCountFrequency (%)
㎡ 1
100.0%
Arrows
ValueCountFrequency (%)
⇒ 1
100.0%

Interactions

2024-05-11T02:39:36.372131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:39:35.624887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:39:36.687564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:39:35.961679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T02:39:48.235888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비용명년월일금액
비용명1.0000.3510.290
년월일0.3511.0000.103
금액0.2900.1031.000
2024-05-11T02:39:48.479201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월일금액비용명
년월일1.0000.0180.137
금액0.0181.0000.122
비용명0.1370.1221.000

Missing values

2024-05-11T02:39:37.115518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T02:39:37.565944image/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
801서울숲리버뷰자이아파트A10026207연체료수익20190412320관리비 연체료 수납
56117강서힐스테이트아파트A15701007승강기수익20190408100000103-1303전출승강기사용료(4/16)
18300창동성원A13292701잡수익20190404880000케이티
15533신내성원A13187004재활용품수익201904124177304월분 재활용수수료
9149상암휴먼시아1단지A12179502잡수익20190408109091상가 주차비(4대)
1723대치SK VIEWA10026924승강기수익20190401150000102-903호 전출 승강기사용료
38529불암현대A13981208광고료수익2019040150000게시판(은호통신)광고
16913창동쌍용A13204406광고료수익2019040450000게시판광고료(라온데이케어센터)
54100상도건영A15603205기타운영수익2019041890000이상노 외 헬스 등록
41471이촌반도A14003101연체료수익2019041511270관리비 연체료 수납
아파트명아파트코드비용명년월일금액내용
36529상계주공6단지A13920707주차장수익2019043021300004월분 주차료
42682오동공원현대홈타운A14206201검침수익20190404105350한전 검침비 청구
52450신도림대림7차e-편한세상A15288807검침수익20190408176730한전검침수당
2188금천롯데캐슬골드파크1차아파트A10027188승강기수익20190410-130000108-1706 전출시 사다리차 사용 환급
20084서울숲2차푸르지오임대A13378103주차장수익20190402800003? ?????(8387)
47706신길우성2차A15086007연체료수익2019040450관리비 연체료 수납
12727장안현대홈타운A13010006연체료수익2019042217820관리비 연체료 수납
19143성수금호3차A13311101임대료수익201904226000000kt 중계기 임대료 (2019.04.01~2020.03.31)
11933래미안위브A13003007승강기수익2019042650000승강기이용료(108-703출)
21349성내삼성A13403101잡수익2019042542019년 1기 예정 부가가치세(1월~3월)