Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells13
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 = 37.70494142)Skewed

Reproduction

Analysis started2024-05-11 02:38:23.049498
Analysis finished2024-05-11 02:38:27.353060
Duration4.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

Distinct2068
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:38:27.605409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length7.2221
Min length2

Characters and Unicode

Total characters72221
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 row월곡래미안루나밸리
2nd row노량진쌍용예가
3rd row공릉태강
4th row중계한화꿈에그린더퍼스트
5th row목동대원칸타빌
ValueCountFrequency (%)
아파트 116
 
1.1%
래미안 39
 
0.4%
힐스테이트 26
 
0.2%
잠실엘스 22
 
0.2%
마포펜트라우스 21
 
0.2%
dmc파크뷰자이아파트 20
 
0.2%
홍제한양 19
 
0.2%
대방대림 18
 
0.2%
성산시영아파트 18
 
0.2%
서초힐스 18
 
0.2%
Other values (2124) 10246
97.0%
2024-05-11T02:38:28.559164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 2247
 
3.1%
파 2229
 
3.1%
지 2087
 
2.9%
트 2038
 
2.8%
대 1709
 
2.4%
단 1664
 
2.3%
동 1561
 
2.2%
신 1430
 
2.0%
차 1399
 
1.9%
성 1299
 
1.8%
Other values (418) 54558
75.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 65892
91.2%
Decimal Number 3988
 
5.5%
Uppercase Letter 827
 
1.1%
Space Separator 632
 
0.9%
Lowercase Letter 287
 
0.4%
Open Punctuation 161
 
0.2%
Close Punctuation 161
 
0.2%
Other Punctuation 147
 
0.2%
Dash Punctuation 117
 
0.2%
Letter Number 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 2247
 
3.4%
파 2229
 
3.4%
지 2087
 
3.2%
트 2038
 
3.1%
대 1709
 
2.6%
단 1664
 
2.5%
동 1561
 
2.4%
신 1430
 
2.2%
차 1399
 
2.1%
성 1299
 
2.0%
Other values (372) 48229
73.2%
Uppercase Letter
ValueCountFrequency (%)
S 162
19.6%
K 127
15.4%
C 81
9.8%
D 57
 
6.9%
M 57
 
6.9%
L 54
 
6.5%
H 52
 
6.3%
I 44
 
5.3%
A 40
 
4.8%
E 39
 
4.7%
Other values (7) 114
13.8%
Lowercase Letter
ValueCountFrequency (%)
e 158
55.1%
l 36
 
12.5%
i 30
 
10.5%
v 20
 
7.0%
s 11
 
3.8%
h 9
 
3.1%
w 6
 
2.1%
g 6
 
2.1%
a 6
 
2.1%
k 3
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 1187
29.8%
2 1172
29.4%
3 529
13.3%
4 271
 
6.8%
5 245
 
6.1%
6 165
 
4.1%
7 131
 
3.3%
9 122
 
3.1%
0 83
 
2.1%
8 83
 
2.1%
Other Punctuation
ValueCountFrequency (%)
, 132
89.8%
. 15
 
10.2%
Space Separator
ValueCountFrequency (%)
632
100.0%
Open Punctuation
ValueCountFrequency (%)
( 161
100.0%
Close Punctuation
ValueCountFrequency (%)
) 161
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 117
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 5
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 65892
91.2%
Common 5210
 
7.2%
Latin 1119
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 2247
 
3.4%
파 2229
 
3.4%
지 2087
 
3.2%
트 2038
 
3.1%
대 1709
 
2.6%
단 1664
 
2.5%
동 1561
 
2.4%
신 1430
 
2.2%
차 1399
 
2.1%
성 1299
 
2.0%
Other values (372) 48229
73.2%
Latin
ValueCountFrequency (%)
S 162
14.5%
e 158
14.1%
K 127
11.3%
C 81
 
7.2%
D 57
 
5.1%
M 57
 
5.1%
L 54
 
4.8%
H 52
 
4.6%
I 44
 
3.9%
A 40
 
3.6%
Other values (19) 287
25.6%
Common
ValueCountFrequency (%)
1 1187
22.8%
2 1172
22.5%
632
12.1%
3 529
10.2%
4 271
 
5.2%
5 245
 
4.7%
6 165
 
3.2%
( 161
 
3.1%
) 161
 
3.1%
, 132
 
2.5%
Other values (7) 555
10.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 65892
91.2%
ASCII 6324
 
8.8%
Number Forms 5
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 2247
 
3.4%
파 2229
 
3.4%
지 2087
 
3.2%
트 2038
 
3.1%
대 1709
 
2.6%
단 1664
 
2.5%
동 1561
 
2.4%
신 1430
 
2.2%
차 1399
 
2.1%
성 1299
 
2.0%
Other values (372) 48229
73.2%
ASCII
ValueCountFrequency (%)
1 1187
18.8%
2 1172
18.5%
632
 
10.0%
3 529
 
8.4%
4 271
 
4.3%
5 245
 
3.9%
6 165
 
2.6%
S 162
 
2.6%
( 161
 
2.5%
) 161
 
2.5%
Other values (35) 1639
25.9%
Number Forms
ValueCountFrequency (%)
Ⅰ 5
100.0%

아파트코드
Text

Distinct2073
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:38:29.185218image/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 rowA13613006
2nd rowA15605003
3rd rowA13980019
4th rowA13922003
5th rowA15805101
ValueCountFrequency (%)
a13822004 22
 
0.2%
a12179004 21
 
0.2%
a10027817 20
 
0.2%
a12085303 19
 
0.2%
a15681110 18
 
0.2%
a13778204 18
 
0.2%
a12185004 18
 
0.2%
a13879102 17
 
0.2%
a15003002 16
 
0.2%
a13704104 16
 
0.2%
Other values (2063) 9815
98.2%
2024-05-11T02:38:30.353049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18433
20.5%
1 17327
19.3%
A 9993
11.1%
3 8897
9.9%
2 7952
8.8%
5 6339
 
7.0%
8 5714
 
6.3%
7 5060
 
5.6%
4 3869
 
4.3%
6 3431
 
3.8%
Other values (2) 2985
 
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 18433
23.0%
1 17327
21.7%
3 8897
11.1%
2 7952
9.9%
5 6339
 
7.9%
8 5714
 
7.1%
7 5060
 
6.3%
4 3869
 
4.8%
6 3431
 
4.3%
9 2978
 
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 18433
23.0%
1 17327
21.7%
3 8897
11.1%
2 7952
9.9%
5 6339
 
7.9%
8 5714
 
7.1%
7 5060
 
6.3%
4 3869
 
4.8%
6 3431
 
4.3%
9 2978
 
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 18433
20.5%
1 17327
19.3%
A 9993
11.1%
3 8897
9.9%
2 7952
8.8%
5 6339
 
7.0%
8 5714
 
6.3%
7 5060
 
5.6%
4 3869
 
4.3%
6 3431
 
3.8%
Other values (2) 2985
 
3.3%

비용명
Categorical

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
연체료수익
3583 
광고료수익
1028 
잡수익
986 
승강기수익
891 
주차장수익
823 
Other values (10)
2689 

Length

Max length9
Median length5
Mean length5.0522
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기타운영수익
2nd row주차장수익
3rd row승강기수익
4th row기타운영수익
5th row승강기수익

Common Values

ValueCountFrequency (%)
연체료수익 3583
35.8%
광고료수익 1028
 
10.3%
잡수익 986
 
9.9%
승강기수익 891
 
8.9%
주차장수익 823
 
8.2%
기타운영수익 751
 
7.5%
고용안정사업수익 530
 
5.3%
검침수익 314
 
3.1%
임대료수익 230
 
2.3%
재활용품수익 216
 
2.2%
Other values (5) 648
 
6.5%

Length

2024-05-11T02:38:30.823357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
연체료수익 3583
35.8%
광고료수익 1028
 
10.3%
잡수익 986
 
9.9%
승강기수익 891
 
8.9%
주차장수익 823
 
8.2%
기타운영수익 751
 
7.5%
고용안정사업수익 530
 
5.3%
검침수익 314
 
3.1%
임대료수익 230
 
2.3%
재활용품수익 216
 
2.2%
Other values (5) 648
 
6.5%

년월일
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190818
Minimum20190801
Maximum20190831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:38:31.177739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190801
5-th percentile20190802
Q120190809
median20190820
Q320190827
95-th percentile20190831
Maximum20190831
Range30
Interquartile range (IQR)18

Descriptive statistics

Standard deviation9.4647917
Coefficient of variation (CV)4.6876713 × 10-7
Kurtosis-1.2033029
Mean20190818
Median Absolute Deviation (MAD)8
Skewness-0.27515648
Sum2.0190818 × 1011
Variance89.582283
MonotonicityNot monotonic
2024-05-11T02:38:31.712369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20190831 650
 
6.5%
20190830 648
 
6.5%
20190819 530
 
5.3%
20190814 525
 
5.2%
20190826 507
 
5.1%
20190820 501
 
5.0%
20190801 461
 
4.6%
20190827 442
 
4.4%
20190805 442
 
4.4%
20190829 436
 
4.4%
Other values (21) 4858
48.6%
ValueCountFrequency (%)
20190801 461
4.6%
20190802 313
3.1%
20190803 92
 
0.9%
20190804 67
 
0.7%
20190805 442
4.4%
20190806 319
3.2%
20190807 293
2.9%
20190808 282
2.8%
20190809 298
3.0%
20190810 79
 
0.8%
ValueCountFrequency (%)
20190831 650
6.5%
20190830 648
6.5%
20190829 436
4.4%
20190828 416
4.2%
20190827 442
4.4%
20190826 507
5.1%
20190825 148
 
1.5%
20190824 137
 
1.4%
20190823 431
4.3%
20190822 358
3.6%

금액
Real number (ℝ)

SKEWED 

Distinct3235
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean260035.04
Minimum-12426114
Maximum1.0347455 × 108
Zeros13
Zeros (%)0.1%
Negative44
Negative (%)0.4%
Memory size166.0 KiB
2024-05-11T02:38:32.309756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-12426114
5-th percentile170
Q13017.5
median30000
Q3110000
95-th percentile1080000
Maximum1.0347455 × 108
Range1.1590066 × 108
Interquartile range (IQR)106982.5

Descriptive statistics

Standard deviation1527022
Coefficient of variation (CV)5.8723699
Kurtosis2234.0629
Mean260035.04
Median Absolute Deviation (MAD)29300
Skewness37.704941
Sum2.6003504 × 109
Variance2.3317961 × 1012
MonotonicityNot monotonic
2024-05-11T02:38:32.822747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 607
 
6.1%
30000 599
 
6.0%
100000 459
 
4.6%
70000 152
 
1.5%
60000 147
 
1.5%
80000 140
 
1.4%
40000 134
 
1.3%
150000 110
 
1.1%
200000 106
 
1.1%
120000 105
 
1.1%
Other values (3225) 7441
74.4%
ValueCountFrequency (%)
-12426114 1
< 0.1%
-1572600 1
< 0.1%
-1384020 1
< 0.1%
-1251000 1
< 0.1%
-986180 1
< 0.1%
-780000 1
< 0.1%
-539679 1
< 0.1%
-396000 1
< 0.1%
-300000 1
< 0.1%
-240000 1
< 0.1%
ValueCountFrequency (%)
103474550 1
< 0.1%
48007340 1
< 0.1%
32250000 1
< 0.1%
29613220 1
< 0.1%
21801958 1
< 0.1%
18878776 1
< 0.1%
17925293 1
< 0.1%
15400000 1
< 0.1%
15007500 1
< 0.1%
15000000 2
< 0.1%

내용
Text

Distinct5757
Distinct (%)57.6%
Missing13
Missing (%)0.1%
Memory size156.2 KiB
2024-05-11T02:38:33.549529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length88
Median length68
Mean length13.696105
Min length1

Characters and Unicode

Total characters136783
Distinct characters747
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

Unique5495 ?
Unique (%)55.0%

Sample

1st row독19-151)김지우 3개월
2nd row08월분 주차료
3rd row1004동802호 공사 승강기사용료
4th row독서실 사용료 (8/23~9/22)102-1201,103-1804,102-1704
5th row승강기 사용료(1003호 공사)
ValueCountFrequency (%)
관리비 3718
 
14.3%
연체료 3594
 
13.9%
수납 3591
 
13.9%
7월분 347
 
1.3%
8월분 343
 
1.3%
승강기 251
 
1.0%
8월 236
 
0.9%
234
 
0.9%
입금 233
 
0.9%
승강기사용료 182
 
0.7%
Other values (7205) 13184
50.9%
2024-05-11T02:38:34.702743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16052
 
11.7%
료 5517
 
4.0%
수 5059
 
3.7%
리 4955
 
3.6%
비 4622
 
3.4%
0 4096
 
3.0%
1 3994
 
2.9%
관 3948
 
2.9%
체 3770
 
2.8%
연 3668
 
2.7%
Other values (737) 81102
59.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89712
65.6%
Decimal Number 18974
 
13.9%
Space Separator 16052
 
11.7%
Close Punctuation 3020
 
2.2%
Open Punctuation 3011
 
2.2%
Other Punctuation 2604
 
1.9%
Dash Punctuation 2171
 
1.6%
Uppercase Letter 711
 
0.5%
Math Symbol 275
 
0.2%
Lowercase Letter 186
 
0.1%
Other values (3) 67
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
료 5517
 
6.1%
수 5059
 
5.6%
리 4955
 
5.5%
비 4622
 
5.2%
관 3948
 
4.4%
체 3770
 
4.2%
연 3668
 
4.1%
납 3650
 
4.1%
월 2008
 
2.2%
금 1712
 
1.9%
Other values (646) 50803
56.6%
Uppercase Letter
ValueCountFrequency (%)
N 79
 
11.1%
S 54
 
7.6%
O 51
 
7.2%
G 51
 
7.2%
B 48
 
6.8%
T 48
 
6.8%
K 46
 
6.5%
M 41
 
5.8%
L 40
 
5.6%
A 39
 
5.5%
Other values (15) 214
30.1%
Lowercase Letter
ValueCountFrequency (%)
o 59
31.7%
n 30
16.1%
x 19
 
10.2%
e 14
 
7.5%
b 10
 
5.4%
k 9
 
4.8%
s 8
 
4.3%
c 7
 
3.8%
l 4
 
2.2%
i 4
 
2.2%
Other values (12) 22
 
11.8%
Other Punctuation
ValueCountFrequency (%)
/ 830
31.9%
, 720
27.6%
. 596
22.9%
: 199
 
7.6%
* 143
 
5.5%
? 35
 
1.3%
@ 31
 
1.2%
% 19
 
0.7%
# 11
 
0.4%
& 6
 
0.2%
Other values (6) 14
 
0.5%
Decimal Number
ValueCountFrequency (%)
0 4096
21.6%
1 3994
21.0%
2 2230
11.8%
8 1965
10.4%
7 1483
 
7.8%
3 1396
 
7.4%
9 1071
 
5.6%
4 1009
 
5.3%
5 921
 
4.9%
6 809
 
4.3%
Math Symbol
ValueCountFrequency (%)
~ 228
82.9%
> 14
 
5.1%
+ 13
 
4.7%
< 8
 
2.9%
= 5
 
1.8%
→ 2
 
0.7%
× 2
 
0.7%
⇒ 2
 
0.7%
÷ 1
 
0.4%
Close Punctuation
ValueCountFrequency (%)
) 2947
97.6%
] 73
 
2.4%
Open Punctuation
ValueCountFrequency (%)
( 2940
97.6%
[ 71
 
2.4%
Space Separator
ValueCountFrequency (%)
16052
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2171
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 64
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%
Other Number
ValueCountFrequency (%)
③ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89707
65.6%
Common 46174
33.8%
Latin 897
 
0.7%
Han 5
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
료 5517
 
6.2%
수 5059
 
5.6%
리 4955
 
5.5%
비 4622
 
5.2%
관 3948
 
4.4%
체 3770
 
4.2%
연 3668
 
4.1%
납 3650
 
4.1%
월 2008
 
2.2%
금 1712
 
1.9%
Other values (641) 50798
56.6%
Latin
ValueCountFrequency (%)
N 79
 
8.8%
o 59
 
6.6%
S 54
 
6.0%
O 51
 
5.7%
G 51
 
5.7%
B 48
 
5.4%
T 48
 
5.4%
K 46
 
5.1%
M 41
 
4.6%
L 40
 
4.5%
Other values (37) 380
42.4%
Common
ValueCountFrequency (%)
16052
34.8%
0 4096
 
8.9%
1 3994
 
8.6%
) 2947
 
6.4%
( 2940
 
6.4%
2 2230
 
4.8%
- 2171
 
4.7%
8 1965
 
4.3%
7 1483
 
3.2%
3 1396
 
3.0%
Other values (34) 6900
14.9%
Han
ValueCountFrequency (%)
內 1
20.0%
人 1
20.0%
日 1
20.0%
分 1
20.0%
入 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89706
65.6%
ASCII 47062
34.4%
CJK 5
 
< 0.1%
Arrows 4
 
< 0.1%
None 4
 
< 0.1%
Enclosed Alphanum 1
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16052
34.1%
0 4096
 
8.7%
1 3994
 
8.5%
) 2947
 
6.3%
( 2940
 
6.2%
2 2230
 
4.7%
- 2171
 
4.6%
8 1965
 
4.2%
7 1483
 
3.2%
3 1396
 
3.0%
Other values (75) 7788
16.5%
Hangul
ValueCountFrequency (%)
료 5517
 
6.2%
수 5059
 
5.6%
리 4955
 
5.5%
비 4622
 
5.2%
관 3948
 
4.4%
체 3770
 
4.2%
연 3668
 
4.1%
납 3650
 
4.1%
월 2008
 
2.2%
금 1712
 
1.9%
Other values (640) 50797
56.6%
Arrows
ValueCountFrequency (%)
→ 2
50.0%
⇒ 2
50.0%
None
ValueCountFrequency (%)
× 2
50.0%
· 1
25.0%
÷ 1
25.0%
Enclosed Alphanum
ValueCountFrequency (%)
③ 1
100.0%
CJK
ValueCountFrequency (%)
內 1
20.0%
人 1
20.0%
日 1
20.0%
分 1
20.0%
入 1
20.0%
Compat Jamo
ValueCountFrequency (%)
ㅡ 1
100.0%

Interactions

2024-05-11T02:38:26.165506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:38:25.422257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:38:26.469015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:38:25.801399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T02:38:34.959362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비용명년월일금액
비용명1.0000.3770.153
년월일0.3771.0000.014
금액0.1530.0141.000
2024-05-11T02:38:35.225794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월일금액비용명
년월일1.0000.0390.150
금액0.0391.0000.069
비용명0.1500.0691.000

Missing values

2024-05-11T02:38:26.831546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T02:38:27.203963image/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
28165월곡래미안루나밸리A13613006기타운영수익20190825130000독19-151)김지우 3개월
52369노량진쌍용예가A15605003주차장수익20190830128758008월분 주차료
37060공릉태강A13980019승강기수익20190826500001004동802호 공사 승강기사용료
35961중계한화꿈에그린더퍼스트A13922003기타운영수익20190822120000독서실 사용료 (8/23~9/22)102-1201,103-1804,102-1704
57332목동대원칸타빌A15805101승강기수익2019080550000승강기 사용료(1003호 공사)
26633개포7차우성A13594403광고료수익2019082650000수학 개인지도 게시판 광고료
1606북한산 더샵A10026604기타운영수익2019082413600안소연 8월 독서실 이용료
39615하계현대우성A13987303검침수익2019081656760019.7월 아파트 지원금 지급요청
59038목동2단지A15875102고용안정사업수익2019082081900007월 경비원 일자리안정자금 63명*130,000원
30648서초힐스A13778204연체료수익201908124240관리비 연체료 수납
아파트명아파트코드비용명년월일금액내용
29298돈암동부센트레빌A13681303승강기수익20190803150000109-603호 공사업체 - 승강기 사용료
4147DMC파크뷰자이아파트A10027817연체료수익20190830104980관리비 연체료 수납
19035옥수극동A13310004연체료수익201908161600관리비 연체료 수납
53622대방성원A15681105알뜰시장수익20190826292200평화자원(재활용품)
25136개포1차2차우성A13528105부과차익2019083133008월분 부과차이
39765하계극동건영벽산A13987306연체료수익2019081629500관리비 연체료 수납
58024롯데캐슬A15807205연체료수익20190829550관리비 연체료 수납
5560중림삼성사이버빌리지A10085903주차장수익2019082780000외부주차(오관식)-9월분
29605반포자이A13704104승강기수익20190802400000승강기사용료-113동2201호(리폼인테리어)
59612신정5차현대A15886504광고료수익2019082930000광고료수입