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 = 20.56679175)Skewed

Reproduction

Analysis started2024-05-11 02:30:50.063423
Analysis finished2024-05-11 02:30:52.880808
Duration2.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

Distinct2152
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:30:53.130889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length19
Mean length7.4025
Min length2

Characters and Unicode

Total characters74025
Distinct characters427
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique256 ?
Unique (%)2.6%

Sample

1st row용두신동아
2nd row방배아크로리버
3rd row방배아트자이
4th row신정푸른마을2단지
5th row신길우성4차
ValueCountFrequency (%)
아파트 204
 
1.9%
래미안 55
 
0.5%
아이파크 32
 
0.3%
백련산 22
 
0.2%
sk뷰 21
 
0.2%
고덕 21
 
0.2%
올림픽선수기자촌아파트 20
 
0.2%
힐스테이트 19
 
0.2%
신림현대 19
 
0.2%
항동하버라인3단지 19
 
0.2%
Other values (2219) 10395
96.0%
2024-05-11T02:30:53.859469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
파 2626
 
3.5%
아 2625
 
3.5%
트 2449
 
3.3%
지 2078
 
2.8%
대 1676
 
2.3%
단 1637
 
2.2%
동 1584
 
2.1%
신 1394
 
1.9%
이 1372
 
1.9%
차 1262
 
1.7%
Other values (417) 55322
74.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67623
91.4%
Decimal Number 3693
 
5.0%
Space Separator 940
 
1.3%
Uppercase Letter 856
 
1.2%
Lowercase Letter 348
 
0.5%
Close Punctuation 151
 
0.2%
Open Punctuation 151
 
0.2%
Other Punctuation 131
 
0.2%
Dash Punctuation 125
 
0.2%
Letter Number 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
파 2626
 
3.9%
아 2625
 
3.9%
트 2449
 
3.6%
지 2078
 
3.1%
대 1676
 
2.5%
단 1637
 
2.4%
동 1584
 
2.3%
신 1394
 
2.1%
이 1372
 
2.0%
차 1262
 
1.9%
Other values (372) 48920
72.3%
Uppercase Letter
ValueCountFrequency (%)
S 138
16.1%
C 118
13.8%
K 109
12.7%
M 96
11.2%
D 96
11.2%
L 56
6.5%
H 53
 
6.2%
I 36
 
4.2%
R 26
 
3.0%
A 26
 
3.0%
Other values (7) 102
11.9%
Lowercase Letter
ValueCountFrequency (%)
e 191
54.9%
s 31
 
8.9%
l 30
 
8.6%
i 26
 
7.5%
k 22
 
6.3%
v 16
 
4.6%
h 11
 
3.2%
a 6
 
1.7%
g 6
 
1.7%
w 5
 
1.4%
Decimal Number
ValueCountFrequency (%)
1 1115
30.2%
2 1018
27.6%
3 489
13.2%
4 263
 
7.1%
5 233
 
6.3%
6 140
 
3.8%
7 140
 
3.8%
9 118
 
3.2%
8 99
 
2.7%
0 78
 
2.1%
Other Punctuation
ValueCountFrequency (%)
, 112
85.5%
. 19
 
14.5%
Space Separator
ValueCountFrequency (%)
940
100.0%
Close Punctuation
ValueCountFrequency (%)
) 151
100.0%
Open Punctuation
ValueCountFrequency (%)
( 151
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 125
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67623
91.4%
Common 5191
 
7.0%
Latin 1211
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
파 2626
 
3.9%
아 2625
 
3.9%
트 2449
 
3.6%
지 2078
 
3.1%
대 1676
 
2.5%
단 1637
 
2.4%
동 1584
 
2.3%
신 1394
 
2.1%
이 1372
 
2.0%
차 1262
 
1.9%
Other values (372) 48920
72.3%
Latin
ValueCountFrequency (%)
e 191
15.8%
S 138
11.4%
C 118
9.7%
K 109
 
9.0%
M 96
 
7.9%
D 96
 
7.9%
L 56
 
4.6%
H 53
 
4.4%
I 36
 
3.0%
s 31
 
2.6%
Other values (19) 287
23.7%
Common
ValueCountFrequency (%)
1 1115
21.5%
2 1018
19.6%
940
18.1%
3 489
9.4%
4 263
 
5.1%
5 233
 
4.5%
) 151
 
2.9%
( 151
 
2.9%
6 140
 
2.7%
7 140
 
2.7%
Other values (6) 551
10.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67623
91.4%
ASCII 6395
 
8.6%
Number Forms 7
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
파 2626
 
3.9%
아 2625
 
3.9%
트 2449
 
3.6%
지 2078
 
3.1%
대 1676
 
2.5%
단 1637
 
2.4%
동 1584
 
2.3%
신 1394
 
2.1%
이 1372
 
2.0%
차 1262
 
1.9%
Other values (372) 48920
72.3%
ASCII
ValueCountFrequency (%)
1 1115
17.4%
2 1018
15.9%
940
14.7%
3 489
 
7.6%
4 263
 
4.1%
5 233
 
3.6%
e 191
 
3.0%
) 151
 
2.4%
( 151
 
2.4%
6 140
 
2.2%
Other values (34) 1704
26.6%
Number Forms
ValueCountFrequency (%)
Ⅰ 7
100.0%

아파트코드
Text

Distinct2156
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:30:54.464961image/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

Unique257 ?
Unique (%)2.6%

Sample

1st rowA13007004
2nd rowA13706001
3rd rowA10025927
4th rowA15886508
5th rowA15005001
ValueCountFrequency (%)
a13805002 20
 
0.2%
a10026941 19
 
0.2%
a10025614 19
 
0.2%
a15101508 19
 
0.2%
a10025850 18
 
0.2%
a15805115 18
 
0.2%
a12078704 17
 
0.2%
a15101504 17
 
0.2%
a13003005 16
 
0.2%
a13822002 16
 
0.2%
Other values (2146) 9821
98.2%
2024-05-11T02:30:55.490025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18636
20.7%
1 17142
19.0%
A 9996
11.1%
3 8768
9.7%
2 8336
9.3%
5 6342
 
7.0%
8 5563
 
6.2%
7 4766
 
5.3%
4 4154
 
4.6%
6 3350
 
3.7%
Other values (2) 2947
 
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 18636
23.3%
1 17142
21.4%
3 8768
11.0%
2 8336
10.4%
5 6342
 
7.9%
8 5563
 
7.0%
7 4766
 
6.0%
4 4154
 
5.2%
6 3350
 
4.2%
9 2943
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
A 9996
> 99.9%
B 4
 
< 0.1%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
0 18636
23.3%
1 17142
21.4%
3 8768
11.0%
2 8336
10.4%
5 6342
 
7.9%
8 5563
 
7.0%
7 4766
 
6.0%
4 4154
 
5.2%
6 3350
 
4.2%
9 2943
 
3.7%
Latin
ValueCountFrequency (%)
A 9996
> 99.9%
B 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18636
20.7%
1 17142
19.0%
A 9996
11.1%
3 8768
9.7%
2 8336
9.3%
5 6342
 
7.0%
8 5563
 
6.2%
7 4766
 
5.3%
4 4154
 
4.6%
6 3350
 
3.7%
Other values (2) 2947
 
3.3%

비용명
Categorical

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
연체료수익
3388 
승강기수익
1281 
잡수익
968 
주차장수익
940 
광고료수익
858 
Other values (10)
2565 

Length

Max length9
Median length5
Mean length5.0409
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
연체료수익 3388
33.9%
승강기수익 1281
 
12.8%
잡수익 968
 
9.7%
주차장수익 940
 
9.4%
광고료수익 858
 
8.6%
기타운영수익 632
 
6.3%
고용안정사업수익 506
 
5.1%
검침수익 312
 
3.1%
알뜰시장수익 247
 
2.5%
재활용품수익 227
 
2.3%
Other values (5) 641
 
6.4%

Length

2024-05-11T02:30:55.965479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
연체료수익 3388
33.9%
승강기수익 1281
 
12.8%
잡수익 968
 
9.7%
주차장수익 940
 
9.4%
광고료수익 858
 
8.6%
기타운영수익 632
 
6.3%
고용안정사업수익 506
 
5.1%
검침수익 312
 
3.1%
알뜰시장수익 247
 
2.5%
재활용품수익 227
 
2.3%
Other values (5) 641
 
6.4%

년월일
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20211018
Minimum20211001
Maximum20211031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:30:56.195977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20211001
5-th percentile20211001
Q120211010
median20211019
Q320211026
95-th percentile20211031
Maximum20211031
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.251937
Coefficient of variation (CV)4.57767 × 10-7
Kurtosis-1.1765816
Mean20211018
Median Absolute Deviation (MAD)7
Skewness-0.25191127
Sum2.0211018 × 1011
Variance85.598339
MonotonicityNot monotonic
2024-05-11T02:30:56.530241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20211031 690
 
6.9%
20211029 590
 
5.9%
20211025 586
 
5.9%
20211015 565
 
5.7%
20211001 506
 
5.1%
20211005 474
 
4.7%
20211012 464
 
4.6%
20211026 458
 
4.6%
20211020 439
 
4.4%
20211028 431
 
4.3%
Other values (21) 4797
48.0%
ValueCountFrequency (%)
20211001 506
5.1%
20211002 132
 
1.3%
20211003 92
 
0.9%
20211004 129
 
1.3%
20211005 474
4.7%
20211006 394
3.9%
20211007 341
3.4%
20211008 329
3.3%
20211009 60
 
0.6%
20211010 51
 
0.5%
ValueCountFrequency (%)
20211031 690
6.9%
20211030 218
 
2.2%
20211029 590
5.9%
20211028 431
4.3%
20211027 379
3.8%
20211026 458
4.6%
20211025 586
5.9%
20211024 142
 
1.4%
20211023 126
 
1.3%
20211022 386
3.9%

금액
Real number (ℝ)

SKEWED 

Distinct3028
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean235555.33
Minimum-1155000
Maximum50371091
Zeros17
Zeros (%)0.2%
Negative45
Negative (%)0.4%
Memory size166.0 KiB
2024-05-11T02:30:56.845363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1155000
5-th percentile130
Q12477.5
median30000
Q3100000
95-th percentile823382.5
Maximum50371091
Range51526091
Interquartile range (IQR)97522.5

Descriptive statistics

Standard deviation1293176.4
Coefficient of variation (CV)5.4899051
Kurtosis610.21653
Mean235555.33
Median Absolute Deviation (MAD)29410
Skewness20.566792
Sum2.3555533 × 109
Variance1.6723052 × 1012
MonotonicityNot monotonic
2024-05-11T02:30:57.285141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 673
 
6.7%
100000 600
 
6.0%
30000 579
 
5.8%
150000 212
 
2.1%
200000 187
 
1.9%
60000 180
 
1.8%
70000 151
 
1.5%
40000 122
 
1.2%
20000 109
 
1.1%
80000 104
 
1.0%
Other values (3018) 7083
70.8%
ValueCountFrequency (%)
-1155000 1
< 0.1%
-1000000 1
< 0.1%
-500000 2
< 0.1%
-390000 1
< 0.1%
-340000 1
< 0.1%
-320000 1
< 0.1%
-316660 1
< 0.1%
-163830 1
< 0.1%
-154000 1
< 0.1%
-150000 1
< 0.1%
ValueCountFrequency (%)
50371091 1
< 0.1%
47293750 1
< 0.1%
40000000 1
< 0.1%
36480000 1
< 0.1%
24800000 1
< 0.1%
23520000 1
< 0.1%
20820470 1
< 0.1%
19200000 1
< 0.1%
18500000 1
< 0.1%
16540000 1
< 0.1%

내용
Text

Distinct5914
Distinct (%)59.2%
Missing6
Missing (%)0.1%
Memory size156.2 KiB
2024-05-11T02:30:57.769984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length84
Median length67
Mean length14.398839
Min length2

Characters and Unicode

Total characters143902
Distinct characters757
Distinct categories14 ?
Distinct scripts4 ?
Distinct blocks9 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5658 ?
Unique (%)56.6%

Sample

1st row3-1504# 전출 시 복도 등 파손비
2nd row104-506 주차료 할인권(1시간)
3rd row103동 1106호 회의실 사용비(3시간)
4th row201-최기정(보강상사) 창고임대료(10월분)
5th row관리비 연체료 수납
ValueCountFrequency (%)
관리비 3542
 
13.5%
수납 3399
 
12.9%
연체료 3397
 
12.9%
10월분 407
 
1.5%
승강기사용료 336
 
1.3%
승강기 333
 
1.3%
9월분 320
 
1.2%
10월 273
 
1.0%
261
 
1.0%
입금 229
 
0.9%
Other values (7462) 13793
52.5%
2024-05-11T02:30:58.445854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16420
 
11.4%
1 6677
 
4.6%
0 6361
 
4.4%
료 5626
 
3.9%
리 4895
 
3.4%
수 4843
 
3.4%
비 4386
 
3.0%
관 3777
 
2.6%
체 3609
 
2.5%
연 3476
 
2.4%
Other values (747) 83832
58.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 90825
63.1%
Decimal Number 23920
 
16.6%
Space Separator 16420
 
11.4%
Close Punctuation 3208
 
2.2%
Open Punctuation 3201
 
2.2%
Other Punctuation 2716
 
1.9%
Dash Punctuation 2450
 
1.7%
Uppercase Letter 636
 
0.4%
Math Symbol 294
 
0.2%
Lowercase Letter 134
 
0.1%
Other values (4) 98
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
료 5626
 
6.2%
리 4895
 
5.4%
수 4843
 
5.3%
비 4386
 
4.8%
관 3777
 
4.2%
체 3609
 
4.0%
연 3476
 
3.8%
납 3450
 
3.8%
사 2028
 
2.2%
월 1940
 
2.1%
Other values (656) 52795
58.1%
Uppercase Letter
ValueCountFrequency (%)
N 75
 
11.8%
L 51
 
8.0%
K 49
 
7.7%
O 43
 
6.8%
S 42
 
6.6%
B 38
 
6.0%
D 38
 
6.0%
A 36
 
5.7%
E 34
 
5.3%
T 33
 
5.2%
Other values (15) 197
31.0%
Lowercase Letter
ValueCountFrequency (%)
o 45
33.6%
k 17
 
12.7%
x 13
 
9.7%
t 11
 
8.2%
n 11
 
8.2%
s 6
 
4.5%
p 5
 
3.7%
e 4
 
3.0%
b 4
 
3.0%
c 3
 
2.2%
Other values (10) 15
 
11.2%
Other Punctuation
ValueCountFrequency (%)
/ 847
31.2%
. 704
25.9%
, 660
24.3%
: 176
 
6.5%
* 158
 
5.8%
? 86
 
3.2%
@ 39
 
1.4%
% 20
 
0.7%
& 10
 
0.4%
# 6
 
0.2%
Other values (4) 10
 
0.4%
Decimal Number
ValueCountFrequency (%)
1 6677
27.9%
0 6361
26.6%
2 3209
13.4%
3 1648
 
6.9%
9 1435
 
6.0%
4 1249
 
5.2%
5 1127
 
4.7%
6 807
 
3.4%
7 715
 
3.0%
8 692
 
2.9%
Math Symbol
ValueCountFrequency (%)
~ 256
87.1%
> 10
 
3.4%
+ 10
 
3.4%
× 7
 
2.4%
= 4
 
1.4%
< 3
 
1.0%
→ 2
 
0.7%
÷ 1
 
0.3%
⇒ 1
 
0.3%
Other Symbol
ValueCountFrequency (%)
㎡ 2
33.3%
☞ 2
33.3%
㎏ 1
16.7%
㈜ 1
16.7%
Close Punctuation
ValueCountFrequency (%)
) 3123
97.4%
] 85
 
2.6%
Open Punctuation
ValueCountFrequency (%)
( 3116
97.3%
[ 85
 
2.7%
Space Separator
ValueCountFrequency (%)
16420
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2450
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 90
100.0%
Letter Number
ValueCountFrequency (%)
ⅹ 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 90822
63.1%
Common 52305
36.3%
Latin 771
 
0.5%
Han 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
료 5626
 
6.2%
리 4895
 
5.4%
수 4843
 
5.3%
비 4386
 
4.8%
관 3777
 
4.2%
체 3609
 
4.0%
연 3476
 
3.8%
납 3450
 
3.8%
사 2028
 
2.2%
월 1940
 
2.1%
Other values (654) 52792
58.1%
Latin
ValueCountFrequency (%)
N 75
 
9.7%
L 51
 
6.6%
K 49
 
6.4%
o 45
 
5.8%
O 43
 
5.6%
S 42
 
5.4%
B 38
 
4.9%
D 38
 
4.9%
A 36
 
4.7%
E 34
 
4.4%
Other values (36) 320
41.5%
Common
ValueCountFrequency (%)
16420
31.4%
1 6677
12.8%
0 6361
 
12.2%
2 3209
 
6.1%
) 3123
 
6.0%
( 3116
 
6.0%
- 2450
 
4.7%
3 1648
 
3.2%
9 1435
 
2.7%
4 1249
 
2.4%
Other values (34) 6617
12.7%
Han
ValueCountFrequency (%)
分 2
50.0%
內 1
25.0%
月 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 90820
63.1%
ASCII 53059
36.9%
None 9
 
< 0.1%
CJK 4
 
< 0.1%
Arrows 3
 
< 0.1%
CJK Compat 3
 
< 0.1%
Misc Symbols 2
 
< 0.1%
Compat Jamo 1
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16420
30.9%
1 6677
12.6%
0 6361
 
12.0%
2 3209
 
6.0%
) 3123
 
5.9%
( 3116
 
5.9%
- 2450
 
4.6%
3 1648
 
3.1%
9 1435
 
2.7%
4 1249
 
2.4%
Other values (72) 7371
13.9%
Hangul
ValueCountFrequency (%)
료 5626
 
6.2%
리 4895
 
5.4%
수 4843
 
5.3%
비 4386
 
4.8%
관 3777
 
4.2%
체 3609
 
4.0%
연 3476
 
3.8%
납 3450
 
3.8%
사 2028
 
2.2%
월 1940
 
2.1%
Other values (652) 52790
58.1%
None
ValueCountFrequency (%)
× 7
77.8%
÷ 1
 
11.1%
㈜ 1
 
11.1%
Arrows
ValueCountFrequency (%)
→ 2
66.7%
⇒ 1
33.3%
CJK Compat
ValueCountFrequency (%)
㎡ 2
66.7%
㎏ 1
33.3%
Misc Symbols
ValueCountFrequency (%)
☞ 2
100.0%
CJK
ValueCountFrequency (%)
分 2
50.0%
內 1
25.0%
月 1
25.0%
Compat Jamo
ValueCountFrequency (%)
ㅍ 1
100.0%
Number Forms
ValueCountFrequency (%)
ⅹ 1
100.0%

Interactions

2024-05-11T02:30:52.106951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:30:51.751183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:30:52.323323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:30:51.932808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T02:30:58.691308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비용명년월일금액
비용명1.0000.4140.225
년월일0.4141.0000.096
금액0.2250.0961.000
2024-05-11T02:30:58.924958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월일금액비용명
년월일1.0000.0080.164
금액0.0081.0000.098
비용명0.1640.0981.000

Missing values

2024-05-11T02:30:52.593163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T02:30:52.793664image/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
14720용두신동아A13007004잡수익2021101350003-1504# 전출 시 복도 등 파손비
30905방배아크로리버A13706001주차장수익2021100125450104-506 주차료 할인권(1시간)
3841방배아트자이A10025927잡수익202110039000103동 1106호 회의실 사용비(3시간)
59640신정푸른마을2단지A15886508임대료수익20211030250000201-최기정(보강상사) 창고임대료(10월분)
45331신길우성4차A15005001연체료수익202110295790관리비 연체료 수납
40082중계주공2단지A13985909연체료수익2021102512380관리비 연체료 수납
26351대치아이파크A13528102연체료수익2021102619450관리비 연체료 수납
44781여의도삼부A15001020연체료수익202110141550관리비 연체료 수납
7392강남한신휴플러스 6단지A10027912연체료수익2021100290관리비 연체료 수납
30294이편한세상보문A13677401고용안정사업수익20211018150000일자리지원금 미화원 9월분(5명*3만원)
아파트명아파트코드비용명년월일금액내용
3321목동파크자이아파트A10025729기타운영수익20211012700자동이체 할인
15436장안위더스빌A13078701주차장수익20211024118182월주차-이준효
32312방배래미안A13785301검침수익202110051302909월분검침지원금(01-4062-4091)
11784브라운스톤공덕A12179001임대료수익202110201434관리비부과차익
47386여의도진주A15089513연체료수익20211008110관리비 연체료 수납
27389압구정한양3단지A13590602연체료수익202110092410관리비 연체료 수납
11048성산2차e-편한세상A12125001광고료수익2021101830000게시판 광고료(아빠컴퍼니)
48487관악드림타운A15180705연체료수익202110117720관리비 연체료 수납
53406사당자이A15609502임대료수익20211006250000창고임대료 - (주)동아기업환경
40774성북역신도브래뉴A13987501공동주택지원금수익20211021594000경비실 에어컨설치비 구청지원금 입금