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 = 28.2431442)Skewed

Reproduction

Analysis started2024-05-11 02:27:45.121908
Analysis finished2024-05-11 02:27:49.735170
Duration4.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

Distinct2190
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:27:50.079881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length19
Mean length7.4586
Min length2

Characters and Unicode

Total characters74586
Distinct characters436
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

Unique245 ?
Unique (%)2.5%

Sample

1st row메세나폴리스
2nd row구로성호주상복합
3rd row구의강변우성
4th row역삼한스빌아파트
5th row길음뉴타운푸르지오아파트2,3단지
ValueCountFrequency (%)
아파트 227
 
2.1%
래미안 62
 
0.6%
아이파크 38
 
0.3%
sk뷰 33
 
0.3%
e편한세상 25
 
0.2%
고덕 22
 
0.2%
마포래미안푸르지오 22
 
0.2%
백련산 21
 
0.2%
디에이치 19
 
0.2%
센트럴 18
 
0.2%
Other values (2265) 10467
95.6%
2024-05-11T02:27:51.238636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 2675
 
3.6%
파 2602
 
3.5%
트 2518
 
3.4%
지 2041
 
2.7%
단 1609
 
2.2%
대 1605
 
2.2%
동 1525
 
2.0%
이 1507
 
2.0%
신 1328
 
1.8%
차 1270
 
1.7%
Other values (426) 55906
75.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68073
91.3%
Decimal Number 3656
 
4.9%
Space Separator 1046
 
1.4%
Uppercase Letter 883
 
1.2%
Lowercase Letter 337
 
0.5%
Open Punctuation 164
 
0.2%
Close Punctuation 164
 
0.2%
Other Punctuation 130
 
0.2%
Dash Punctuation 127
 
0.2%
Letter Number 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 2675
 
3.9%
파 2602
 
3.8%
트 2518
 
3.7%
지 2041
 
3.0%
단 1609
 
2.4%
대 1605
 
2.4%
동 1525
 
2.2%
이 1507
 
2.2%
신 1328
 
2.0%
차 1270
 
1.9%
Other values (381) 49393
72.6%
Uppercase Letter
ValueCountFrequency (%)
S 153
17.3%
C 115
13.0%
K 98
11.1%
D 95
10.8%
M 95
10.8%
H 66
7.5%
L 59
 
6.7%
E 36
 
4.1%
I 36
 
4.1%
A 32
 
3.6%
Other values (7) 98
11.1%
Lowercase Letter
ValueCountFrequency (%)
e 190
56.4%
l 31
 
9.2%
s 30
 
8.9%
k 29
 
8.6%
i 21
 
6.2%
v 17
 
5.0%
c 6
 
1.8%
h 5
 
1.5%
w 4
 
1.2%
g 2
 
0.6%
Decimal Number
ValueCountFrequency (%)
1 1106
30.3%
2 1000
27.4%
3 453
12.4%
4 274
 
7.5%
5 215
 
5.9%
6 168
 
4.6%
9 132
 
3.6%
7 131
 
3.6%
8 93
 
2.5%
0 84
 
2.3%
Other Punctuation
ValueCountFrequency (%)
, 105
80.8%
. 25
 
19.2%
Space Separator
ValueCountFrequency (%)
1046
100.0%
Open Punctuation
ValueCountFrequency (%)
( 164
100.0%
Close Punctuation
ValueCountFrequency (%)
) 164
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 127
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68073
91.3%
Common 5287
 
7.1%
Latin 1226
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 2675
 
3.9%
파 2602
 
3.8%
트 2518
 
3.7%
지 2041
 
3.0%
단 1609
 
2.4%
대 1605
 
2.4%
동 1525
 
2.2%
이 1507
 
2.2%
신 1328
 
2.0%
차 1270
 
1.9%
Other values (381) 49393
72.6%
Latin
ValueCountFrequency (%)
e 190
15.5%
S 153
12.5%
C 115
9.4%
K 98
 
8.0%
D 95
 
7.7%
M 95
 
7.7%
H 66
 
5.4%
L 59
 
4.8%
E 36
 
2.9%
I 36
 
2.9%
Other values (19) 283
23.1%
Common
ValueCountFrequency (%)
1 1106
20.9%
1046
19.8%
2 1000
18.9%
3 453
8.6%
4 274
 
5.2%
5 215
 
4.1%
6 168
 
3.2%
( 164
 
3.1%
) 164
 
3.1%
9 132
 
2.5%
Other values (6) 565
10.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68073
91.3%
ASCII 6507
 
8.7%
Number Forms 6
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 2675
 
3.9%
파 2602
 
3.8%
트 2518
 
3.7%
지 2041
 
3.0%
단 1609
 
2.4%
대 1605
 
2.4%
동 1525
 
2.2%
이 1507
 
2.2%
신 1328
 
2.0%
차 1270
 
1.9%
Other values (381) 49393
72.6%
ASCII
ValueCountFrequency (%)
1 1106
17.0%
1046
16.1%
2 1000
15.4%
3 453
 
7.0%
4 274
 
4.2%
5 215
 
3.3%
e 190
 
2.9%
6 168
 
2.6%
( 164
 
2.5%
) 164
 
2.5%
Other values (34) 1727
26.5%
Number Forms
ValueCountFrequency (%)
Ⅰ 6
100.0%

아파트코드
Text

Distinct2196
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:27:52.277845image/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

Unique246 ?
Unique (%)2.5%

Sample

1st rowA12174601
2nd rowA15284204
3rd rowA14320302
4th rowA13508005
5th rowA13611007
ValueCountFrequency (%)
a12175203 22
 
0.2%
a13778204 18
 
0.2%
a10025614 18
 
0.2%
a10025850 18
 
0.2%
a13707010 17
 
0.2%
a13822003 17
 
0.2%
a15728009 17
 
0.2%
a10027906 17
 
0.2%
a14272309 17
 
0.2%
a13704104 17
 
0.2%
Other values (2186) 9822
98.2%
2024-05-11T02:27:53.824173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18831
20.9%
1 17123
19.0%
A 9991
11.1%
3 8575
9.5%
2 8268
9.2%
5 6403
 
7.1%
8 5457
 
6.1%
7 4829
 
5.4%
4 4134
 
4.6%
6 3413
 
3.8%
Other values (2) 2976
 
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 18831
23.5%
1 17123
21.4%
3 8575
10.7%
2 8268
10.3%
5 6403
 
8.0%
8 5457
 
6.8%
7 4829
 
6.0%
4 4134
 
5.2%
6 3413
 
4.3%
9 2967
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
A 9991
99.9%
B 9
 
0.1%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
0 18831
23.5%
1 17123
21.4%
3 8575
10.7%
2 8268
10.3%
5 6403
 
8.0%
8 5457
 
6.8%
7 4829
 
6.0%
4 4134
 
5.2%
6 3413
 
4.3%
9 2967
 
3.7%
Latin
ValueCountFrequency (%)
A 9991
99.9%
B 9
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18831
20.9%
1 17123
19.0%
A 9991
11.1%
3 8575
9.5%
2 8268
9.2%
5 6403
 
7.1%
8 5457
 
6.1%
7 4829
 
5.4%
4 4134
 
4.6%
6 3413
 
3.8%
Other values (2) 2976
 
3.3%

비용명
Categorical

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
연체료수익
3672 
승강기수익
1275 
잡수익
962 
주차장수익
865 
광고료수익
799 
Other values (10)
2427 

Length

Max length9
Median length5
Mean length4.9571
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
연체료수익 3672
36.7%
승강기수익 1275
 
12.8%
잡수익 962
 
9.6%
주차장수익 865
 
8.6%
광고료수익 799
 
8.0%
기타운영수익 723
 
7.2%
검침수익 331
 
3.3%
고용안정사업수익 304
 
3.0%
부과차익 241
 
2.4%
임대료수익 237
 
2.4%
Other values (5) 591
 
5.9%

Length

2024-05-11T02:27:54.412773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
연체료수익 3672
36.7%
승강기수익 1275
 
12.8%
잡수익 962
 
9.6%
주차장수익 865
 
8.6%
광고료수익 799
 
8.0%
기타운영수익 723
 
7.2%
검침수익 331
 
3.3%
고용안정사업수익 304
 
3.0%
부과차익 241
 
2.4%
임대료수익 237
 
2.4%
Other values (5) 591
 
5.9%

년월일
Real number (ℝ)

Distinct28
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20220216
Minimum20220201
Maximum20220228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:27:55.100096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20220201
5-th percentile20220203
Q120220208
median20220217
Q320220224
95-th percentile20220228
Maximum20220228
Range27
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.6243709
Coefficient of variation (CV)4.2652219 × 10-7
Kurtosis-1.3363352
Mean20220216
Median Absolute Deviation (MAD)8
Skewness-0.1634726
Sum2.0220216 × 1011
Variance74.379774
MonotonicityNot monotonic
2024-05-11T02:27:55.739060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
20220228 1273
 
12.7%
20220225 740
 
7.4%
20220203 714
 
7.1%
20220210 510
 
5.1%
20220207 502
 
5.0%
20220204 471
 
4.7%
20220221 450
 
4.5%
20220223 433
 
4.3%
20220222 432
 
4.3%
20220224 421
 
4.2%
Other values (18) 4054
40.5%
ValueCountFrequency (%)
20220201 108
 
1.1%
20220202 188
 
1.9%
20220203 714
7.1%
20220204 471
4.7%
20220205 114
 
1.1%
20220206 101
 
1.0%
20220207 502
5.0%
20220208 343
3.4%
20220209 327
3.3%
20220210 510
5.1%
ValueCountFrequency (%)
20220228 1273
12.7%
20220227 198
 
2.0%
20220226 173
 
1.7%
20220225 740
7.4%
20220224 421
 
4.2%
20220223 433
 
4.3%
20220222 432
 
4.3%
20220221 450
 
4.5%
20220220 94
 
0.9%
20220219 81
 
0.8%

금액
Real number (ℝ)

SKEWED 

Distinct3330
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean240976.08
Minimum-4418720
Maximum82076000
Zeros8
Zeros (%)0.1%
Negative36
Negative (%)0.4%
Memory size166.0 KiB
2024-05-11T02:27:56.538262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4418720
5-th percentile200
Q12880
median30000
Q3100000
95-th percentile879686.5
Maximum82076000
Range86494720
Interquartile range (IQR)97120

Descriptive statistics

Standard deviation1588465.1
Coefficient of variation (CV)6.5917957
Kurtosis1099.6724
Mean240976.08
Median Absolute Deviation (MAD)29210
Skewness28.243144
Sum2.4097608 × 109
Variance2.5232214 × 1012
MonotonicityNot monotonic
2024-05-11T02:27:57.160381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 573
 
5.7%
50000 522
 
5.2%
30000 513
 
5.1%
150000 182
 
1.8%
60000 165
 
1.7%
70000 153
 
1.5%
200000 125
 
1.2%
40000 111
 
1.1%
20000 109
 
1.1%
80000 90
 
0.9%
Other values (3320) 7457
74.6%
ValueCountFrequency (%)
-4418720 1
< 0.1%
-3224090 1
< 0.1%
-1100000 1
< 0.1%
-766350 1
< 0.1%
-685000 1
< 0.1%
-484280 1
< 0.1%
-330000 1
< 0.1%
-300000 1
< 0.1%
-253030 1
< 0.1%
-240000 1
< 0.1%
ValueCountFrequency (%)
82076000 1
 
< 0.1%
52518320 1
 
< 0.1%
51083383 1
 
< 0.1%
47064670 1
 
< 0.1%
42383880 1
 
< 0.1%
37896060 1
 
< 0.1%
24750000 1
 
< 0.1%
22343180 1
 
< 0.1%
16000000 1
 
< 0.1%
15000000 3
< 0.1%

내용
Text

Distinct5705
Distinct (%)57.1%
Missing4
Missing (%)< 0.1%
Memory size156.2 KiB
2024-05-11T02:27:58.185062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length76
Median length69
Mean length14.148559
Min length2

Characters and Unicode

Total characters141429
Distinct characters737
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

Unique5460 ?
Unique (%)54.6%

Sample

1st row폐기물수입(1건)
2nd row장대호(5761) 방문
3rd row2월분 주차비 (59세대)
4th row관리비 연체료 수납
5th row스포츠센타 주민이용카드대금(2/15일 입금예정)
ValueCountFrequency (%)
관리비 3813
 
14.3%
연체료 3682
 
13.8%
수납 3681
 
13.8%
2월분 363
 
1.4%
승강기 362
 
1.4%
311
 
1.2%
승강기사용료 303
 
1.1%
2월 259
 
1.0%
1월분 250
 
0.9%
사용료 244
 
0.9%
Other values (7255) 13421
50.3%
2024-05-11T02:27:59.838742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16823
 
11.9%
료 5960
 
4.2%
1 5344
 
3.8%
0 5202
 
3.7%
수 5153
 
3.6%
2 5066
 
3.6%
리 4783
 
3.4%
비 4591
 
3.2%
관 4027
 
2.8%
체 3901
 
2.8%
Other values (727) 80579
57.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89646
63.4%
Decimal Number 22578
 
16.0%
Space Separator 16823
 
11.9%
Other Punctuation 2983
 
2.1%
Close Punctuation 2897
 
2.0%
Open Punctuation 2892
 
2.0%
Dash Punctuation 2442
 
1.7%
Uppercase Letter 634
 
0.4%
Math Symbol 287
 
0.2%
Lowercase Letter 142
 
0.1%
Other values (4) 105
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
료 5960
 
6.6%
수 5153
 
5.7%
리 4783
 
5.3%
비 4591
 
5.1%
관 4027
 
4.5%
체 3901
 
4.4%
연 3752
 
4.2%
납 3746
 
4.2%
용 1893
 
2.1%
사 1868
 
2.1%
Other values (634) 49972
55.7%
Uppercase Letter
ValueCountFrequency (%)
N 75
11.8%
B 60
 
9.5%
L 49
 
7.7%
O 45
 
7.1%
K 43
 
6.8%
A 41
 
6.5%
E 40
 
6.3%
T 38
 
6.0%
C 38
 
6.0%
G 32
 
5.0%
Other values (15) 173
27.3%
Lowercase Letter
ValueCountFrequency (%)
o 39
27.5%
t 15
 
10.6%
k 10
 
7.0%
n 9
 
6.3%
e 9
 
6.3%
a 8
 
5.6%
s 8
 
5.6%
x 7
 
4.9%
p 6
 
4.2%
d 5
 
3.5%
Other values (13) 26
18.3%
Other Punctuation
ValueCountFrequency (%)
/ 816
27.4%
, 696
23.3%
. 682
22.9%
? 390
13.1%
: 178
 
6.0%
* 138
 
4.6%
@ 33
 
1.1%
% 17
 
0.6%
& 13
 
0.4%
# 6
 
0.2%
Other values (5) 14
 
0.5%
Decimal Number
ValueCountFrequency (%)
1 5344
23.7%
0 5202
23.0%
2 5066
22.4%
3 1876
 
8.3%
4 1270
 
5.6%
5 1032
 
4.6%
6 803
 
3.6%
7 756
 
3.3%
8 656
 
2.9%
9 573
 
2.5%
Math Symbol
ValueCountFrequency (%)
~ 231
80.5%
+ 26
 
9.1%
> 12
 
4.2%
× 8
 
2.8%
= 6
 
2.1%
< 2
 
0.7%
⇒ 1
 
0.3%
→ 1
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 2813
97.1%
] 83
 
2.9%
} 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 2808
97.1%
[ 81
 
2.8%
{ 3
 
0.1%
Space Separator
ValueCountFrequency (%)
16823
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2442
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 100
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 3
100.0%
Other Number
ValueCountFrequency (%)
④ 1
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89636
63.4%
Common 51007
36.1%
Latin 776
 
0.5%
Han 10
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
료 5960
 
6.6%
수 5153
 
5.7%
리 4783
 
5.3%
비 4591
 
5.1%
관 4027
 
4.5%
체 3901
 
4.4%
연 3752
 
4.2%
납 3746
 
4.2%
용 1893
 
2.1%
사 1868
 
2.1%
Other values (629) 49962
55.7%
Latin
ValueCountFrequency (%)
N 75
 
9.7%
B 60
 
7.7%
L 49
 
6.3%
O 45
 
5.8%
K 43
 
5.5%
A 41
 
5.3%
E 40
 
5.2%
o 39
 
5.0%
T 38
 
4.9%
C 38
 
4.9%
Other values (38) 308
39.7%
Common
ValueCountFrequency (%)
16823
33.0%
1 5344
 
10.5%
0 5202
 
10.2%
2 5066
 
9.9%
) 2813
 
5.5%
( 2808
 
5.5%
- 2442
 
4.8%
3 1876
 
3.7%
4 1270
 
2.5%
5 1032
 
2.0%
Other values (35) 6331
 
12.4%
Han
ValueCountFrequency (%)
入 5
50.0%
不 2
 
20.0%
月 1
 
10.0%
分 1
 
10.0%
出 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89635
63.4%
ASCII 51767
36.6%
None 12
 
< 0.1%
CJK 8
 
< 0.1%
CJK Compat Ideographs 2
 
< 0.1%
Arrows 2
 
< 0.1%
Compat Jamo 1
 
< 0.1%
Enclosed Alphanum 1
 
< 0.1%
CJK Compat 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16823
32.5%
1 5344
 
10.3%
0 5202
 
10.0%
2 5066
 
9.8%
) 2813
 
5.4%
( 2808
 
5.4%
- 2442
 
4.7%
3 1876
 
3.6%
4 1270
 
2.5%
5 1032
 
2.0%
Other values (77) 7091
13.7%
Hangul
ValueCountFrequency (%)
료 5960
 
6.6%
수 5153
 
5.7%
리 4783
 
5.3%
비 4591
 
5.1%
관 4027
 
4.5%
체 3901
 
4.4%
연 3752
 
4.2%
납 3746
 
4.2%
용 1893
 
2.1%
사 1868
 
2.1%
Other values (628) 49961
55.7%
None
ValueCountFrequency (%)
× 8
66.7%
？ 4
33.3%
CJK
ValueCountFrequency (%)
入 5
62.5%
月 1
 
12.5%
分 1
 
12.5%
出 1
 
12.5%
CJK Compat Ideographs
ValueCountFrequency (%)
不 2
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅒ 1
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
④ 1
100.0%
CJK Compat
ValueCountFrequency (%)
㎡ 1
100.0%
Arrows
ValueCountFrequency (%)
⇒ 1
50.0%
→ 1
50.0%

Interactions

2024-05-11T02:27:47.897844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:27:47.220270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:27:48.340079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:27:47.542806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T02:28:00.204662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비용명년월일금액
비용명1.0000.3960.145
년월일0.3961.0000.083
금액0.1450.0831.000
2024-05-11T02:28:00.477055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월일금액비용명
년월일1.0000.1080.159
금액0.1081.0000.062
비용명0.1590.0621.000

Missing values

2024-05-11T02:27:48.900019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T02:27:49.514530image/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
11968메세나폴리스A12174601기타운영수익202202124500폐기물수입(1건)
48353구로성호주상복합A15284204주차장수익202202194545장대호(5761) 방문
42119구의강변우성A14320302주차장수익2022022817078602월분 주차비 (59세대)
24035역삼한스빌아파트A13508005연체료수익202202271360관리비 연체료 수납
28388길음뉴타운푸르지오아파트2,3단지A13611007기타운영수익2022021266400스포츠센타 주민이용카드대금(2/15일 입금예정)
51209신동아리버파크제2관리사무소A15676701연체료수익202202241240관리비 연체료 수납
8680신당현대A10045601검침수익202202224050602월 전기검침수당
50577노량진쌍용예가A15605003연체료수익202202192050관리비 연체료 수납
380디에이치 자이 개포A10024216승강기수익20220210100000803동 1406호 전입 승강기 사용료
45831신림신도브래뉴A15101802연체료수익202202246170관리비 연체료 수납
아파트명아파트코드비용명년월일금액내용
21655서울숲푸르지오A13380803연체료수익2022021521910관리비 연체료 수납
25998도곡한신A13550403광고료수익2022022340000게시판광고 1주(@44,000 빡센과외-최문석)
8544신당남산타운(분양)A10045302승강기수익20220222160000승강기사용료(13-1601)
14989휘경동일스위트리버A13009206연체료수익202202032900관리비 연체료 수납
12437동양엔파트A12181101연체료수익202202254140관리비 연체료 수납
6368보문파크뷰자이아파트A10027189승강기수익20220203100000111동 1204호 전출시 승강기 사용료
53945가양9-1A15781002고용안정사업수익202202281820002월분 일자리안정자금 지원금(미화-상우)7명*26,000원
45807신림현대A15101508승강기수익2022022430000108-508 승강기사용료
2483DMC에코자이A10025130연체료수익20220206520관리비 연체료 수납
57466거평프리젠B11680022잡수익20220203831월분