Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells29
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 = 20.1205191)Skewed

Reproduction

Analysis started2024-05-11 02:28:24.386190
Analysis finished2024-05-11 02:28:28.262382
Duration3.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

Distinct2173
Distinct (%)21.8%
Missing21
Missing (%)0.2%
Memory size156.2 KiB
2024-05-11T02:28:28.525029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length20
Mean length7.5215954
Min length2

Characters and Unicode

Total characters75058
Distinct characters435
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

Unique246 ?
Unique (%)2.5%

Sample

1st row래미안에스티움
2nd row양천롯데캐슬
3rd row서울숲더샵
4th row어울림더리버아파트
5th row서초호반써밋
ValueCountFrequency (%)
아파트 233
 
2.1%
래미안 61
 
0.6%
e편한세상 40
 
0.4%
아이파크 37
 
0.3%
센트럴 25
 
0.2%
sk뷰 25
 
0.2%
푸르지오 22
 
0.2%
고덕 22
 
0.2%
롯데캐슬노블레스 19
 
0.2%
마포래미안푸르지오 18
 
0.2%
Other values (2254) 10463
95.4%
2024-05-11T02:28:29.520961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 2703
 
3.6%
파 2694
 
3.6%
트 2630
 
3.5%
지 2003
 
2.7%
대 1617
 
2.2%
단 1582
 
2.1%
이 1543
 
2.1%
동 1528
 
2.0%
신 1321
 
1.8%
차 1263
 
1.7%
Other values (425) 56174
74.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68697
91.5%
Decimal Number 3557
 
4.7%
Space Separator 1087
 
1.4%
Uppercase Letter 867
 
1.2%
Lowercase Letter 296
 
0.4%
Close Punctuation 171
 
0.2%
Open Punctuation 171
 
0.2%
Other Punctuation 106
 
0.1%
Dash Punctuation 102
 
0.1%
Letter Number 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 2703
 
3.9%
파 2694
 
3.9%
트 2630
 
3.8%
지 2003
 
2.9%
대 1617
 
2.4%
단 1582
 
2.3%
이 1543
 
2.2%
동 1528
 
2.2%
신 1321
 
1.9%
차 1263
 
1.8%
Other values (380) 49813
72.5%
Uppercase Letter
ValueCountFrequency (%)
S 145
16.7%
C 120
13.8%
K 104
12.0%
D 95
11.0%
M 95
11.0%
H 55
 
6.3%
L 44
 
5.1%
E 40
 
4.6%
I 39
 
4.5%
A 26
 
3.0%
Other values (7) 104
12.0%
Lowercase Letter
ValueCountFrequency (%)
e 182
61.5%
l 27
 
9.1%
s 21
 
7.1%
k 19
 
6.4%
i 18
 
6.1%
v 13
 
4.4%
h 6
 
2.0%
w 4
 
1.4%
c 2
 
0.7%
g 2
 
0.7%
Decimal Number
ValueCountFrequency (%)
1 1069
30.1%
2 968
27.2%
3 459
12.9%
4 253
 
7.1%
5 247
 
6.9%
6 157
 
4.4%
7 129
 
3.6%
9 106
 
3.0%
8 96
 
2.7%
0 73
 
2.1%
Other Punctuation
ValueCountFrequency (%)
, 87
82.1%
. 19
 
17.9%
Space Separator
ValueCountFrequency (%)
1087
100.0%
Close Punctuation
ValueCountFrequency (%)
) 171
100.0%
Open Punctuation
ValueCountFrequency (%)
( 171
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 102
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68697
91.5%
Common 5194
 
6.9%
Latin 1167
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 2703
 
3.9%
파 2694
 
3.9%
트 2630
 
3.8%
지 2003
 
2.9%
대 1617
 
2.4%
단 1582
 
2.3%
이 1543
 
2.2%
동 1528
 
2.2%
신 1321
 
1.9%
차 1263
 
1.8%
Other values (380) 49813
72.5%
Latin
ValueCountFrequency (%)
e 182
15.6%
S 145
12.4%
C 120
10.3%
K 104
8.9%
D 95
 
8.1%
M 95
 
8.1%
H 55
 
4.7%
L 44
 
3.8%
E 40
 
3.4%
I 39
 
3.3%
Other values (19) 248
21.3%
Common
ValueCountFrequency (%)
1087
20.9%
1 1069
20.6%
2 968
18.6%
3 459
8.8%
4 253
 
4.9%
5 247
 
4.8%
) 171
 
3.3%
( 171
 
3.3%
6 157
 
3.0%
7 129
 
2.5%
Other values (6) 483
9.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68697
91.5%
ASCII 6357
 
8.5%
Number Forms 4
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 2703
 
3.9%
파 2694
 
3.9%
트 2630
 
3.8%
지 2003
 
2.9%
대 1617
 
2.4%
단 1582
 
2.3%
이 1543
 
2.2%
동 1528
 
2.2%
신 1321
 
1.9%
차 1263
 
1.8%
Other values (380) 49813
72.5%
ASCII
ValueCountFrequency (%)
1087
17.1%
1 1069
16.8%
2 968
15.2%
3 459
 
7.2%
4 253
 
4.0%
5 247
 
3.9%
e 182
 
2.9%
) 171
 
2.7%
( 171
 
2.7%
6 157
 
2.5%
Other values (34) 1593
25.1%
Number Forms
ValueCountFrequency (%)
Ⅰ 4
100.0%

아파트코드
Text

Distinct2179
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:28:30.402875image/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

Unique249 ?
Unique (%)2.5%

Sample

1st rowA10027073
2nd rowA15883202
3rd rowA13307003
4th rowA13375906
5th rowA13778211
ValueCountFrequency (%)
a10027817 21
 
0.2%
a10026180 19
 
0.2%
a13879102 18
 
0.2%
a12175203 18
 
0.2%
a13822003 17
 
0.2%
a14272304 17
 
0.2%
a12179004 17
 
0.2%
a11054101 17
 
0.2%
a13920706 17
 
0.2%
a13822004 16
 
0.2%
Other values (2169) 9823
98.2%
2024-05-11T02:28:31.931586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18871
21.0%
1 17202
19.1%
A 9994
11.1%
3 8519
9.5%
2 8354
9.3%
5 6179
 
6.9%
8 5431
 
6.0%
7 4891
 
5.4%
4 4162
 
4.6%
6 3358
 
3.7%
Other values (2) 3039
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80000
88.9%
Uppercase Letter 10000
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18871
23.6%
1 17202
21.5%
3 8519
10.6%
2 8354
10.4%
5 6179
 
7.7%
8 5431
 
6.8%
7 4891
 
6.1%
4 4162
 
5.2%
6 3358
 
4.2%
9 3033
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
A 9994
99.9%
B 6
 
0.1%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
0 18871
23.6%
1 17202
21.5%
3 8519
10.6%
2 8354
10.4%
5 6179
 
7.7%
8 5431
 
6.8%
7 4891
 
6.1%
4 4162
 
5.2%
6 3358
 
4.2%
9 3033
 
3.8%
Latin
ValueCountFrequency (%)
A 9994
99.9%
B 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18871
21.0%
1 17202
19.1%
A 9994
11.1%
3 8519
9.5%
2 8354
9.3%
5 6179
 
6.9%
8 5431
 
6.0%
7 4891
 
5.4%
4 4162
 
4.6%
6 3358
 
3.7%
Other values (2) 3039
 
3.4%

비용명
Categorical

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
연체료수익
3713 
승강기수익
1071 
주차장수익
1021 
잡수익
1019 
기타운영수익
903 
Other values (10)
2273 

Length

Max length9
Median length5
Mean length4.8785
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
연체료수익 3713
37.1%
승강기수익 1071
 
10.7%
주차장수익 1021
 
10.2%
잡수익 1019
 
10.2%
기타운영수익 903
 
9.0%
광고료수익 882
 
8.8%
검침수익 309
 
3.1%
부과차익 253
 
2.5%
임대료수익 250
 
2.5%
재활용품수익 225
 
2.2%
Other values (5) 354
 
3.5%

Length

2024-05-11T02:28:32.522569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
연체료수익 3713
37.1%
승강기수익 1071
 
10.7%
주차장수익 1021
 
10.2%
잡수익 1019
 
10.2%
기타운영수익 903
 
9.0%
광고료수익 882
 
8.8%
검침수익 309
 
3.1%
부과차익 253
 
2.5%
임대료수익 250
 
2.5%
재활용품수익 225
 
2.2%
Other values (5) 354
 
3.5%

년월일
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20220817
Minimum20220801
Maximum20220831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:28:32.919613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20220801
5-th percentile20220801
Q120220808
median20220819
Q320220826
95-th percentile20220831
Maximum20220831
Range30
Interquartile range (IQR)18

Descriptive statistics

Standard deviation10.271233
Coefficient of variation (CV)5.0795338 × 10-7
Kurtosis-1.3821877
Mean20220817
Median Absolute Deviation (MAD)9
Skewness-0.20330433
Sum2.0220817 × 1011
Variance105.49822
MonotonicityNot monotonic
2024-05-11T02:28:33.551941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20220831 1104
 
11.0%
20220801 649
 
6.5%
20220830 561
 
5.6%
20220825 538
 
5.4%
20220829 498
 
5.0%
20220810 481
 
4.8%
20220822 438
 
4.4%
20220826 398
 
4.0%
20220802 379
 
3.8%
20220805 367
 
3.7%
Other values (21) 4587
45.9%
ValueCountFrequency (%)
20220801 649
6.5%
20220802 379
3.8%
20220803 317
3.2%
20220804 341
3.4%
20220805 367
3.7%
20220806 77
 
0.8%
20220807 74
 
0.7%
20220808 342
3.4%
20220809 272
2.7%
20220810 481
4.8%
ValueCountFrequency (%)
20220831 1104
11.0%
20220830 561
5.6%
20220829 498
5.0%
20220828 167
 
1.7%
20220827 148
 
1.5%
20220826 398
 
4.0%
20220825 538
5.4%
20220824 341
 
3.4%
20220823 362
 
3.6%
20220822 438
 
4.4%

금액
Real number (ℝ)

SKEWED 

Distinct3250
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268808.48
Minimum-11292000
Maximum65860100
Zeros13
Zeros (%)0.1%
Negative42
Negative (%)0.4%
Memory size166.0 KiB
2024-05-11T02:28:34.198536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-11292000
5-th percentile150
Q12330
median30000
Q3100000
95-th percentile931211.35
Maximum65860100
Range77152100
Interquartile range (IQR)97670

Descriptive statistics

Standard deviation1588260.2
Coefficient of variation (CV)5.9085198
Kurtosis603.13352
Mean268808.48
Median Absolute Deviation (MAD)29025
Skewness20.120519
Sum2.6880848 × 109
Variance2.5225706 × 1012
MonotonicityNot monotonic
2024-05-11T02:28:34.737849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 594
 
5.9%
100000 497
 
5.0%
30000 488
 
4.9%
70000 176
 
1.8%
60000 170
 
1.7%
150000 155
 
1.6%
200000 142
 
1.4%
40000 136
 
1.4%
20000 114
 
1.1%
80000 112
 
1.1%
Other values (3240) 7416
74.2%
ValueCountFrequency (%)
-11292000 1
< 0.1%
-4464840 1
< 0.1%
-1639140 1
< 0.1%
-1426240 1
< 0.1%
-1300000 1
< 0.1%
-960000 1
< 0.1%
-755020 1
< 0.1%
-607500 1
< 0.1%
-454000 1
< 0.1%
-374230 1
< 0.1%
ValueCountFrequency (%)
65860100 1
< 0.1%
50934390 1
< 0.1%
48585220 1
< 0.1%
45085980 1
< 0.1%
34065000 1
< 0.1%
26759000 1
< 0.1%
26353570 1
< 0.1%
23520000 1
< 0.1%
23410280 1
< 0.1%
19500000 1
< 0.1%

내용
Text

Distinct5651
Distinct (%)56.6%
Missing8
Missing (%)0.1%
Memory size156.2 KiB
2024-05-11T02:28:35.819501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length79
Median length68
Mean length14.27472
Min length2

Characters and Unicode

Total characters142633
Distinct characters726
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks9 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5413 ?
Unique (%)54.2%

Sample

1st row118동 901호 전입 승강기사용료
2nd row관리비 연체료 수납
3rd row커뮤니티 PT 레슨(강대현/101-1203)
4th row관리비 연체료 수납
5th row8월분 커뮤니티 부과 관리비
ValueCountFrequency (%)
관리비 3867
 
14.2%
연체료 3722
 
13.6%
수납 3720
 
13.6%
8월분 400
 
1.5%
356
 
1.3%
승강기 318
 
1.2%
승강기사용료 263
 
1.0%
8월 248
 
0.9%
사용료 238
 
0.9%
7월분 221
 
0.8%
Other values (7435) 13920
51.0%
2024-05-11T02:28:37.667920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17631
 
12.4%
료 5947
 
4.2%
수 5224
 
3.7%
0 5117
 
3.6%
1 4776
 
3.3%
비 4604
 
3.2%
리 4381
 
3.1%
관 4070
 
2.9%
체 3957
 
2.8%
연 3827
 
2.7%
Other values (716) 83099
58.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89174
62.5%
Decimal Number 23054
 
16.2%
Space Separator 17631
 
12.4%
Other Punctuation 3075
 
2.2%
Close Punctuation 2902
 
2.0%
Open Punctuation 2895
 
2.0%
Dash Punctuation 2565
 
1.8%
Uppercase Letter 735
 
0.5%
Math Symbol 335
 
0.2%
Lowercase Letter 166
 
0.1%
Other values (2) 101
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
료 5947
 
6.7%
수 5224
 
5.9%
비 4604
 
5.2%
리 4381
 
4.9%
관 4070
 
4.6%
체 3957
 
4.4%
연 3827
 
4.3%
납 3790
 
4.3%
용 1885
 
2.1%
월 1754
 
2.0%
Other values (629) 49735
55.8%
Uppercase Letter
ValueCountFrequency (%)
N 91
12.4%
C 59
 
8.0%
T 59
 
8.0%
K 57
 
7.8%
O 52
 
7.1%
B 52
 
7.1%
A 50
 
6.8%
L 43
 
5.9%
S 34
 
4.6%
E 33
 
4.5%
Other values (15) 205
27.9%
Lowercase Letter
ValueCountFrequency (%)
o 53
31.9%
e 17
 
10.2%
n 15
 
9.0%
s 12
 
7.2%
k 10
 
6.0%
t 7
 
4.2%
a 7
 
4.2%
c 6
 
3.6%
f 5
 
3.0%
x 5
 
3.0%
Other values (12) 29
17.5%
Other Punctuation
ValueCountFrequency (%)
/ 887
28.8%
. 814
26.5%
, 651
21.2%
? 369
12.0%
: 193
 
6.3%
* 85
 
2.8%
@ 33
 
1.1%
% 24
 
0.8%
& 7
 
0.2%
' 5
 
0.2%
Other values (4) 7
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 5117
22.2%
1 4776
20.7%
2 3620
15.7%
8 2316
10.0%
3 1728
 
7.5%
4 1247
 
5.4%
7 1247
 
5.4%
5 1144
 
5.0%
9 936
 
4.1%
6 923
 
4.0%
Math Symbol
ValueCountFrequency (%)
~ 273
81.5%
> 17
 
5.1%
× 16
 
4.8%
+ 15
 
4.5%
= 6
 
1.8%
< 6
 
1.8%
∼ 1
 
0.3%
⇒ 1
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 2828
97.5%
] 74
 
2.5%
Open Punctuation
ValueCountFrequency (%)
( 2820
97.4%
[ 75
 
2.6%
Space Separator
ValueCountFrequency (%)
17631
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2565
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 100
100.0%
Other Symbol
ValueCountFrequency (%)
▶ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89166
62.5%
Common 52558
36.8%
Latin 901
 
0.6%
Han 8
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
료 5947
 
6.7%
수 5224
 
5.9%
비 4604
 
5.2%
리 4381
 
4.9%
관 4070
 
4.6%
체 3957
 
4.4%
연 3827
 
4.3%
납 3790
 
4.3%
용 1885
 
2.1%
월 1754
 
2.0%
Other values (626) 49727
55.8%
Latin
ValueCountFrequency (%)
N 91
 
10.1%
C 59
 
6.5%
T 59
 
6.5%
K 57
 
6.3%
o 53
 
5.9%
O 52
 
5.8%
B 52
 
5.8%
A 50
 
5.5%
L 43
 
4.8%
S 34
 
3.8%
Other values (37) 351
39.0%
Common
ValueCountFrequency (%)
17631
33.5%
0 5117
 
9.7%
1 4776
 
9.1%
2 3620
 
6.9%
) 2828
 
5.4%
( 2820
 
5.4%
- 2565
 
4.9%
8 2316
 
4.4%
3 1728
 
3.3%
4 1247
 
2.4%
Other values (30) 7910
15.1%
Han
ValueCountFrequency (%)
入 6
75.0%
不 1
 
12.5%
內 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89165
62.5%
ASCII 53439
37.5%
None 17
 
< 0.1%
CJK 7
 
< 0.1%
Math Operators 1
 
< 0.1%
Compat Jamo 1
 
< 0.1%
Geometric Shapes 1
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%
Arrows 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17631
33.0%
0 5117
 
9.6%
1 4776
 
8.9%
2 3620
 
6.8%
) 2828
 
5.3%
( 2820
 
5.3%
- 2565
 
4.8%
8 2316
 
4.3%
3 1728
 
3.2%
4 1247
 
2.3%
Other values (72) 8791
16.5%
Hangul
ValueCountFrequency (%)
료 5947
 
6.7%
수 5224
 
5.9%
비 4604
 
5.2%
리 4381
 
4.9%
관 4070
 
4.6%
체 3957
 
4.4%
연 3827
 
4.3%
납 3790
 
4.3%
용 1885
 
2.1%
월 1754
 
2.0%
Other values (625) 49726
55.8%
None
ValueCountFrequency (%)
× 16
94.1%
？ 1
 
5.9%
CJK
ValueCountFrequency (%)
入 6
85.7%
內 1
 
14.3%
Math Operators
ValueCountFrequency (%)
∼ 1
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅡ 1
100.0%
Geometric Shapes
ValueCountFrequency (%)
▶ 1
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
不 1
100.0%
Arrows
ValueCountFrequency (%)
⇒ 1
100.0%

Interactions

2024-05-11T02:28:26.855871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:28:26.167640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:28:27.149607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:28:26.454561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T02:28:37.957205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비용명년월일금액
비용명1.0000.3340.255
년월일0.3341.0000.078
금액0.2550.0781.000
2024-05-11T02:28:38.619810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월일금액비용명
년월일1.0000.0720.132
금액0.0721.0000.106
비용명0.1320.1061.000

Missing values

2024-05-11T02:28:27.574177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T02:28:27.855257image/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.
2024-05-11T02:28:28.107002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

아파트명아파트코드비용명년월일금액내용
6600래미안에스티움A10027073승강기수익2022081570000118동 901호 전입 승강기사용료
57496양천롯데캐슬A15883202연체료수익202208264240관리비 연체료 수납
21124서울숲더샵A13307003기타운영수익20220816300000커뮤니티 PT 레슨(강대현/101-1203)
21889어울림더리버아파트A13375906연체료수익20220802490관리비 연체료 수납
31889서초호반써밋A13778211기타운영수익2022083195312008월분 커뮤니티 부과 관리비
57512신정뉴타운두산위브A15883401주차장수익20220805200008월주차비 유정인-102-210호
35545거여현대2차A13881401주차장수익202208311523000주차시설이용료
33588금호어울림1차A13812003잡수익2022083120190음식물부과할인액
2324구로항동우남퍼스트빌A10024849기타운영수익20220810500고용,산재,연금 자동이체할인
30459길음뉴타운7단지A13679403공동주택지원금수익2022080310000요가-박혜란,김정선
아파트명아파트코드비용명년월일금액내용
5334래미안미드카운티A10026232잡수익2022080197월 전기료 국민카드 납부 할인액 끝전처리
30014래미안라센트A13671209승강기수익2022081850000No.629(승) - 107동 704호 인테리어공사시 승강기사용료 -
3544항동하버라인4단지아파트A10025302잡수익20220819594음식물처리기 카드판매-임대 분 36.03%
47583해태보라매타워A15183001임대료수익20220809427000b4-1~4 목동악기-7월분
9079신당삼성(분양)A10045403연체료수익202208307090관리비 연체료 수납
7576상도2차 두산위브트레지움 아파트A10027633연체료수익20220809660관리비 연체료 수납
18366신내8단지두산화성A13187201검침수익202208186104002022년 08월분 수도 위탁검침비
11705염리삼성래미안A12109002광고료수익2022082240000게시판(보일러, 수도 배관청소)
49796구로현대A15288004광고료수익20220831795508월분 게시판/승강기거울광고 1,909,090*1/24(209회)
58219은평뉴타운구파발9단지1관리A41279921연체료수익2022081010420관리비 연체료 수납