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

Alerts

금액 is highly skewed (γ1 = 33.41113468)Skewed

Reproduction

Analysis started2024-05-11 02:25:11.397953
Analysis finished2024-05-11 02:25:14.845680
Duration3.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

Distinct2171
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:25:15.061222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length20
Mean length7.5349
Min length2

Characters and Unicode

Total characters75349
Distinct characters429
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

Unique279 ?
Unique (%)2.8%

Sample

1st row무악현대
2nd row서초진흥
3rd row상도래미안1차제2
4th row금호자이2차
5th row관악우성아파트
ValueCountFrequency (%)
아파트 215
 
1.9%
래미안 78
 
0.7%
e편한세상 44
 
0.4%
아이파크 38
 
0.3%
고덕 30
 
0.3%
디에이치 28
 
0.3%
이편한세상 26
 
0.2%
송파 25
 
0.2%
영등포 25
 
0.2%
sk뷰 24
 
0.2%
Other values (2254) 10561
95.2%
2024-05-11T02:25:15.778918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
파 2634
 
3.5%
아 2619
 
3.5%
트 2580
 
3.4%
지 2039
 
2.7%
대 1581
 
2.1%
이 1572
 
2.1%
단 1568
 
2.1%
동 1535
 
2.0%
신 1253
 
1.7%
차 1243
 
1.6%
Other values (419) 56725
75.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68937
91.5%
Decimal Number 3486
 
4.6%
Space Separator 1200
 
1.6%
Uppercase Letter 861
 
1.1%
Lowercase Letter 330
 
0.4%
Open Punctuation 149
 
0.2%
Close Punctuation 149
 
0.2%
Dash Punctuation 120
 
0.2%
Other Punctuation 108
 
0.1%
Letter Number 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
파 2634
 
3.8%
아 2619
 
3.8%
트 2580
 
3.7%
지 2039
 
3.0%
대 1581
 
2.3%
이 1572
 
2.3%
단 1568
 
2.3%
동 1535
 
2.2%
신 1253
 
1.8%
차 1243
 
1.8%
Other values (374) 50313
73.0%
Uppercase Letter
ValueCountFrequency (%)
S 151
17.5%
C 130
15.1%
K 120
13.9%
D 98
11.4%
M 98
11.4%
H 46
 
5.3%
I 36
 
4.2%
L 29
 
3.4%
E 29
 
3.4%
V 24
 
2.8%
Other values (7) 100
11.6%
Lowercase Letter
ValueCountFrequency (%)
e 204
61.8%
l 28
 
8.5%
s 26
 
7.9%
k 22
 
6.7%
i 21
 
6.4%
v 14
 
4.2%
w 6
 
1.8%
h 5
 
1.5%
c 2
 
0.6%
a 1
 
0.3%
Decimal Number
ValueCountFrequency (%)
1 1030
29.5%
2 963
27.6%
3 474
13.6%
4 257
 
7.4%
5 229
 
6.6%
6 156
 
4.5%
7 134
 
3.8%
9 106
 
3.0%
8 89
 
2.6%
0 48
 
1.4%
Other Punctuation
ValueCountFrequency (%)
, 86
79.6%
. 22
 
20.4%
Space Separator
ValueCountFrequency (%)
1200
100.0%
Open Punctuation
ValueCountFrequency (%)
( 149
100.0%
Close Punctuation
ValueCountFrequency (%)
) 149
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 120
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68937
91.5%
Common 5212
 
6.9%
Latin 1200
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
파 2634
 
3.8%
아 2619
 
3.8%
트 2580
 
3.7%
지 2039
 
3.0%
대 1581
 
2.3%
이 1572
 
2.3%
단 1568
 
2.3%
동 1535
 
2.2%
신 1253
 
1.8%
차 1243
 
1.8%
Other values (374) 50313
73.0%
Latin
ValueCountFrequency (%)
e 204
17.0%
S 151
12.6%
C 130
10.8%
K 120
10.0%
D 98
 
8.2%
M 98
 
8.2%
H 46
 
3.8%
I 36
 
3.0%
L 29
 
2.4%
E 29
 
2.4%
Other values (19) 259
21.6%
Common
ValueCountFrequency (%)
1200
23.0%
1 1030
19.8%
2 963
18.5%
3 474
 
9.1%
4 257
 
4.9%
5 229
 
4.4%
6 156
 
3.0%
( 149
 
2.9%
) 149
 
2.9%
7 134
 
2.6%
Other values (6) 471
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68937
91.5%
ASCII 6403
 
8.5%
Number Forms 9
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
파 2634
 
3.8%
아 2619
 
3.8%
트 2580
 
3.7%
지 2039
 
3.0%
대 1581
 
2.3%
이 1572
 
2.3%
단 1568
 
2.3%
동 1535
 
2.2%
신 1253
 
1.8%
차 1243
 
1.8%
Other values (374) 50313
73.0%
ASCII
ValueCountFrequency (%)
1200
18.7%
1 1030
16.1%
2 963
15.0%
3 474
 
7.4%
4 257
 
4.0%
5 229
 
3.6%
e 204
 
3.2%
6 156
 
2.4%
S 151
 
2.4%
( 149
 
2.3%
Other values (34) 1590
24.8%
Number Forms
ValueCountFrequency (%)
Ⅰ 9
100.0%

아파트코드
Text

Distinct2175
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:25:16.374637image/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

Unique282 ?
Unique (%)2.8%

Sample

1st rowA11081503
2nd rowA13785604
3rd rowA15603007
4th rowA13379001
5th rowA15105603
ValueCountFrequency (%)
a13778204 22
 
0.2%
a13822004 21
 
0.2%
a13822003 19
 
0.2%
a13527203 19
 
0.2%
a12179004 18
 
0.2%
a10026941 18
 
0.2%
a13824006 18
 
0.2%
a10023887 18
 
0.2%
a12175203 18
 
0.2%
a15728009 17
 
0.2%
Other values (2165) 9812
98.1%
2024-05-11T02:25:17.200846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 19028
21.1%
1 17002
18.9%
A 9990
11.1%
3 8650
9.6%
2 8576
9.5%
5 6169
 
6.9%
8 5404
 
6.0%
7 4751
 
5.3%
4 4081
 
4.5%
6 3454
 
3.8%
Other values (2) 2895
 
3.2%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19028
23.8%
1 17002
21.3%
3 8650
10.8%
2 8576
10.7%
5 6169
 
7.7%
8 5404
 
6.8%
7 4751
 
5.9%
4 4081
 
5.1%
6 3454
 
4.3%
9 2885
 
3.6%
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 19028
23.8%
1 17002
21.3%
3 8650
10.8%
2 8576
10.7%
5 6169
 
7.7%
8 5404
 
6.8%
7 4751
 
5.9%
4 4081
 
5.1%
6 3454
 
4.3%
9 2885
 
3.6%
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 19028
21.1%
1 17002
18.9%
A 9990
11.1%
3 8650
9.6%
2 8576
9.5%
5 6169
 
6.9%
8 5404
 
6.0%
7 4751
 
5.3%
4 4081
 
4.5%
6 3454
 
3.8%
Other values (2) 2895
 
3.2%

비용명
Categorical

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
연체료수익
3598 
승강기수익
1257 
잡수익
1007 
기타운영수익
1005 
주차장수익
893 
Other values (10)
2240 

Length

Max length9
Median length5
Mean length4.889
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
연체료수익 3598
36.0%
승강기수익 1257
 
12.6%
잡수익 1007
 
10.1%
기타운영수익 1005
 
10.1%
주차장수익 893
 
8.9%
광고료수익 893
 
8.9%
검침수익 324
 
3.2%
부과차익 251
 
2.5%
임대료수익 224
 
2.2%
재활용품수익 216
 
2.2%
Other values (5) 332
 
3.3%

Length

2024-05-11T02:25:17.478585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
연체료수익 3598
36.0%
승강기수익 1257
 
12.6%
잡수익 1007
 
10.1%
기타운영수익 1005
 
10.1%
주차장수익 893
 
8.9%
광고료수익 893
 
8.9%
검침수익 324
 
3.2%
부과차익 251
 
2.5%
임대료수익 224
 
2.2%
재활용품수익 216
 
2.2%
Other values (5) 332
 
3.3%

년월일
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20230818
Minimum20230801
Maximum20230831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:25:17.711298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20230801
5-th percentile20230801
Q120230809
median20230819
Q320230828
95-th percentile20230831
Maximum20230831
Range30
Interquartile range (IQR)19

Descriptive statistics

Standard deviation10.099319
Coefficient of variation (CV)4.992047 × 10-7
Kurtosis-1.3381689
Mean20230818
Median Absolute Deviation (MAD)9
Skewness-0.23250904
Sum2.0230818 × 1011
Variance101.99625
MonotonicityNot monotonic
2024-05-11T02:25:17.950724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20230831 1114
 
11.1%
20230830 555
 
5.5%
20230801 548
 
5.5%
20230825 531
 
5.3%
20230821 458
 
4.6%
20230828 444
 
4.4%
20230810 431
 
4.3%
20230807 394
 
3.9%
20230829 393
 
3.9%
20230824 386
 
3.9%
Other values (21) 4746
47.5%
ValueCountFrequency (%)
20230801 548
5.5%
20230802 376
3.8%
20230803 339
3.4%
20230804 320
3.2%
20230805 96
 
1.0%
20230806 85
 
0.9%
20230807 394
3.9%
20230808 322
3.2%
20230809 283
2.8%
20230810 431
4.3%
ValueCountFrequency (%)
20230831 1114
11.1%
20230830 555
5.5%
20230829 393
 
3.9%
20230828 444
 
4.4%
20230827 183
 
1.8%
20230826 168
 
1.7%
20230825 531
5.3%
20230824 386
 
3.9%
20230823 326
 
3.3%
20230822 334
 
3.3%

금액
Real number (ℝ)

SKEWED 

Distinct3276
Distinct (%)32.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean333265.72
Minimum-53630000
Maximum1.6938 × 108
Zeros11
Zeros (%)0.1%
Negative42
Negative (%)0.4%
Memory size166.0 KiB
2024-05-11T02:25:18.506869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-53630000
5-th percentile160
Q12400
median30000
Q3100000
95-th percentile967048.2
Maximum1.6938 × 108
Range2.2301 × 108
Interquartile range (IQR)97600

Descriptive statistics

Standard deviation3440408.8
Coefficient of variation (CV)10.32332
Kurtosis1453.5721
Mean333265.72
Median Absolute Deviation (MAD)29080
Skewness33.411135
Sum3.3323239 × 109
Variance1.1836412 × 1013
MonotonicityNot monotonic
2024-05-11T02:25:18.932519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 551
 
5.5%
30000 546
 
5.5%
100000 545
 
5.5%
150000 195
 
1.9%
70000 148
 
1.5%
40000 147
 
1.5%
200000 141
 
1.4%
60000 130
 
1.3%
20000 110
 
1.1%
80000 99
 
1.0%
Other values (3266) 7387
73.9%
ValueCountFrequency (%)
-53630000 1
 
< 0.1%
-16062000 1
 
< 0.1%
-1206924 1
 
< 0.1%
-1000000 1
 
< 0.1%
-629200 1
 
< 0.1%
-450000 1
 
< 0.1%
-440000 1
 
< 0.1%
-260000 1
 
< 0.1%
-170000 1
 
< 0.1%
-150000 3
< 0.1%
ValueCountFrequency (%)
169380000 1
< 0.1%
164895453 1
< 0.1%
136080000 1
< 0.1%
100620000 1
< 0.1%
59840000 1
< 0.1%
48374689 1
< 0.1%
46969791 1
< 0.1%
41934000 1
< 0.1%
40653200 1
< 0.1%
40000000 1
< 0.1%

내용
Text

Distinct5773
Distinct (%)57.8%
Missing9
Missing (%)0.1%
Memory size156.2 KiB
2024-05-11T02:25:19.633874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length78
Median length68
Mean length14.871584
Min length2

Characters and Unicode

Total characters148582
Distinct characters738
Distinct categories14 ?
Distinct scripts4 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5538 ?
Unique (%)55.4%

Sample

1st row음식물종량제카드 발급(108-704)
2nd row관리비 연체료 수납
3rd row외부주차료(김경훈)
4th row2023. 8월 운동시설이용료(403세대x5,000원)
5th row재활용수입-8월분
ValueCountFrequency (%)
관리비 3751
 
13.5%
연체료 3603
 
13.0%
수납 3602
 
13.0%
승강기 409
 
1.5%
372
 
1.3%
8월분 342
 
1.2%
승강기사용료 276
 
1.0%
사용료 272
 
1.0%
8월 252
 
0.9%
7월분 228
 
0.8%
Other values (7811) 14580
52.7%
2024-05-11T02:25:20.661695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18115
 
12.2%
료 5951
 
4.0%
0 5736
 
3.9%
1 5363
 
3.6%
수 5178
 
3.5%
비 4439
 
3.0%
리 4315
 
2.9%
관 3964
 
2.7%
체 3830
 
2.6%
2 3715
 
2.5%
Other values (728) 87976
59.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 91508
61.6%
Decimal Number 25384
 
17.1%
Space Separator 18115
 
12.2%
Other Punctuation 3199
 
2.2%
Open Punctuation 3170
 
2.1%
Close Punctuation 3162
 
2.1%
Dash Punctuation 2717
 
1.8%
Uppercase Letter 702
 
0.5%
Math Symbol 400
 
0.3%
Connector Punctuation 114
 
0.1%
Other values (4) 111
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
료 5951
 
6.5%
수 5178
 
5.7%
비 4439
 
4.9%
리 4315
 
4.7%
관 3964
 
4.3%
체 3830
 
4.2%
연 3706
 
4.0%
납 3657
 
4.0%
용 2037
 
2.2%
사 1943
 
2.1%
Other values (647) 52488
57.4%
Uppercase Letter
ValueCountFrequency (%)
N 89
 
12.7%
A 58
 
8.3%
T 47
 
6.7%
C 46
 
6.6%
G 43
 
6.1%
B 42
 
6.0%
O 40
 
5.7%
K 39
 
5.6%
L 34
 
4.8%
D 31
 
4.4%
Other values (14) 233
33.2%
Lowercase Letter
ValueCountFrequency (%)
o 41
38.3%
k 18
16.8%
s 8
 
7.5%
n 8
 
7.5%
c 7
 
6.5%
t 6
 
5.6%
x 5
 
4.7%
e 3
 
2.8%
p 2
 
1.9%
w 2
 
1.9%
Other values (5) 7
 
6.5%
Other Punctuation
ValueCountFrequency (%)
/ 1052
32.9%
. 975
30.5%
, 744
23.3%
: 211
 
6.6%
* 104
 
3.3%
@ 44
 
1.4%
% 35
 
1.1%
' 10
 
0.3%
# 10
 
0.3%
& 8
 
0.3%
Other values (4) 6
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 5736
22.6%
1 5363
21.1%
2 3715
14.6%
8 2309
9.1%
3 2263
 
8.9%
4 1527
 
6.0%
7 1352
 
5.3%
5 1217
 
4.8%
9 956
 
3.8%
6 946
 
3.7%
Math Symbol
ValueCountFrequency (%)
~ 332
83.0%
+ 18
 
4.5%
> 17
 
4.2%
× 12
 
3.0%
= 11
 
2.8%
< 6
 
1.5%
÷ 3
 
0.8%
→ 1
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 3074
97.0%
[ 96
 
3.0%
Close Punctuation
ValueCountFrequency (%)
) 3066
97.0%
] 96
 
3.0%
Space Separator
ValueCountFrequency (%)
18115
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2717
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 114
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 2
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%
Other Number
ValueCountFrequency (%)
① 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 91501
61.6%
Common 56265
37.9%
Latin 809
 
0.5%
Han 7
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
료 5951
 
6.5%
수 5178
 
5.7%
비 4439
 
4.9%
리 4315
 
4.7%
관 3964
 
4.3%
체 3830
 
4.2%
연 3706
 
4.1%
납 3657
 
4.0%
용 2037
 
2.2%
사 1943
 
2.1%
Other values (644) 52481
57.4%
Common
ValueCountFrequency (%)
18115
32.2%
0 5736
 
10.2%
1 5363
 
9.5%
2 3715
 
6.6%
( 3074
 
5.5%
) 3066
 
5.4%
- 2717
 
4.8%
8 2309
 
4.1%
3 2263
 
4.0%
4 1527
 
2.7%
Other values (32) 8380
14.9%
Latin
ValueCountFrequency (%)
N 89
 
11.0%
A 58
 
7.2%
T 47
 
5.8%
C 46
 
5.7%
G 43
 
5.3%
B 42
 
5.2%
o 41
 
5.1%
O 40
 
4.9%
K 39
 
4.8%
L 34
 
4.2%
Other values (29) 330
40.8%
Han
ValueCountFrequency (%)
入 4
57.1%
中 2
28.6%
男 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 91501
61.6%
ASCII 57053
38.4%
None 16
 
< 0.1%
CJK 7
 
< 0.1%
CJK Compat 2
 
< 0.1%
Enclosed Alphanum 1
 
< 0.1%
Arrows 1
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18115
31.8%
0 5736
 
10.1%
1 5363
 
9.4%
2 3715
 
6.5%
( 3074
 
5.4%
) 3066
 
5.4%
- 2717
 
4.8%
8 2309
 
4.0%
3 2263
 
4.0%
4 1527
 
2.7%
Other values (64) 9168
16.1%
Hangul
ValueCountFrequency (%)
료 5951
 
6.5%
수 5178
 
5.7%
비 4439
 
4.9%
리 4315
 
4.7%
관 3964
 
4.3%
체 3830
 
4.2%
연 3706
 
4.1%
납 3657
 
4.0%
용 2037
 
2.2%
사 1943
 
2.1%
Other values (644) 52481
57.4%
None
ValueCountFrequency (%)
× 12
75.0%
÷ 3
 
18.8%
· 1
 
6.2%
CJK
ValueCountFrequency (%)
入 4
57.1%
中 2
28.6%
男 1
 
14.3%
CJK Compat
ValueCountFrequency (%)
㎡ 2
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
① 1
100.0%
Arrows
ValueCountFrequency (%)
→ 1
100.0%
Punctuation
ValueCountFrequency (%)
※ 1
100.0%

Interactions

2024-05-11T02:25:13.598231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:25:13.056535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:25:13.866585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:25:13.333055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T02:25:20.909512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비용명년월일금액
비용명1.0000.3320.240
년월일0.3321.0000.050
금액0.2400.0501.000
2024-05-11T02:25:21.143800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월일금액비용명
년월일1.0000.0390.132
금액0.0391.0000.100
비용명0.1320.1001.000

Missing values

2024-05-11T02:25:14.205905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T02:25:14.574485image/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.
2024-05-11T02:25:14.748901image/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
11027무악현대A11081503잡수익202308244400음식물종량제카드 발급(108-704)
33390서초진흥A13785604연체료수익202308291530관리비 연체료 수납
51656상도래미안1차제2A15603007주차장수익2023081770000외부주차료(김경훈)
23375금호자이2차A13379001기타운영수익2023083120150002023. 8월 운동시설이용료(403세대x5,000원)
47521관악우성아파트A15105603재활용품수익202308011597000재활용수입-8월분
53088대방현대1차A15681106검침수익20230816221020한전검침 수당
51732아이파크상도동A15603203광고료수익2023080730000게시판광고(제니스튜디오 상도점)
35148잠실동트리지움A13822002승강기수익20230802150000306-1002(8/2전입) 승강기사용료-육동천이호전입
32158래미안서초유니빌A13707010승강기수익20230831700001109호 전입
949롯데캐슬클라시아A10023926연체료수익202308309830관리비 연체료 수납
아파트명아파트코드비용명년월일금액내용
9457텐즈힐1단지A10027920승강기수익2023082430000106-401 전입 승강기보양재사용료
54749우장산힐스테이트A15728009연체료수익202308011000관리비 연체료 수납
51049독산한신A15383307연체료수익20230825110관리비 연체료 수납
55460가양9-2A15781003잡수익202308179007월 부과차익
2623서초센트럴아이파크A10024574승강기수익20230828100000102-1201 이사 시 승강기 사용료 입금
21813하왕금호베스트빌A13302204승강기수익2023083055000승강기 사용료(내부수리,105-901호)
49335고척LIG리가아파트A15279402잡수익202308105007월 고용,산재보험 자동이체 할인액
30187길음뉴타운푸르지오아파트2,3단지A13611007기타운영수익20230819175800스포츠센터 이용(카드결재)(8월 22일) 입금예정
12246DMC센트레빌A12072801연체료수익20230804340관리비 연체료 수납
29660브라운스톤 돈암A13606201기타운영수익2023080995000헬스장 수입