Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells9
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 = 36.15585118)Skewed

Reproduction

Analysis started2024-05-11 02:30:14.978607
Analysis finished2024-05-11 02:30:19.453211
Duration4.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

Distinct2147
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:30:19.868359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length19
Mean length7.3562
Min length2

Characters and Unicode

Total characters73562
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

Unique222 ?
Unique (%)2.2%

Sample

1st row우장산에스케이뷰
2nd row둔촌신동아
3rd row하계2차현대아파트
4th row반포미도아파트
5th row광장현대파크빌
ValueCountFrequency (%)
아파트 191
 
1.8%
래미안 52
 
0.5%
e편한세상 24
 
0.2%
은마 23
 
0.2%
아이파크 22
 
0.2%
잠실5단지아파트 19
 
0.2%
경남아너스빌 18
 
0.2%
해모로 18
 
0.2%
잠실리센츠 18
 
0.2%
롯데캐슬노블레스 18
 
0.2%
Other values (2223) 10404
96.3%
2024-05-11T02:30:21.096010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 2540
 
3.5%
파 2513
 
3.4%
트 2311
 
3.1%
지 2032
 
2.8%
단 1634
 
2.2%
대 1611
 
2.2%
동 1542
 
2.1%
이 1412
 
1.9%
차 1406
 
1.9%
신 1382
 
1.9%
Other values (419) 55179
75.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67192
91.3%
Decimal Number 3801
 
5.2%
Space Separator 919
 
1.2%
Uppercase Letter 898
 
1.2%
Lowercase Letter 292
 
0.4%
Dash Punctuation 116
 
0.2%
Open Punctuation 114
 
0.2%
Close Punctuation 114
 
0.2%
Other Punctuation 114
 
0.2%
Letter Number 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 2540
 
3.8%
파 2513
 
3.7%
트 2311
 
3.4%
지 2032
 
3.0%
단 1634
 
2.4%
대 1611
 
2.4%
동 1542
 
2.3%
이 1412
 
2.1%
차 1406
 
2.1%
신 1382
 
2.1%
Other values (375) 48809
72.6%
Uppercase Letter
ValueCountFrequency (%)
S 163
18.2%
K 120
13.4%
C 117
13.0%
M 73
8.1%
D 73
8.1%
L 70
7.8%
H 62
 
6.9%
I 43
 
4.8%
E 40
 
4.5%
G 28
 
3.1%
Other values (7) 109
12.1%
Decimal Number
ValueCountFrequency (%)
1 1156
30.4%
2 1076
28.3%
3 505
13.3%
4 251
 
6.6%
5 250
 
6.6%
6 156
 
4.1%
7 143
 
3.8%
9 123
 
3.2%
8 74
 
1.9%
0 67
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
e 193
66.1%
i 19
 
6.5%
k 17
 
5.8%
l 16
 
5.5%
s 13
 
4.5%
v 11
 
3.8%
c 8
 
2.7%
w 7
 
2.4%
g 4
 
1.4%
a 4
 
1.4%
Other Punctuation
ValueCountFrequency (%)
, 96
84.2%
. 18
 
15.8%
Space Separator
ValueCountFrequency (%)
919
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 116
100.0%
Open Punctuation
ValueCountFrequency (%)
( 114
100.0%
Close Punctuation
ValueCountFrequency (%)
) 114
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67192
91.3%
Common 5178
 
7.0%
Latin 1192
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 2540
 
3.8%
파 2513
 
3.7%
트 2311
 
3.4%
지 2032
 
3.0%
단 1634
 
2.4%
대 1611
 
2.4%
동 1542
 
2.3%
이 1412
 
2.1%
차 1406
 
2.1%
신 1382
 
2.1%
Other values (375) 48809
72.6%
Latin
ValueCountFrequency (%)
e 193
16.2%
S 163
13.7%
K 120
10.1%
C 117
9.8%
M 73
 
6.1%
D 73
 
6.1%
L 70
 
5.9%
H 62
 
5.2%
I 43
 
3.6%
E 40
 
3.4%
Other values (18) 238
20.0%
Common
ValueCountFrequency (%)
1 1156
22.3%
2 1076
20.8%
919
17.7%
3 505
9.8%
4 251
 
4.8%
5 250
 
4.8%
6 156
 
3.0%
7 143
 
2.8%
9 123
 
2.4%
- 116
 
2.2%
Other values (6) 483
9.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67192
91.3%
ASCII 6368
 
8.7%
Number Forms 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 2540
 
3.8%
파 2513
 
3.7%
트 2311
 
3.4%
지 2032
 
3.0%
단 1634
 
2.4%
대 1611
 
2.4%
동 1542
 
2.3%
이 1412
 
2.1%
차 1406
 
2.1%
신 1382
 
2.1%
Other values (375) 48809
72.6%
ASCII
ValueCountFrequency (%)
1 1156
18.2%
2 1076
16.9%
919
14.4%
3 505
 
7.9%
4 251
 
3.9%
5 250
 
3.9%
e 193
 
3.0%
S 163
 
2.6%
6 156
 
2.4%
7 143
 
2.2%
Other values (33) 1556
24.4%
Number Forms
ValueCountFrequency (%)
Ⅰ 2
100.0%

아파트코드
Text

Distinct2153
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:30:22.031703image/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

Unique222 ?
Unique (%)2.2%

Sample

1st rowA15701002
2nd rowA13406204
3rd rowA13923106
4th rowA13704404
5th rowA14381516
ValueCountFrequency (%)
a13583507 23
 
0.2%
a13879102 19
 
0.2%
a15003002 18
 
0.2%
a13822003 18
 
0.2%
a15701003 18
 
0.2%
a10026180 18
 
0.2%
a10025614 17
 
0.2%
a10025461 17
 
0.2%
a15805115 16
 
0.2%
a13187305 16
 
0.2%
Other values (2143) 9820
98.2%
2024-05-11T02:30:23.466150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18530
20.6%
1 17085
19.0%
A 9995
11.1%
3 8889
9.9%
2 8156
9.1%
5 6443
 
7.2%
8 5540
 
6.2%
7 4846
 
5.4%
4 4115
 
4.6%
6 3342
 
3.7%
Other values (2) 3059
 
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 18530
23.2%
1 17085
21.4%
3 8889
11.1%
2 8156
10.2%
5 6443
 
8.1%
8 5540
 
6.9%
7 4846
 
6.1%
4 4115
 
5.1%
6 3342
 
4.2%
9 3054
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
A 9995
> 99.9%
B 5
 
< 0.1%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
0 18530
23.2%
1 17085
21.4%
3 8889
11.1%
2 8156
10.2%
5 6443
 
8.1%
8 5540
 
6.9%
7 4846
 
6.1%
4 4115
 
5.1%
6 3342
 
4.2%
9 3054
 
3.8%
Latin
ValueCountFrequency (%)
A 9995
> 99.9%
B 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18530
20.6%
1 17085
19.0%
A 9995
11.1%
3 8889
9.9%
2 8156
9.1%
5 6443
 
7.2%
8 5540
 
6.2%
7 4846
 
5.4%
4 4115
 
4.6%
6 3342
 
3.7%
Other values (2) 3059
 
3.4%

비용명
Categorical

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
연체료수익
3135 
승강기수익
977 
잡수익
948 
이자수익
933 
주차장수익
870 
Other values (10)
3137 

Length

Max length9
Median length5
Mean length4.9793
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
연체료수익 3135
31.4%
승강기수익 977
 
9.8%
잡수익 948
 
9.5%
이자수익 933
 
9.3%
주차장수익 870
 
8.7%
광고료수익 770
 
7.7%
기타운영수익 628
 
6.3%
고용안정사업수익 515
 
5.1%
검침수익 273
 
2.7%
알뜰시장수익 237
 
2.4%
Other values (5) 714
 
7.1%

Length

2024-05-11T02:30:24.121061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
연체료수익 3135
31.4%
승강기수익 977
 
9.8%
잡수익 948
 
9.5%
이자수익 933
 
9.3%
주차장수익 870
 
8.7%
광고료수익 770
 
7.7%
기타운영수익 628
 
6.3%
고용안정사업수익 515
 
5.1%
검침수익 273
 
2.7%
알뜰시장수익 237
 
2.4%
Other values (5) 714
 
7.1%

년월일
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20211218
Minimum20211201
Maximum20211231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:30:24.722660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20211201
5-th percentile20211202
Q120211210
median20211218
Q320211227
95-th percentile20211231
Maximum20211231
Range30
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.4627327
Coefficient of variation (CV)4.6819211 × 10-7
Kurtosis-1.182706
Mean20211218
Median Absolute Deviation (MAD)8
Skewness-0.20219168
Sum2.0211218 × 1011
Variance89.543311
MonotonicityNot monotonic
2024-05-11T02:30:25.354205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20211231 930
 
9.3%
20211230 470
 
4.7%
20211201 464
 
4.6%
20211227 449
 
4.5%
20211220 445
 
4.5%
20211215 435
 
4.3%
20211224 393
 
3.9%
20211210 390
 
3.9%
20211221 383
 
3.8%
20211228 368
 
3.7%
Other values (21) 5273
52.7%
ValueCountFrequency (%)
20211201 464
4.6%
20211202 313
3.1%
20211203 287
2.9%
20211204 76
 
0.8%
20211205 71
 
0.7%
20211206 363
3.6%
20211207 305
3.0%
20211208 237
2.4%
20211209 265
2.6%
20211210 390
3.9%
ValueCountFrequency (%)
20211231 930
9.3%
20211230 470
4.7%
20211229 355
 
3.5%
20211228 368
 
3.7%
20211227 449
4.5%
20211226 149
 
1.5%
20211225 272
 
2.7%
20211224 393
3.9%
20211223 307
 
3.1%
20211222 293
 
2.9%

금액
Real number (ℝ)

SKEWED 

Distinct3741
Distinct (%)37.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254271.41
Minimum-6480000
Maximum1.2464786 × 108
Zeros19
Zeros (%)0.2%
Negative58
Negative (%)0.6%
Memory size166.0 KiB
2024-05-11T02:30:25.795731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6480000
5-th percentile128.8
Q12429.25
median28000
Q3100000
95-th percentile824065.75
Maximum1.2464786 × 108
Range1.3112786 × 108
Interquartile range (IQR)97570.75

Descriptive statistics

Standard deviation2016904.1
Coefficient of variation (CV)7.9320917
Kurtosis1813.5438
Mean254271.41
Median Absolute Deviation (MAD)27030
Skewness36.155851
Sum2.5427141 × 109
Variance4.0679023 × 1012
MonotonicityNot monotonic
2024-05-11T02:30:26.367642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 554
 
5.5%
30000 479
 
4.8%
100000 465
 
4.7%
150000 174
 
1.7%
60000 171
 
1.7%
200000 165
 
1.7%
70000 129
 
1.3%
40000 119
 
1.2%
80000 101
 
1.0%
20000 94
 
0.9%
Other values (3731) 7549
75.5%
ValueCountFrequency (%)
-6480000 1
< 0.1%
-3546810 1
< 0.1%
-910000 1
< 0.1%
-473000 1
< 0.1%
-450000 1
< 0.1%
-414082 1
< 0.1%
-360000 1
< 0.1%
-300000 1
< 0.1%
-280000 1
< 0.1%
-247740 1
< 0.1%
ValueCountFrequency (%)
124647860 1
< 0.1%
74598812 1
< 0.1%
65337542 1
< 0.1%
51827119 1
< 0.1%
35306896 1
< 0.1%
34920000 1
< 0.1%
28711630 1
< 0.1%
23520000 1
< 0.1%
22727273 1
< 0.1%
21232270 1
< 0.1%

내용
Text

Distinct6046
Distinct (%)60.5%
Missing9
Missing (%)0.1%
Memory size156.2 KiB
2024-05-11T02:30:27.407250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length93
Median length71
Mean length14.398959
Min length2

Characters and Unicode

Total characters143860
Distinct characters736
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

Unique5769 ?
Unique (%)57.7%

Sample

1st row11/4 지하창고 임대료 수입(4738)
2nd row관리비 연체료 수납
3rd row일자리안정자금(11월경비원)
4th row관리비 연체료 수납
5th row173(1005-1303) 음식물카드 재구입비 입금
ValueCountFrequency (%)
관리비 3284
 
12.7%
수납 3140
 
12.2%
연체료 3139
 
12.2%
12월분 335
 
1.3%
11월분 290
 
1.1%
285
 
1.1%
승강기 267
 
1.0%
12월 238
 
0.9%
승강기사용료 231
 
0.9%
입금 229
 
0.9%
Other values (7552) 14397
55.7%
2024-05-11T02:30:29.048890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15958
 
11.1%
1 7333
 
5.1%
료 5018
 
3.5%
수 4840
 
3.4%
리 4785
 
3.3%
0 4665
 
3.2%
2 4493
 
3.1%
비 4323
 
3.0%
관 3714
 
2.6%
체 3357
 
2.3%
Other values (726) 85374
59.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 91440
63.6%
Decimal Number 23125
 
16.1%
Space Separator 15958
 
11.1%
Close Punctuation 3348
 
2.3%
Open Punctuation 3344
 
2.3%
Other Punctuation 2956
 
2.1%
Dash Punctuation 2450
 
1.7%
Uppercase Letter 651
 
0.5%
Math Symbol 332
 
0.2%
Lowercase Letter 147
 
0.1%
Other values (4) 109
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
료 5018
 
5.5%
수 4840
 
5.3%
리 4785
 
5.2%
비 4323
 
4.7%
관 3714
 
4.1%
체 3357
 
3.7%
연 3207
 
3.5%
납 3195
 
3.5%
자 2340
 
2.6%
금 2232
 
2.4%
Other values (636) 54429
59.5%
Uppercase Letter
ValueCountFrequency (%)
N 75
 
11.5%
C 56
 
8.6%
K 47
 
7.2%
B 45
 
6.9%
O 41
 
6.3%
L 41
 
6.3%
T 40
 
6.1%
A 37
 
5.7%
F 34
 
5.2%
D 33
 
5.1%
Other values (14) 202
31.0%
Lowercase Letter
ValueCountFrequency (%)
o 36
24.5%
s 17
11.6%
k 12
 
8.2%
e 12
 
8.2%
t 11
 
7.5%
n 10
 
6.8%
x 9
 
6.1%
c 7
 
4.8%
a 6
 
4.1%
b 4
 
2.7%
Other values (13) 23
15.6%
Other Punctuation
ValueCountFrequency (%)
. 840
28.4%
/ 805
27.2%
, 661
22.4%
? 205
 
6.9%
: 191
 
6.5%
* 151
 
5.1%
@ 53
 
1.8%
% 26
 
0.9%
# 9
 
0.3%
' 7
 
0.2%
Other values (3) 8
 
0.3%
Decimal Number
ValueCountFrequency (%)
1 7333
31.7%
0 4665
20.2%
2 4493
19.4%
3 1507
 
6.5%
4 1246
 
5.4%
5 1089
 
4.7%
6 791
 
3.4%
7 747
 
3.2%
8 641
 
2.8%
9 613
 
2.7%
Math Symbol
ValueCountFrequency (%)
~ 283
85.2%
> 16
 
4.8%
+ 12
 
3.6%
× 10
 
3.0%
< 6
 
1.8%
= 4
 
1.2%
÷ 1
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 3262
97.4%
] 85
 
2.5%
} 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 3257
97.4%
[ 86
 
2.6%
{ 1
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
㎡ 3
75.0%
☞ 1
 
25.0%
Space Separator
ValueCountFrequency (%)
15958
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2450
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 103
100.0%
Letter Number
ValueCountFrequency (%)
ⅹ 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 91430
63.6%
Common 51621
35.9%
Latin 799
 
0.6%
Han 10
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
료 5018
 
5.5%
수 4840
 
5.3%
리 4785
 
5.2%
비 4323
 
4.7%
관 3714
 
4.1%
체 3357
 
3.7%
연 3207
 
3.5%
납 3195
 
3.5%
자 2340
 
2.6%
금 2232
 
2.4%
Other values (629) 54419
59.5%
Latin
ValueCountFrequency (%)
N 75
 
9.4%
C 56
 
7.0%
K 47
 
5.9%
B 45
 
5.6%
O 41
 
5.1%
L 41
 
5.1%
T 40
 
5.0%
A 37
 
4.6%
o 36
 
4.5%
F 34
 
4.3%
Other values (38) 347
43.4%
Common
ValueCountFrequency (%)
15958
30.9%
1 7333
14.2%
0 4665
 
9.0%
2 4493
 
8.7%
) 3262
 
6.3%
( 3257
 
6.3%
- 2450
 
4.7%
3 1507
 
2.9%
4 1246
 
2.4%
5 1089
 
2.1%
Other values (32) 6361
 
12.3%
Han
ValueCountFrequency (%)
入 4
40.0%
氏 1
 
10.0%
月 1
 
10.0%
大 1
 
10.0%
分 1
 
10.0%
出 1
 
10.0%
不 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 91429
63.6%
ASCII 52404
36.4%
None 11
 
< 0.1%
CJK 9
 
< 0.1%
CJK Compat 3
 
< 0.1%
Misc Symbols 1
 
< 0.1%
Number Forms 1
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15958
30.5%
1 7333
14.0%
0 4665
 
8.9%
2 4493
 
8.6%
) 3262
 
6.2%
( 3257
 
6.2%
- 2450
 
4.7%
3 1507
 
2.9%
4 1246
 
2.4%
5 1089
 
2.1%
Other values (75) 7144
13.6%
Hangul
ValueCountFrequency (%)
료 5018
 
5.5%
수 4840
 
5.3%
리 4785
 
5.2%
비 4323
 
4.7%
관 3714
 
4.1%
체 3357
 
3.7%
연 3207
 
3.5%
납 3195
 
3.5%
자 2340
 
2.6%
금 2232
 
2.4%
Other values (628) 54418
59.5%
None
ValueCountFrequency (%)
× 10
90.9%
÷ 1
 
9.1%
CJK
ValueCountFrequency (%)
入 4
44.4%
氏 1
 
11.1%
月 1
 
11.1%
大 1
 
11.1%
分 1
 
11.1%
出 1
 
11.1%
CJK Compat
ValueCountFrequency (%)
㎡ 3
100.0%
Misc Symbols
ValueCountFrequency (%)
☞ 1
100.0%
Number Forms
ValueCountFrequency (%)
ⅹ 1
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
不 1
100.0%
Compat Jamo
ValueCountFrequency (%)
ㄹ 1
100.0%

Interactions

2024-05-11T02:30:17.978521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:30:17.252745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:30:18.348529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:30:17.576228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T02:30:29.491782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비용명년월일금액
비용명1.0000.5340.113
년월일0.5341.0000.048
금액0.1130.0481.000
2024-05-11T02:30:29.745494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월일금액비용명
년월일1.0000.0140.231
금액0.0141.0000.048
비용명0.2310.0481.000

Missing values

2024-05-11T02:30:18.827024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T02:30:19.254405image/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
62966우장산에스케이뷰A15701002임대료수익202112147000011/4 지하창고 임대료 수입(4738)
26880둔촌신동아A13406204연체료수익202112271540관리비 연체료 수납
43630하계2차현대아파트A13923106기타운영수익20211215700000일자리안정자금(11월경비원)
35975반포미도아파트A13704404연체료수익20211224230관리비 연체료 수납
51493광장현대파크빌A14381516기타운영수익202112141650173(1005-1303) 음식물카드 재구입비 입금
59055구로현대A15288004이자수익2021121846084신한은행예금이자발생
33935래미안길음1차A13611103이자수익20211211890142021.12.국민은행(관리비)
46683중계주공2단지A13985909연체료수익202112121110관리비 연체료 수납
1580송학휴스테이아파트A10024822기타운영수익20211215800001-304호 창고사용료
38088양재우성A13789203연체료수익202112029400관리비 연체료 수납
아파트명아파트코드비용명년월일금액내용
9717래미안신당하이베르A10078901연체료수익2021120613300관리비 연체료 수납
55485관악파크푸르지오A15105008주차장수익2021120950000미&찬 25로0307 주차료(2021.12.07 ~ 2022.01.06)
55915남현동한일유앤아이A15108001잡수익2021122471490한전검침수당
55215신림현대A15101508주차장수익20211207165313외부주차비, 카드
58153고척한효A15282403연체료수익2021121523220관리비 연체료 수납
24877래미안옥수리버젠제2임대A13375908부과차익20211231500관리비부과차익
38578신반포한신2차A13790929광고료수익20211220200000게시판광고NO.44084_조스영어
36488서초이오빌A13770611연체료수익202112081380관리비 연체료 수납
35530돈암현대A13681304공동주택지원금수익20211217150000일자리 지원금 11월분 입금(미화원 5명 * 3만원)
9574신당약수하이츠A10045404이자수익202112115340국민은행 예금이자(관리비계좌)