Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells12
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 = 43.30597175)Skewed

Reproduction

Analysis started2024-05-11 02:33:55.161009
Analysis finished2024-05-11 02:34:00.700121
Duration5.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

Distinct2156
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:34:01.180501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length7.2976
Min length2

Characters and Unicode

Total characters72976
Distinct characters437
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique226 ?
Unique (%)2.3%

Sample

1st row신정양천
2nd row공릉화랑타운
3rd row장안현대
4th row홍제한양
5th row남서울건영2차
ValueCountFrequency (%)
아파트 176
 
1.6%
래미안 41
 
0.4%
아이파크 29
 
0.3%
고덕 24
 
0.2%
목동7단지 24
 
0.2%
홍제한양 19
 
0.2%
헬리오시티아파트 18
 
0.2%
센트라스 18
 
0.2%
힐스테이트 18
 
0.2%
북한산 18
 
0.2%
Other values (2223) 10334
96.4%
2024-05-11T02:34:02.551205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 2505
 
3.4%
파 2426
 
3.3%
트 2301
 
3.2%
지 2006
 
2.7%
대 1726
 
2.4%
동 1652
 
2.3%
단 1588
 
2.2%
차 1413
 
1.9%
신 1412
 
1.9%
이 1384
 
1.9%
Other values (427) 54563
74.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66550
91.2%
Decimal Number 3832
 
5.3%
Uppercase Letter 846
 
1.2%
Space Separator 805
 
1.1%
Lowercase Letter 312
 
0.4%
Open Punctuation 165
 
0.2%
Close Punctuation 165
 
0.2%
Other Punctuation 162
 
0.2%
Dash Punctuation 128
 
0.2%
Letter Number 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 2505
 
3.8%
파 2426
 
3.6%
트 2301
 
3.5%
지 2006
 
3.0%
대 1726
 
2.6%
동 1652
 
2.5%
단 1588
 
2.4%
차 1413
 
2.1%
신 1412
 
2.1%
이 1384
 
2.1%
Other values (381) 48137
72.3%
Uppercase Letter
ValueCountFrequency (%)
S 158
18.7%
K 115
13.6%
C 105
12.4%
M 78
9.2%
D 78
9.2%
H 57
 
6.7%
L 53
 
6.3%
I 38
 
4.5%
A 31
 
3.7%
E 30
 
3.5%
Other values (7) 103
12.2%
Lowercase Letter
ValueCountFrequency (%)
e 175
56.1%
l 26
 
8.3%
i 26
 
8.3%
k 16
 
5.1%
s 15
 
4.8%
c 14
 
4.5%
v 14
 
4.5%
g 7
 
2.2%
a 7
 
2.2%
h 6
 
1.9%
Decimal Number
ValueCountFrequency (%)
1 1185
30.9%
2 1063
27.7%
3 501
13.1%
4 263
 
6.9%
5 216
 
5.6%
6 196
 
5.1%
7 144
 
3.8%
9 98
 
2.6%
8 94
 
2.5%
0 72
 
1.9%
Other Punctuation
ValueCountFrequency (%)
, 136
84.0%
. 26
 
16.0%
Space Separator
ValueCountFrequency (%)
805
100.0%
Open Punctuation
ValueCountFrequency (%)
( 165
100.0%
Close Punctuation
ValueCountFrequency (%)
) 165
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 128
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 7
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66550
91.2%
Common 5261
 
7.2%
Latin 1165
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 2505
 
3.8%
파 2426
 
3.6%
트 2301
 
3.5%
지 2006
 
3.0%
대 1726
 
2.6%
동 1652
 
2.5%
단 1588
 
2.4%
차 1413
 
2.1%
신 1412
 
2.1%
이 1384
 
2.1%
Other values (381) 48137
72.3%
Latin
ValueCountFrequency (%)
e 175
15.0%
S 158
13.6%
K 115
9.9%
C 105
 
9.0%
M 78
 
6.7%
D 78
 
6.7%
H 57
 
4.9%
L 53
 
4.5%
I 38
 
3.3%
A 31
 
2.7%
Other values (19) 277
23.8%
Common
ValueCountFrequency (%)
1 1185
22.5%
2 1063
20.2%
805
15.3%
3 501
9.5%
4 263
 
5.0%
5 216
 
4.1%
6 196
 
3.7%
( 165
 
3.1%
) 165
 
3.1%
7 144
 
2.7%
Other values (7) 558
10.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66550
91.2%
ASCII 6419
 
8.8%
Number Forms 7
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 2505
 
3.8%
파 2426
 
3.6%
트 2301
 
3.5%
지 2006
 
3.0%
대 1726
 
2.6%
동 1652
 
2.5%
단 1588
 
2.4%
차 1413
 
2.1%
신 1412
 
2.1%
이 1384
 
2.1%
Other values (381) 48137
72.3%
ASCII
ValueCountFrequency (%)
1 1185
18.5%
2 1063
16.6%
805
12.5%
3 501
 
7.8%
4 263
 
4.1%
5 216
 
3.4%
6 196
 
3.1%
e 175
 
2.7%
( 165
 
2.6%
) 165
 
2.6%
Other values (35) 1685
26.3%
Number Forms
ValueCountFrequency (%)
Ⅰ 7
100.0%

아파트코드
Text

Distinct2163
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:34:03.483605image/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

Unique226 ?
Unique (%)2.3%

Sample

1st rowA15807707
2nd rowA13980010
3rd rowA13010003
4th rowA12085303
5th rowA15384603
ValueCountFrequency (%)
a15805115 24
 
0.2%
a12085303 19
 
0.2%
a10027289 18
 
0.2%
a13982704 18
 
0.2%
a10025850 18
 
0.2%
a12175203 18
 
0.2%
a11081503 16
 
0.2%
a13380803 16
 
0.2%
a13380302 16
 
0.2%
a10086801 15
 
0.1%
Other values (2153) 9822
98.2%
2024-05-11T02:34:04.854621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18686
20.8%
1 17218
19.1%
A 9990
11.1%
3 8765
9.7%
2 8258
9.2%
5 6427
 
7.1%
8 5726
 
6.4%
7 4772
 
5.3%
4 3805
 
4.2%
6 3386
 
3.8%
Other values (2) 2967
 
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 18686
23.4%
1 17218
21.5%
3 8765
11.0%
2 8258
10.3%
5 6427
 
8.0%
8 5726
 
7.2%
7 4772
 
6.0%
4 3805
 
4.8%
6 3386
 
4.2%
9 2957
 
3.7%
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 18686
23.4%
1 17218
21.5%
3 8765
11.0%
2 8258
10.3%
5 6427
 
8.0%
8 5726
 
7.2%
7 4772
 
6.0%
4 3805
 
4.8%
6 3386
 
4.2%
9 2957
 
3.7%
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 18686
20.8%
1 17218
19.1%
A 9990
11.1%
3 8765
9.7%
2 8258
9.2%
5 6427
 
7.1%
8 5726
 
6.4%
7 4772
 
5.3%
4 3805
 
4.2%
6 3386
 
3.8%
Other values (2) 2967
 
3.3%

비용명
Categorical

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
연체료수익
3486 
승강기수익
1160 
잡수익
975 
광고료수익
901 
주차장수익
878 
Other values (10)
2600 

Length

Max length9
Median length5
Mean length5.0453
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row연체료수익
2nd row기타운영수익
3rd row연체료수익
4th row알뜰시장수익
5th row고용안정사업수익

Common Values

ValueCountFrequency (%)
연체료수익 3486
34.9%
승강기수익 1160
 
11.6%
잡수익 975
 
9.8%
광고료수익 901
 
9.0%
주차장수익 878
 
8.8%
기타운영수익 665
 
6.7%
고용안정사업수익 540
 
5.4%
검침수익 288
 
2.9%
알뜰시장수익 251
 
2.5%
임대료수익 236
 
2.4%
Other values (5) 620
 
6.2%

Length

2024-05-11T02:34:05.479450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
연체료수익 3486
34.9%
승강기수익 1160
 
11.6%
잡수익 975
 
9.8%
광고료수익 901
 
9.0%
주차장수익 878
 
8.8%
기타운영수익 665
 
6.7%
고용안정사업수익 540
 
5.4%
검침수익 288
 
2.9%
알뜰시장수익 251
 
2.5%
임대료수익 236
 
2.4%
Other values (5) 620
 
6.2%

년월일
Real number (ℝ)

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20201117
Minimum20201101
Maximum20201130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:34:05.905425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20201101
5-th percentile20201102
Q120201109
median20201117
Q320201125
95-th percentile20201130
Maximum20201130
Range29
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.4415268
Coefficient of variation (CV)4.6737648 × 10-7
Kurtosis-1.3186354
Mean20201117
Median Absolute Deviation (MAD)8
Skewness-0.11426196
Sum2.0201117 × 1011
Variance89.142428
MonotonicityNot monotonic
2024-05-11T02:34:06.395842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20201130 1115
 
11.2%
20201102 656
 
6.6%
20201125 519
 
5.2%
20201113 497
 
5.0%
20201110 486
 
4.9%
20201127 476
 
4.8%
20201116 443
 
4.4%
20201103 422
 
4.2%
20201126 399
 
4.0%
20201117 397
 
4.0%
Other values (20) 4590
45.9%
ValueCountFrequency (%)
20201101 194
 
1.9%
20201102 656
6.6%
20201103 422
4.2%
20201104 316
3.2%
20201105 374
3.7%
20201106 320
3.2%
20201107 72
 
0.7%
20201108 45
 
0.4%
20201109 292
2.9%
20201110 486
4.9%
ValueCountFrequency (%)
20201130 1115
11.2%
20201129 168
 
1.7%
20201128 178
 
1.8%
20201127 476
4.8%
20201126 399
 
4.0%
20201125 519
5.2%
20201124 379
 
3.8%
20201123 396
 
4.0%
20201122 72
 
0.7%
20201121 93
 
0.9%

금액
Real number (ℝ)

SKEWED 

Distinct3210
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean290801.14
Minimum-5350000
Maximum1.5163396 × 108
Zeros20
Zeros (%)0.2%
Negative38
Negative (%)0.4%
Memory size166.0 KiB
2024-05-11T02:34:06.950523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-5350000
5-th percentile190
Q13074.5
median30000
Q3100000
95-th percentile1000000
Maximum1.5163396 × 108
Range1.5698396 × 108
Interquartile range (IQR)96925.5

Descriptive statistics

Standard deviation2633680
Coefficient of variation (CV)9.0566358
Kurtosis2243.1863
Mean290801.14
Median Absolute Deviation (MAD)29340
Skewness43.305972
Sum2.9080114 × 109
Variance6.9362704 × 1012
MonotonicityNot monotonic
2024-05-11T02:34:07.494776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30000 554
 
5.5%
50000 543
 
5.4%
100000 530
 
5.3%
60000 161
 
1.6%
70000 161
 
1.6%
90000 153
 
1.5%
150000 151
 
1.5%
40000 149
 
1.5%
80000 137
 
1.4%
200000 132
 
1.3%
Other values (3200) 7329
73.3%
ValueCountFrequency (%)
-5350000 1
< 0.1%
-4140820 1
< 0.1%
-922910 1
< 0.1%
-320000 1
< 0.1%
-250000 1
< 0.1%
-234780 1
< 0.1%
-200000 1
< 0.1%
-180000 1
< 0.1%
-150000 1
< 0.1%
-140000 1
< 0.1%
ValueCountFrequency (%)
151633960 1
< 0.1%
144238300 1
< 0.1%
102605050 1
< 0.1%
57110317 1
< 0.1%
40000000 1
< 0.1%
38500000 1
< 0.1%
26941280 1
< 0.1%
18086924 1
< 0.1%
17500000 1
< 0.1%
17392070 1
< 0.1%

내용
Text

Distinct5790
Distinct (%)58.0%
Missing12
Missing (%)0.1%
Memory size156.2 KiB
2024-05-11T02:34:08.224650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length74
Median length69
Mean length14.073388
Min length2

Characters and Unicode

Total characters140565
Distinct characters721
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

Unique5549 ?
Unique (%)55.6%

Sample

1st row관리비 연체료 수납
2nd row702-901 자동문키 2개 발급
3rd row관리비 연체료 수납
4th row일일장수입(민속과자)
5th row일자리안정자금지원금
ValueCountFrequency (%)
관리비 3608
 
13.9%
연체료 3497
 
13.4%
수납 3495
 
13.4%
10월분 364
 
1.4%
11월분 355
 
1.4%
승강기 318
 
1.2%
262
 
1.0%
승강기사용료 245
 
0.9%
11월 241
 
0.9%
입금 237
 
0.9%
Other values (7229) 13384
51.5%
2024-05-11T02:34:09.766868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16119
 
11.5%
1 7733
 
5.5%
료 5604
 
4.0%
0 5599
 
4.0%
리 4935
 
3.5%
수 4925
 
3.5%
비 4397
 
3.1%
관 3858
 
2.7%
체 3689
 
2.6%
납 3566
 
2.5%
Other values (711) 80140
57.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89475
63.7%
Decimal Number 22715
 
16.2%
Space Separator 16119
 
11.5%
Close Punctuation 3092
 
2.2%
Open Punctuation 3090
 
2.2%
Other Punctuation 2573
 
1.8%
Dash Punctuation 2316
 
1.6%
Uppercase Letter 675
 
0.5%
Math Symbol 289
 
0.2%
Lowercase Letter 151
 
0.1%
Other values (4) 70
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
료 5604
 
6.3%
리 4935
 
5.5%
수 4925
 
5.5%
비 4397
 
4.9%
관 3858
 
4.3%
체 3689
 
4.1%
납 3566
 
4.0%
연 3556
 
4.0%
월 1856
 
2.1%
사 1724
 
1.9%
Other values (625) 51365
57.4%
Uppercase Letter
ValueCountFrequency (%)
N 71
 
10.5%
K 58
 
8.6%
C 58
 
8.6%
T 56
 
8.3%
L 47
 
7.0%
O 45
 
6.7%
D 36
 
5.3%
S 36
 
5.3%
B 36
 
5.3%
A 35
 
5.2%
Other values (14) 197
29.2%
Lowercase Letter
ValueCountFrequency (%)
o 38
25.2%
k 13
 
8.6%
s 12
 
7.9%
n 11
 
7.3%
x 11
 
7.3%
e 10
 
6.6%
t 8
 
5.3%
l 7
 
4.6%
h 6
 
4.0%
i 5
 
3.3%
Other values (10) 30
19.9%
Other Punctuation
ValueCountFrequency (%)
/ 767
29.8%
, 667
25.9%
. 664
25.8%
: 178
 
6.9%
* 152
 
5.9%
? 57
 
2.2%
@ 32
 
1.2%
% 20
 
0.8%
# 13
 
0.5%
' 9
 
0.3%
Other values (4) 14
 
0.5%
Decimal Number
ValueCountFrequency (%)
1 7733
34.0%
0 5599
24.6%
2 2906
 
12.8%
3 1460
 
6.4%
4 1167
 
5.1%
5 939
 
4.1%
6 833
 
3.7%
9 734
 
3.2%
8 675
 
3.0%
7 669
 
2.9%
Math Symbol
ValueCountFrequency (%)
~ 222
76.8%
+ 23
 
8.0%
> 14
 
4.8%
× 13
 
4.5%
= 10
 
3.5%
< 7
 
2.4%
Close Punctuation
ValueCountFrequency (%)
) 3002
97.1%
] 90
 
2.9%
Open Punctuation
ValueCountFrequency (%)
( 3001
97.1%
[ 89
 
2.9%
Other Number
ValueCountFrequency (%)
② 1
50.0%
① 1
50.0%
Other Symbol
ValueCountFrequency (%)
▶ 1
50.0%
㎡ 1
50.0%
Space Separator
ValueCountFrequency (%)
16119
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2316
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 65
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89470
63.7%
Common 50264
35.8%
Latin 826
 
0.6%
Han 5
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
료 5604
 
6.3%
리 4935
 
5.5%
수 4925
 
5.5%
비 4397
 
4.9%
관 3858
 
4.3%
체 3689
 
4.1%
납 3566
 
4.0%
연 3556
 
4.0%
월 1856
 
2.1%
사 1724
 
1.9%
Other values (622) 51360
57.4%
Latin
ValueCountFrequency (%)
N 71
 
8.6%
K 58
 
7.0%
C 58
 
7.0%
T 56
 
6.8%
L 47
 
5.7%
O 45
 
5.4%
o 38
 
4.6%
D 36
 
4.4%
S 36
 
4.4%
B 36
 
4.4%
Other values (34) 345
41.8%
Common
ValueCountFrequency (%)
16119
32.1%
1 7733
15.4%
0 5599
 
11.1%
) 3002
 
6.0%
( 3001
 
6.0%
2 2906
 
5.8%
- 2316
 
4.6%
3 1460
 
2.9%
4 1167
 
2.3%
5 939
 
1.9%
Other values (32) 6022
 
12.0%
Han
ValueCountFrequency (%)
入 2
40.0%
大 2
40.0%
不 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89469
63.6%
ASCII 51072
36.3%
None 14
 
< 0.1%
CJK 4
 
< 0.1%
Enclosed Alphanum 2
 
< 0.1%
Geometric Shapes 1
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%
CJK Compat 1
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16119
31.6%
1 7733
15.1%
0 5599
 
11.0%
) 3002
 
5.9%
( 3001
 
5.9%
2 2906
 
5.7%
- 2316
 
4.5%
3 1460
 
2.9%
4 1167
 
2.3%
5 939
 
1.8%
Other values (70) 6830
13.4%
Hangul
ValueCountFrequency (%)
료 5604
 
6.3%
리 4935
 
5.5%
수 4925
 
5.5%
비 4397
 
4.9%
관 3858
 
4.3%
체 3689
 
4.1%
납 3566
 
4.0%
연 3556
 
4.0%
월 1856
 
2.1%
사 1724
 
1.9%
Other values (621) 51359
57.4%
None
ValueCountFrequency (%)
× 13
92.9%
· 1
 
7.1%
CJK
ValueCountFrequency (%)
入 2
50.0%
大 2
50.0%
Enclosed Alphanum
ValueCountFrequency (%)
② 1
50.0%
① 1
50.0%
Geometric Shapes
ValueCountFrequency (%)
▶ 1
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
不 1
100.0%
CJK Compat
ValueCountFrequency (%)
㎡ 1
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅡ 1
100.0%

Interactions

2024-05-11T02:33:58.785363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:33:57.800500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:33:59.161213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:33:58.227270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T02:34:10.102092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비용명년월일금액
비용명1.0000.4230.122
년월일0.4231.0000.000
금액0.1220.0001.000
2024-05-11T02:34:10.353519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월일금액비용명
년월일1.0000.0280.171
금액0.0281.0000.057
비용명0.1710.0571.000

Missing values

2024-05-11T02:33:59.689079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T02:34:00.232854image/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
60742신정양천A15807707연체료수익20201124180관리비 연체료 수납
38434공릉화랑타운A13980010기타운영수익202011096600702-901 자동문키 2개 발급
14402장안현대A13010003연체료수익2020113040관리비 연체료 수납
9452홍제한양A12085303알뜰시장수익2020111627273일일장수입(민속과자)
53491남서울건영2차A15384603고용안정사업수익20201117960000일자리안정자금지원금
11043상암휴먼시아2단지아파트A12179501잡수익2020111876520한전검침 11월분 입금
16994신내12단지A13186306연체료수익202011015550관리비 연체료 수납
54096힐스테이트상도프레스티지A15603008임대료수익20201125185000011월분 어린이집 임대료
61664신정푸른마을2단지A15886508잡수익2020111025010월분 산재보험료 자동이체 청구할인
30106종암2차SK뷰A13671213주차장수익20201130124067011월분 초과차량주차비
아파트명아파트코드비용명년월일금액내용
33606풍납시티극동A13804003고용안정사업수익20201113270000관리소 일자리 안정자금(10월분-3명)
22723천호삼성아파트A13402305연체료수익20201110390관리비 연체료 수납
13561답십리동서울한양A13003002연체료수익2020110960관리비 연체료 수납
58414강서월드메르디앙A15782501주차장수익20201129560000주차수입(동명광택)
57919가양강나루현대A15780401승강기수익20201111100000104-1401 전출시 승강기사용료
3758위례포레샤인아파트A10026682검침수익20201125900650한전검침대행비
35018잠실엘스아파트A13822004승강기수익20201128150000이사 승강기사용료입금(101-2302)전입
24251강동롯데캐슬퍼스트아파트A13485302재활용품수익20201110903280재활용수거 수익
58118가양3단지(강변)A15780704연체료수익20201103110관리비 연체료 수납
4584경희궁자이3단지A10027105연체료수익20201126950관리비 연체료 수납