Overview

Dataset statistics

Number of variables5
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory488.3 KiB
Average record size in memory50.0 B

Variable types

Text3
Categorical1
Numeric1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15821/S/1/datasetView.do

Alerts

년월일 has constant value ""Constant
금액 has 1806 (18.1%) zerosZeros

Reproduction

Analysis started2024-05-11 06:51:27.618347
Analysis finished2024-05-11 06:51:29.462114
Duration1.84 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

Distinct2120
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T06:51:29.806280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length21
Mean length7.3631
Min length2

Characters and Unicode

Total characters73631
Distinct characters428
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

Unique108 ?
Unique (%)1.1%

Sample

1st row공릉2단지라이프
2nd row서울숲행당푸르지오
3rd row신길우성3차아파트
4th row래미안강동팰리스
5th row구의강변우성
ValueCountFrequency (%)
아파트 209
 
1.9%
래미안 52
 
0.5%
e편한세상 33
 
0.3%
고덕 27
 
0.2%
아이파크 22
 
0.2%
코오롱하늘채아파트 18
 
0.2%
푸르지오 18
 
0.2%
북한산 17
 
0.2%
sk뷰 17
 
0.2%
잠원신화 15
 
0.1%
Other values (2201) 10514
96.1%
2024-05-11T06:51:30.692814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 2686
 
3.6%
파 2684
 
3.6%
트 2536
 
3.4%
대 1698
 
2.3%
지 1686
 
2.3%
동 1543
 
2.1%
이 1446
 
2.0%
차 1380
 
1.9%
신 1307
 
1.8%
성 1292
 
1.8%
Other values (418) 55373
75.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67527
91.7%
Decimal Number 3364
 
4.6%
Space Separator 1020
 
1.4%
Uppercase Letter 837
 
1.1%
Lowercase Letter 332
 
0.5%
Close Punctuation 148
 
0.2%
Open Punctuation 148
 
0.2%
Dash Punctuation 143
 
0.2%
Other Punctuation 110
 
0.1%
Letter Number 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 2686
 
4.0%
파 2684
 
4.0%
트 2536
 
3.8%
대 1698
 
2.5%
지 1686
 
2.5%
동 1543
 
2.3%
이 1446
 
2.1%
차 1380
 
2.0%
신 1307
 
1.9%
성 1292
 
1.9%
Other values (373) 49269
73.0%
Uppercase Letter
ValueCountFrequency (%)
S 138
16.5%
C 119
14.2%
M 86
10.3%
D 86
10.3%
K 83
9.9%
L 68
8.1%
H 55
 
6.6%
I 45
 
5.4%
E 37
 
4.4%
G 30
 
3.6%
Other values (7) 90
10.8%
Lowercase Letter
ValueCountFrequency (%)
e 193
58.1%
l 36
 
10.8%
i 26
 
7.8%
v 19
 
5.7%
k 18
 
5.4%
s 17
 
5.1%
c 8
 
2.4%
w 4
 
1.2%
a 4
 
1.2%
g 4
 
1.2%
Decimal Number
ValueCountFrequency (%)
1 1015
30.2%
2 955
28.4%
3 455
13.5%
4 234
 
7.0%
5 198
 
5.9%
6 151
 
4.5%
7 112
 
3.3%
8 106
 
3.2%
9 80
 
2.4%
0 58
 
1.7%
Other Punctuation
ValueCountFrequency (%)
, 92
83.6%
. 18
 
16.4%
Space Separator
ValueCountFrequency (%)
1020
100.0%
Close Punctuation
ValueCountFrequency (%)
) 148
100.0%
Open Punctuation
ValueCountFrequency (%)
( 148
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 143
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67527
91.7%
Common 4933
 
6.7%
Latin 1171
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 2686
 
4.0%
파 2684
 
4.0%
트 2536
 
3.8%
대 1698
 
2.5%
지 1686
 
2.5%
동 1543
 
2.3%
이 1446
 
2.1%
차 1380
 
2.0%
신 1307
 
1.9%
성 1292
 
1.9%
Other values (373) 49269
73.0%
Latin
ValueCountFrequency (%)
e 193
16.5%
S 138
11.8%
C 119
10.2%
M 86
 
7.3%
D 86
 
7.3%
K 83
 
7.1%
L 68
 
5.8%
H 55
 
4.7%
I 45
 
3.8%
E 37
 
3.2%
Other values (19) 261
22.3%
Common
ValueCountFrequency (%)
1020
20.7%
1 1015
20.6%
2 955
19.4%
3 455
9.2%
4 234
 
4.7%
5 198
 
4.0%
6 151
 
3.1%
) 148
 
3.0%
( 148
 
3.0%
- 143
 
2.9%
Other values (6) 466
9.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67527
91.7%
ASCII 6102
 
8.3%
Number Forms 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 2686
 
4.0%
파 2684
 
4.0%
트 2536
 
3.8%
대 1698
 
2.5%
지 1686
 
2.5%
동 1543
 
2.3%
이 1446
 
2.1%
차 1380
 
2.0%
신 1307
 
1.9%
성 1292
 
1.9%
Other values (373) 49269
73.0%
ASCII
ValueCountFrequency (%)
1020
16.7%
1 1015
16.6%
2 955
15.7%
3 455
 
7.5%
4 234
 
3.8%
5 198
 
3.2%
e 193
 
3.2%
6 151
 
2.5%
) 148
 
2.4%
( 148
 
2.4%
Other values (34) 1585
26.0%
Number Forms
ValueCountFrequency (%)
Ⅰ 2
100.0%

아파트코드
Text

Distinct2124
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T06:51:31.414276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters90000
Distinct characters11
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

Unique108 ?
Unique (%)1.1%

Sample

1st rowA13980510
2nd rowA13307002
3rd rowA15086004
4th rowA10026852
5th rowA14320302
ValueCountFrequency (%)
a13204408 15
 
0.1%
a13790703 15
 
0.1%
a10025263 15
 
0.1%
a13590204 13
 
0.1%
a13519006 13
 
0.1%
a13007001 13
 
0.1%
a12179004 12
 
0.1%
a15703001 12
 
0.1%
a15701007 12
 
0.1%
a10025533 12
 
0.1%
Other values (2114) 9868
98.7%
2024-05-11T06:51:32.608803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18995
21.1%
1 17463
19.4%
A 10000
11.1%
3 9003
10.0%
2 8690
9.7%
5 5738
 
6.4%
8 5200
 
5.8%
7 4572
 
5.1%
4 3984
 
4.4%
6 3432
 
3.8%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18995
23.7%
1 17463
21.8%
3 9003
11.3%
2 8690
10.9%
5 5738
 
7.2%
8 5200
 
6.5%
7 4572
 
5.7%
4 3984
 
5.0%
6 3432
 
4.3%
9 2923
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
A 10000
100.0%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
0 18995
23.7%
1 17463
21.8%
3 9003
11.3%
2 8690
10.9%
5 5738
 
7.2%
8 5200
 
6.5%
7 4572
 
5.7%
4 3984
 
5.0%
6 3432
 
4.3%
9 2923
 
3.7%
Latin
ValueCountFrequency (%)
A 10000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18995
21.1%
1 17463
19.4%
A 10000
11.1%
3 9003
10.0%
2 8690
9.7%
5 5738
 
6.4%
8 5200
 
5.8%
7 4572
 
5.1%
4 3984
 
4.4%
6 3432
 
3.8%

비용명
Text

Distinct87
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T06:51:33.207705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length4.8916
Min length2

Characters and Unicode

Total characters48916
Distinct characters120
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row승강기수익
2nd row음식물처리비
3rd row제수당
4th row위탁관리수수료
5th row소모품비
ValueCountFrequency (%)
도서인쇄비 225
 
2.2%
승강기유지비 224
 
2.2%
이자수익 222
 
2.2%
소독비 220
 
2.2%
세대전기료 215
 
2.1%
보험료 214
 
2.1%
사무용품비 212
 
2.1%
퇴직급여 208
 
2.1%
청소비 204
 
2.0%
경비비 204
 
2.0%
Other values (77) 7852
78.5%
2024-05-11T06:51:34.350381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
비 5330
 
10.9%
수 3632
 
7.4%
료 2139
 
4.4%
익 2052
 
4.2%
용 1684
 
3.4%
기 1322
 
2.7%
대 1055
 
2.2%
리 866
 
1.8%
보 795
 
1.6%
지 777
 
1.6%
Other values (110) 29264
59.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 48916
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
비 5330
 
10.9%
수 3632
 
7.4%
료 2139
 
4.4%
익 2052
 
4.2%
용 1684
 
3.4%
기 1322
 
2.7%
대 1055
 
2.2%
리 866
 
1.8%
보 795
 
1.6%
지 777
 
1.6%
Other values (110) 29264
59.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 48916
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
비 5330
 
10.9%
수 3632
 
7.4%
료 2139
 
4.4%
익 2052
 
4.2%
용 1684
 
3.4%
기 1322
 
2.7%
대 1055
 
2.2%
리 866
 
1.8%
보 795
 
1.6%
지 777
 
1.6%
Other values (110) 29264
59.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 48916
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
비 5330
 
10.9%
수 3632
 
7.4%
료 2139
 
4.4%
익 2052
 
4.2%
용 1684
 
3.4%
기 1322
 
2.7%
대 1055
 
2.2%
리 866
 
1.8%
보 795
 
1.6%
지 777
 
1.6%
Other values (110) 29264
59.8%

년월일
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
202210
10000 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202210
2nd row202210
3rd row202210
4th row202210
5th row202210

Common Values

ValueCountFrequency (%)
202210 10000
100.0%

Length

2024-05-11T06:51:34.824821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:51:35.060911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202210 10000
100.0%

금액
Real number (ℝ)

ZEROS 

Distinct6660
Distinct (%)66.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3057720.6
Minimum-1543073
Maximum4.3433044 × 108
Zeros1806
Zeros (%)18.1%
Negative10
Negative (%)0.1%
Memory size166.0 KiB
2024-05-11T06:51:35.340812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1543073
5-th percentile0
Q130000
median265955
Q31262117.5
95-th percentile14897631
Maximum4.3433044 × 108
Range4.3587351 × 108
Interquartile range (IQR)1232117.5

Descriptive statistics

Standard deviation11158044
Coefficient of variation (CV)3.6491379
Kurtosis325.45824
Mean3057720.6
Median Absolute Deviation (MAD)265955
Skewness13.075976
Sum3.0577206 × 1010
Variance1.2450195 × 1014
MonotonicityNot monotonic
2024-05-11T06:51:36.218034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1806
 
18.1%
200000 85
 
0.9%
300000 63
 
0.6%
100000 51
 
0.5%
150000 44
 
0.4%
400000 37
 
0.4%
250000 31
 
0.3%
30000 30
 
0.3%
60000 29
 
0.3%
120000 24
 
0.2%
Other values (6650) 7800
78.0%
ValueCountFrequency (%)
-1543073 1
< 0.1%
-1309090 1
< 0.1%
-836450 1
< 0.1%
-329660 1
< 0.1%
-292700 1
< 0.1%
-150060 1
< 0.1%
-12780 1
< 0.1%
-5220 1
< 0.1%
-2960 1
< 0.1%
-62 1
< 0.1%
ValueCountFrequency (%)
434330440 1
< 0.1%
282099260 1
< 0.1%
235949360 1
< 0.1%
183942000 1
< 0.1%
172182525 1
< 0.1%
171738210 1
< 0.1%
140727926 1
< 0.1%
138052055 1
< 0.1%
135715690 1
< 0.1%
132321480 1
< 0.1%

Interactions

2024-05-11T06:51:28.525697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T06:51:36.478822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비용명금액
비용명1.0000.307
금액0.3071.000

Missing values

2024-05-11T06:51:28.936730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T06:51:29.334876image/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

아파트명아파트코드비용명년월일금액
66674공릉2단지라이프A13980510승강기수익202210900000
36132서울숲행당푸르지오A13307002음식물처리비202210685490
81510신길우성3차아파트A15086004제수당2022101050000
9633래미안강동팰리스A10026852위탁관리수수료202210878490
75766구의강변우성A14320302소모품비202210214310
37394옥수하이츠제2A13375904복리후생비2022100
30562신내우남푸르미아A13186502기타부대비202210172780
52629정릉1차e-편한세상A13676703복리후생비202210840000
82421대림동현대A15095001복리후생비202210335700
2916아크로 서울포레스트A10024503충당부채전입이자비용2022100
아파트명아파트코드비용명년월일금액
47903압구정한양아파트제2단지A13590204통신비202210212360
97690화곡초록A15770801전산고지비202210259600
19695공덕래미안5차A12170603감가상각비202210100000
62475가락삼익맨숀A13885306소모품비202210406370
91457힐스테이트상도프레스티지A15603008공동주택지원금수익2022100
86962고척대우A15279404소모품비202210146170
91776상도경향렉스빌A15603401승강기수익2022100
5827고덕 그라시움 아파트A10025263공동주택지원금수익2022100
67921상계주공11단지A13982301부과차익2022104819
52639정릉1차e-편한세상A13676703주차장수익2022102407350