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 194 (1.9%) zerosZeros

Reproduction

Analysis started2024-05-11 06:50:04.130102
Analysis finished2024-05-11 06:50:06.117101
Duration1.99 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

Distinct2203
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T06:50:06.378213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length20
Mean length7.3867
Min length2

Characters and Unicode

Total characters73867
Distinct characters437
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

Unique126 ?
Unique (%)1.3%

Sample

1st row신도림대림5차e-편한세상
2nd row도봉동아에코빌
3rd row창동주공2단지
4th row이촌동부센트레빌
5th row남서울힐스테이트
ValueCountFrequency (%)
아파트 195
 
1.8%
래미안 34
 
0.3%
북한산 20
 
0.2%
고덕 20
 
0.2%
아이파크 20
 
0.2%
e편한세상 19
 
0.2%
송파 17
 
0.2%
sk뷰 16
 
0.1%
장미3차 15
 
0.1%
신반포 15
 
0.1%
Other values (2286) 10495
96.6%
2024-05-11T06:50:07.571727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 2617
 
3.5%
파 2548
 
3.4%
트 2346
 
3.2%
지 1886
 
2.6%
대 1788
 
2.4%
동 1697
 
2.3%
단 1522
 
2.1%
차 1490
 
2.0%
이 1463
 
2.0%
신 1461
 
2.0%
Other values (427) 55049
74.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67566
91.5%
Decimal Number 3803
 
5.1%
Space Separator 942
 
1.3%
Uppercase Letter 754
 
1.0%
Lowercase Letter 291
 
0.4%
Close Punctuation 139
 
0.2%
Open Punctuation 139
 
0.2%
Dash Punctuation 120
 
0.2%
Other Punctuation 108
 
0.1%
Letter Number 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 2617
 
3.9%
파 2548
 
3.8%
트 2346
 
3.5%
지 1886
 
2.8%
대 1788
 
2.6%
동 1697
 
2.5%
단 1522
 
2.3%
차 1490
 
2.2%
이 1463
 
2.2%
신 1461
 
2.2%
Other values (382) 48748
72.1%
Uppercase Letter
ValueCountFrequency (%)
C 114
15.1%
S 113
15.0%
K 82
10.9%
D 73
9.7%
M 73
9.7%
L 69
9.2%
H 54
7.2%
I 40
 
5.3%
G 30
 
4.0%
E 26
 
3.4%
Other values (7) 80
10.6%
Lowercase Letter
ValueCountFrequency (%)
e 163
56.0%
l 32
 
11.0%
i 24
 
8.2%
v 19
 
6.5%
s 17
 
5.8%
k 16
 
5.5%
c 6
 
2.1%
w 6
 
2.1%
h 4
 
1.4%
a 2
 
0.7%
Decimal Number
ValueCountFrequency (%)
1 1108
29.1%
2 1099
28.9%
3 507
13.3%
4 303
 
8.0%
5 228
 
6.0%
6 168
 
4.4%
7 139
 
3.7%
9 92
 
2.4%
0 81
 
2.1%
8 78
 
2.1%
Other Punctuation
ValueCountFrequency (%)
, 92
85.2%
. 16
 
14.8%
Space Separator
ValueCountFrequency (%)
942
100.0%
Close Punctuation
ValueCountFrequency (%)
) 139
100.0%
Open Punctuation
ValueCountFrequency (%)
( 139
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 120
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67566
91.5%
Common 5251
 
7.1%
Latin 1050
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 2617
 
3.9%
파 2548
 
3.8%
트 2346
 
3.5%
지 1886
 
2.8%
대 1788
 
2.6%
동 1697
 
2.5%
단 1522
 
2.3%
차 1490
 
2.2%
이 1463
 
2.2%
신 1461
 
2.2%
Other values (382) 48748
72.1%
Latin
ValueCountFrequency (%)
e 163
15.5%
C 114
10.9%
S 113
10.8%
K 82
 
7.8%
D 73
 
7.0%
M 73
 
7.0%
L 69
 
6.6%
H 54
 
5.1%
I 40
 
3.8%
l 32
 
3.0%
Other values (19) 237
22.6%
Common
ValueCountFrequency (%)
1 1108
21.1%
2 1099
20.9%
942
17.9%
3 507
9.7%
4 303
 
5.8%
5 228
 
4.3%
6 168
 
3.2%
7 139
 
2.6%
) 139
 
2.6%
( 139
 
2.6%
Other values (6) 479
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67566
91.5%
ASCII 6296
 
8.5%
Number Forms 5
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 2617
 
3.9%
파 2548
 
3.8%
트 2346
 
3.5%
지 1886
 
2.8%
대 1788
 
2.6%
동 1697
 
2.5%
단 1522
 
2.3%
차 1490
 
2.2%
이 1463
 
2.2%
신 1461
 
2.2%
Other values (382) 48748
72.1%
ASCII
ValueCountFrequency (%)
1 1108
17.6%
2 1099
17.5%
942
15.0%
3 507
 
8.1%
4 303
 
4.8%
5 228
 
3.6%
6 168
 
2.7%
e 163
 
2.6%
7 139
 
2.2%
) 139
 
2.2%
Other values (34) 1500
23.8%
Number Forms
ValueCountFrequency (%)
Ⅰ 5
100.0%

아파트코드
Text

Distinct2210
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T06:50:08.359997image/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

Unique126 ?
Unique (%)1.3%

Sample

1st rowA15288805
2nd rowA13201206
3rd rowA13204508
4th rowA14003004
5th rowA15370103
ValueCountFrequency (%)
a13872504 15
 
0.1%
a15086601 14
 
0.1%
a12127006 13
 
0.1%
a13985605 12
 
0.1%
a13550502 12
 
0.1%
a13204104 12
 
0.1%
a15703204 11
 
0.1%
a15786321 11
 
0.1%
a15101508 11
 
0.1%
a13879102 11
 
0.1%
Other values (2200) 9878
98.8%
2024-05-11T06:50:09.741344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18504
20.6%
1 17511
19.5%
A 9994
11.1%
3 8658
9.6%
2 8334
9.3%
5 6310
 
7.0%
8 5573
 
6.2%
7 4687
 
5.2%
4 3977
 
4.4%
6 3449
 
3.8%
Other values (2) 3003
 
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 18504
23.1%
1 17511
21.9%
3 8658
10.8%
2 8334
10.4%
5 6310
 
7.9%
8 5573
 
7.0%
7 4687
 
5.9%
4 3977
 
5.0%
6 3449
 
4.3%
9 2997
 
3.7%
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 18504
23.1%
1 17511
21.9%
3 8658
10.8%
2 8334
10.4%
5 6310
 
7.9%
8 5573
 
7.0%
7 4687
 
5.9%
4 3977
 
5.0%
6 3449
 
4.3%
9 2997
 
3.7%
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 18504
20.6%
1 17511
19.5%
A 9994
11.1%
3 8658
9.6%
2 8334
9.3%
5 6310
 
7.0%
8 5573
 
6.2%
7 4687
 
5.2%
4 3977
 
4.4%
6 3449
 
3.8%
Other values (2) 3003
 
3.3%

비용명
Text

Distinct87
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T06:50:10.562183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length4.8085
Min length2

Characters and Unicode

Total characters48085
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

Unique4 ?
Unique (%)< 0.1%

Sample

1st row감가상각비
2nd row기타부대비
3rd row청소비
4th row퇴직급여
5th row산재보험료
ValueCountFrequency (%)
퇴직급여 255
 
2.5%
청소비 254
 
2.5%
급여 247
 
2.5%
소독비 245
 
2.5%
세대전기료 242
 
2.4%
수선유지비 242
 
2.4%
사무용품비 242
 
2.4%
연체료수익 242
 
2.4%
통신비 242
 
2.4%
산재보험료 233
 
2.3%
Other values (77) 7556
75.6%
2024-05-11T06:50:11.984946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
비 5267
 
11.0%
수 3597
 
7.5%
료 2316
 
4.8%
익 1875
 
3.9%
용 1706
 
3.5%
기 1335
 
2.8%
대 1170
 
2.4%
보 943
 
2.0%
험 894
 
1.9%
리 857
 
1.8%
Other values (110) 28125
58.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 48085
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
비 5267
 
11.0%
수 3597
 
7.5%
료 2316
 
4.8%
익 1875
 
3.9%
용 1706
 
3.5%
기 1335
 
2.8%
대 1170
 
2.4%
보 943
 
2.0%
험 894
 
1.9%
리 857
 
1.8%
Other values (110) 28125
58.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 48085
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
비 5267
 
11.0%
수 3597
 
7.5%
료 2316
 
4.8%
익 1875
 
3.9%
용 1706
 
3.5%
기 1335
 
2.8%
대 1170
 
2.4%
보 943
 
2.0%
험 894
 
1.9%
리 857
 
1.8%
Other values (110) 28125
58.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 48085
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
비 5267
 
11.0%
수 3597
 
7.5%
료 2316
 
4.8%
익 1875
 
3.9%
용 1706
 
3.5%
기 1335
 
2.8%
대 1170
 
2.4%
보 943
 
2.0%
험 894
 
1.9%
리 857
 
1.8%
Other values (110) 28125
58.5%

년월일
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
202201
10000 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202201
2nd row202201
3rd row202201
4th row202201
5th row202201

Common Values

ValueCountFrequency (%)
202201 10000
100.0%

Length

2024-05-11T06:50:12.520791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:50:12.940356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202201 10000
100.0%

금액
Real number (ℝ)

ZEROS 

Distinct7769
Distinct (%)77.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4426582.4
Minimum-2923446
Maximum6.619606 × 108
Zeros194
Zeros (%)1.9%
Negative3
Negative (%)< 0.1%
Memory size166.0 KiB
2024-05-11T06:50:13.375214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2923446
5-th percentile5000
Q1130410
median400000
Q31776527.5
95-th percentile20037743
Maximum6.619606 × 108
Range6.6488405 × 108
Interquartile range (IQR)1646117.5

Descriptive statistics

Standard deviation18394193
Coefficient of variation (CV)4.1553937
Kurtosis392.06827
Mean4426582.4
Median Absolute Deviation (MAD)346770
Skewness15.466789
Sum4.4265824 × 1010
Variance3.3834632 × 1014
MonotonicityNot monotonic
2024-05-11T06:50:14.525279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 194
 
1.9%
200000 105
 
1.1%
100000 72
 
0.7%
300000 70
 
0.7%
150000 58
 
0.6%
400000 49
 
0.5%
250000 41
 
0.4%
50000 39
 
0.4%
500000 31
 
0.3%
110000 28
 
0.3%
Other values (7759) 9313
93.1%
ValueCountFrequency (%)
-2923446 1
 
< 0.1%
-261800 1
 
< 0.1%
-63 1
 
< 0.1%
0 194
1.9%
2 4
 
< 0.1%
3 3
 
< 0.1%
4 1
 
< 0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
661960600 1
< 0.1%
627197177 1
< 0.1%
439250910 1
< 0.1%
361395538 1
< 0.1%
357260398 1
< 0.1%
270274270 1
< 0.1%
255220298 1
< 0.1%
253187900 1
< 0.1%
249538280 1
< 0.1%
246923970 1
< 0.1%

Interactions

2024-05-11T06:50:05.186938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T06:50:14.956499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비용명금액
비용명1.0000.398
금액0.3981.000

Missing values

2024-05-11T06:50:05.626006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T06:50:05.975807image/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
75628신도림대림5차e-편한세상A15288805감가상각비202201235210
26216도봉동아에코빌A13201206기타부대비202201323050
27922창동주공2단지A13204508청소비2022016072400
60755이촌동부센트레빌A14003004퇴직급여202201822960
76266남서울힐스테이트A15370103산재보험료202201329000
60751이촌동부센트레빌A14003004연차수당202201318540
14024DMC센트레빌A12072801입주자대표회의운영비202201700000
45087돈암현대A13681304기타운영수익202201257400
65500자양우성3차A14386110세대전기료20220122937460
28221방학청구A13276415정화조관리비202201785480
아파트명아파트코드비용명년월일금액
13016홍은풍림2차A12010103승강기유지비202201380000
87621목동6단지A15875103세대난방비202201177656190
69759롯데캐슬아이비A15088915소모품비2022011236350
65634자양7차현대홈타운A14388204소모품비20220153000
750서초그랑자이A10024240세대난방비202201107597360
52374가락프라자A13881204연차수당2022010
89288은평뉴타운마고정3단지A41279912잡비용202201551814
4366래미안솔베뉴A10025415시설보수비20220185400
24534면목삼익A13183502피복비20220140010
56508화랑해링턴플레이스A13980413제수당202201887620