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-15820/S/1/datasetView.do

Alerts

년월일 has constant value ""Constant
금액 has 2241 (22.4%) zerosZeros

Reproduction

Analysis started2024-05-11 05:59:41.828670
Analysis finished2024-05-11 05:59:42.690315
Duration0.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

Distinct2203
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T14:59:42.906463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length7.2641
Min length2

Characters and Unicode

Total characters72641
Distinct characters436
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

Unique120 ?
Unique (%)1.2%

Sample

1st row금천롯데캐슬골드파크1차아파트
2nd row송파현대1차
3rd row삼성한솔
4th row전농우성
5th row상봉우정
ValueCountFrequency (%)
아파트 139
 
1.3%
래미안 25
 
0.2%
북한산 19
 
0.2%
고덕 17
 
0.2%
e편한세상 15
 
0.1%
아이파크 15
 
0.1%
서울숲2차푸르지오임대 14
 
0.1%
신내 14
 
0.1%
면목삼익 13
 
0.1%
도화현대1차아파트 13
 
0.1%
Other values (2272) 10363
97.3%
2024-05-11T14:59:43.456603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
파 2312
 
3.2%
아 2299
 
3.2%
트 2069
 
2.8%
지 1889
 
2.6%
대 1850
 
2.5%
동 1703
 
2.3%
차 1534
 
2.1%
단 1516
 
2.1%
신 1438
 
2.0%
성 1261
 
1.7%
Other values (426) 54770
75.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66344
91.3%
Decimal Number 3847
 
5.3%
Uppercase Letter 797
 
1.1%
Space Separator 718
 
1.0%
Lowercase Letter 337
 
0.5%
Close Punctuation 159
 
0.2%
Open Punctuation 159
 
0.2%
Dash Punctuation 144
 
0.2%
Other Punctuation 123
 
0.2%
Letter Number 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
파 2312
 
3.5%
아 2299
 
3.5%
트 2069
 
3.1%
지 1889
 
2.8%
대 1850
 
2.8%
동 1703
 
2.6%
차 1534
 
2.3%
단 1516
 
2.3%
신 1438
 
2.2%
성 1261
 
1.9%
Other values (381) 48473
73.1%
Uppercase Letter
ValueCountFrequency (%)
C 126
15.8%
S 120
15.1%
K 103
12.9%
M 84
10.5%
D 84
10.5%
L 51
6.4%
G 37
 
4.6%
I 36
 
4.5%
H 35
 
4.4%
E 29
 
3.6%
Other values (7) 92
11.5%
Decimal Number
ValueCountFrequency (%)
1 1203
31.3%
2 1104
28.7%
3 481
 
12.5%
4 236
 
6.1%
5 215
 
5.6%
6 187
 
4.9%
7 138
 
3.6%
8 103
 
2.7%
0 93
 
2.4%
9 87
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
e 196
58.2%
l 30
 
8.9%
i 30
 
8.9%
v 20
 
5.9%
s 20
 
5.9%
h 10
 
3.0%
k 10
 
3.0%
w 9
 
2.7%
a 6
 
1.8%
g 6
 
1.8%
Other Punctuation
ValueCountFrequency (%)
, 107
87.0%
. 16
 
13.0%
Space Separator
ValueCountFrequency (%)
718
100.0%
Close Punctuation
ValueCountFrequency (%)
) 159
100.0%
Open Punctuation
ValueCountFrequency (%)
( 159
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 144
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 7
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66344
91.3%
Common 5156
 
7.1%
Latin 1141
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
파 2312
 
3.5%
아 2299
 
3.5%
트 2069
 
3.1%
지 1889
 
2.8%
대 1850
 
2.8%
동 1703
 
2.6%
차 1534
 
2.3%
단 1516
 
2.3%
신 1438
 
2.2%
성 1261
 
1.9%
Other values (381) 48473
73.1%
Latin
ValueCountFrequency (%)
e 196
17.2%
C 126
11.0%
S 120
10.5%
K 103
 
9.0%
M 84
 
7.4%
D 84
 
7.4%
L 51
 
4.5%
G 37
 
3.2%
I 36
 
3.2%
H 35
 
3.1%
Other values (18) 269
23.6%
Common
ValueCountFrequency (%)
1 1203
23.3%
2 1104
21.4%
718
13.9%
3 481
 
9.3%
4 236
 
4.6%
5 215
 
4.2%
6 187
 
3.6%
) 159
 
3.1%
( 159
 
3.1%
- 144
 
2.8%
Other values (7) 550
10.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66344
91.3%
ASCII 6290
 
8.7%
Number Forms 7
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
파 2312
 
3.5%
아 2299
 
3.5%
트 2069
 
3.1%
지 1889
 
2.8%
대 1850
 
2.8%
동 1703
 
2.6%
차 1534
 
2.3%
단 1516
 
2.3%
신 1438
 
2.2%
성 1261
 
1.9%
Other values (381) 48473
73.1%
ASCII
ValueCountFrequency (%)
1 1203
19.1%
2 1104
17.6%
718
11.4%
3 481
 
7.6%
4 236
 
3.8%
5 215
 
3.4%
e 196
 
3.1%
6 187
 
3.0%
) 159
 
2.5%
( 159
 
2.5%
Other values (34) 1632
25.9%
Number Forms
ValueCountFrequency (%)
Ⅰ 7
100.0%

아파트코드
Text

Distinct2209
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T14:59:43.853647image/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

Unique120 ?
Unique (%)1.2%

Sample

1st rowA10027188
2nd rowA13885301
3rd rowA13509004
4th rowA13084803
5th rowA13185602
ValueCountFrequency (%)
a13183502 13
 
0.1%
a12181406 13
 
0.1%
a15383905 12
 
0.1%
a15870701 12
 
0.1%
a15807705 11
 
0.1%
a13905204 11
 
0.1%
a14272309 11
 
0.1%
a15210212 11
 
0.1%
a13822701 11
 
0.1%
a15288807 11
 
0.1%
Other values (2199) 9884
98.8%
2024-05-11T14:59:44.410327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18407
20.5%
1 17670
19.6%
A 9984
11.1%
3 8769
9.7%
2 8115
9.0%
5 6333
 
7.0%
8 5772
 
6.4%
7 4721
 
5.2%
4 3934
 
4.4%
6 3308
 
3.7%
Other values (2) 2987
 
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 18407
23.0%
1 17670
22.1%
3 8769
11.0%
2 8115
10.1%
5 6333
 
7.9%
8 5772
 
7.2%
7 4721
 
5.9%
4 3934
 
4.9%
6 3308
 
4.1%
9 2971
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
A 9984
99.8%
B 16
 
0.2%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
0 18407
23.0%
1 17670
22.1%
3 8769
11.0%
2 8115
10.1%
5 6333
 
7.9%
8 5772
 
7.2%
7 4721
 
5.9%
4 3934
 
4.9%
6 3308
 
4.1%
9 2971
 
3.7%
Latin
ValueCountFrequency (%)
A 9984
99.8%
B 16
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18407
20.5%
1 17670
19.6%
A 9984
11.1%
3 8769
9.7%
2 8115
9.0%
5 6333
 
7.0%
8 5772
 
6.4%
7 4721
 
5.2%
4 3934
 
4.4%
6 3308
 
3.7%
Other values (2) 2987
 
3.3%

비용명
Text

Distinct77
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T14:59:44.736794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length9
Mean length5.9261
Min length2

Characters and Unicode

Total characters59261
Distinct characters107
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st row비품감가상각누계액
2nd row상여충당부채
3rd row관리비예치금
4th row예수금
5th row주차장충당부채
ValueCountFrequency (%)
예금 347
 
3.5%
당기순이익 335
 
3.4%
관리비미수금 334
 
3.3%
예수금 328
 
3.3%
공동주택적립금 320
 
3.2%
비품 319
 
3.2%
선급비용 315
 
3.1%
퇴직급여충당부채 308
 
3.1%
수선유지비충당부채 301
 
3.0%
미처분이익잉여금 296
 
3.0%
Other values (67) 6797
68.0%
2024-05-11T14:59:45.267791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
금 4676
 
7.9%
당 3726
 
6.3%
수 3233
 
5.5%
비 3062
 
5.2%
충 3031
 
5.1%
부 2935
 
5.0%
채 2626
 
4.4%
기 2305
 
3.9%
선 1922
 
3.2%
예 1811
 
3.1%
Other values (97) 29934
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 59261
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
금 4676
 
7.9%
당 3726
 
6.3%
수 3233
 
5.5%
비 3062
 
5.2%
충 3031
 
5.1%
부 2935
 
5.0%
채 2626
 
4.4%
기 2305
 
3.9%
선 1922
 
3.2%
예 1811
 
3.1%
Other values (97) 29934
50.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 59261
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
금 4676
 
7.9%
당 3726
 
6.3%
수 3233
 
5.5%
비 3062
 
5.2%
충 3031
 
5.1%
부 2935
 
5.0%
채 2626
 
4.4%
기 2305
 
3.9%
선 1922
 
3.2%
예 1811
 
3.1%
Other values (97) 29934
50.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 59261
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
금 4676
 
7.9%
당 3726
 
6.3%
수 3233
 
5.5%
비 3062
 
5.2%
충 3031
 
5.1%
부 2935
 
5.0%
채 2626
 
4.4%
기 2305
 
3.9%
선 1922
 
3.2%
예 1811
 
3.1%
Other values (97) 29934
50.5%

년월일
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
202011
10000 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202011
2nd row202011
3rd row202011
4th row202011
5th row202011

Common Values

ValueCountFrequency (%)
202011 10000
100.0%

Length

2024-05-11T14:59:45.435368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:59:45.566407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202011 10000
100.0%

금액
Real number (ℝ)

ZEROS 

Distinct7434
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70143146
Minimum-7.2125818 × 108
Maximum5.2345699 × 109
Zeros2241
Zeros (%)22.4%
Negative304
Negative (%)3.0%
Memory size166.0 KiB
2024-05-11T14:59:45.695276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-7.2125818 × 108
5-th percentile0
Q10
median3299275
Q335225150
95-th percentile3.6036926 × 108
Maximum5.2345699 × 109
Range5.9558281 × 109
Interquartile range (IQR)35225150

Descriptive statistics

Standard deviation2.4871378 × 108
Coefficient of variation (CV)3.5458031
Kurtosis116.95362
Mean70143146
Median Absolute Deviation (MAD)3299275
Skewness8.9086308
Sum7.0143146 × 1011
Variance6.1858547 × 1016
MonotonicityNot monotonic
2024-05-11T14:59:45.864046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2241
 
22.4%
500000 30
 
0.3%
484000 15
 
0.1%
250000 14
 
0.1%
100000 11
 
0.1%
2000000 10
 
0.1%
300000 10
 
0.1%
3000000 10
 
0.1%
600000 9
 
0.1%
200000 9
 
0.1%
Other values (7424) 7641
76.4%
ValueCountFrequency (%)
-721258178 1
< 0.1%
-269665410 1
< 0.1%
-163934940 1
< 0.1%
-145925010 1
< 0.1%
-138881815 1
< 0.1%
-138288333 1
< 0.1%
-117859134 1
< 0.1%
-112350030 1
< 0.1%
-102572890 1
< 0.1%
-93851930 1
< 0.1%
ValueCountFrequency (%)
5234569921 1
< 0.1%
5153608837 1
< 0.1%
4549930706 1
< 0.1%
4479047170 1
< 0.1%
4438531106 1
< 0.1%
4227412758 1
< 0.1%
3813507246 2
< 0.1%
3565154654 1
< 0.1%
3226222970 1
< 0.1%
3151897116 1
< 0.1%

Interactions

2024-05-11T14:59:42.364048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T14:59:45.979352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비용명금액
비용명1.0000.480
금액0.4801.000

Missing values

2024-05-11T14:59:42.523506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T14:59:42.637148image/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
4249금천롯데캐슬골드파크1차아파트A10027188비품감가상각누계액202011-93851930
39632송파현대1차A13885301상여충당부채2020110
26917삼성한솔A13509004관리비예치금20201142595000
15820전농우성A13084803예수금2020112642355
17844상봉우정A13185602주차장충당부채2020110
219인왕산어울림 아파트A10024620선급비용2020117202810
17061신내6단지A13176901공동주택적립금20201134930152
67524목동6단지A15875103관리비예치금20201160300000
37830올림픽훼밀리타운A13820201시설보수충당부채202011164435091
38429한신코아A13824003저장품20201197000
아파트명아파트코드비용명년월일금액
63471마곡수명산파크7단지A15728005선수관리비2020111699365
63424마곡수명산파크2단지A15728004선수난방비2020110
43326상계주공12단지A13982202장기수선충당예금2020111198843068
55729오류금강A15210206비품2020111930950
12543북한산현대홈타운A12204102선수수도료2020111185830
21828행당두산A13307001가수금20201110117200
57667신도림대림7차e-편한세상A15288807승강기유지비충당부채2020110
66698목동삼성쉐르빌2차A15807601미수금2020114365900
54380대학동현대(구신림9동)A15186002장기수선충당부채202011284525601
45757동부이촌동우성A14003001선수관리비2020110