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 1973 (19.7%) zerosZeros

Reproduction

Analysis started2024-05-11 06:02:15.755459
Analysis finished2024-05-11 06:02:16.814611
Duration1.06 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

Distinct2107
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T15:02:17.062625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length7.1649
Min length2

Characters and Unicode

Total characters71649
Distinct characters431
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

Unique85 ?
Unique (%)0.9%

Sample

1st row흑석한강센트레빌
2nd row개봉두산
3rd row번동삼성
4th row동부이촌동우성
5th row묵동신도
ValueCountFrequency (%)
아파트 101
 
1.0%
래미안 40
 
0.4%
신사씨티 17
 
0.2%
신내 16
 
0.2%
서울숲2차푸르지오임대 14
 
0.1%
신도림현대 13
 
0.1%
역삼아이파크 13
 
0.1%
은평뉴타운상림마을13단지 13
 
0.1%
수서삼익 12
 
0.1%
동일하이빌뉴시티 12
 
0.1%
Other values (2159) 10208
97.6%
2024-05-11T15:02:17.701618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 2111
 
2.9%
파 2036
 
2.8%
대 1911
 
2.7%
지 1878
 
2.6%
트 1843
 
2.6%
동 1680
 
2.3%
차 1570
 
2.2%
신 1568
 
2.2%
단 1475
 
2.1%
성 1377
 
1.9%
Other values (421) 54200
75.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 65539
91.5%
Decimal Number 3926
 
5.5%
Uppercase Letter 739
 
1.0%
Space Separator 516
 
0.7%
Lowercase Letter 353
 
0.5%
Close Punctuation 151
 
0.2%
Open Punctuation 151
 
0.2%
Dash Punctuation 135
 
0.2%
Other Punctuation 125
 
0.2%
Math Symbol 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 2111
 
3.2%
파 2036
 
3.1%
대 1911
 
2.9%
지 1878
 
2.9%
트 1843
 
2.8%
동 1680
 
2.6%
차 1570
 
2.4%
신 1568
 
2.4%
단 1475
 
2.3%
성 1377
 
2.1%
Other values (375) 48090
73.4%
Uppercase Letter
ValueCountFrequency (%)
S 139
18.8%
K 102
13.8%
C 76
10.3%
L 55
 
7.4%
H 46
 
6.2%
G 45
 
6.1%
D 40
 
5.4%
M 40
 
5.4%
I 39
 
5.3%
E 38
 
5.1%
Other values (7) 119
16.1%
Lowercase Letter
ValueCountFrequency (%)
e 190
53.8%
l 46
 
13.0%
i 39
 
11.0%
v 26
 
7.4%
s 11
 
3.1%
w 11
 
3.1%
h 8
 
2.3%
k 6
 
1.7%
c 6
 
1.7%
g 5
 
1.4%
Decimal Number
ValueCountFrequency (%)
2 1197
30.5%
1 1173
29.9%
3 502
12.8%
4 265
 
6.7%
5 206
 
5.2%
6 146
 
3.7%
7 128
 
3.3%
8 113
 
2.9%
9 98
 
2.5%
0 98
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 102
81.6%
. 23
 
18.4%
Space Separator
ValueCountFrequency (%)
516
100.0%
Close Punctuation
ValueCountFrequency (%)
) 151
100.0%
Open Punctuation
ValueCountFrequency (%)
( 151
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 135
100.0%
Math Symbol
ValueCountFrequency (%)
~ 9
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 65539
91.5%
Common 5013
 
7.0%
Latin 1097
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 2111
 
3.2%
파 2036
 
3.1%
대 1911
 
2.9%
지 1878
 
2.9%
트 1843
 
2.8%
동 1680
 
2.6%
차 1570
 
2.4%
신 1568
 
2.4%
단 1475
 
2.3%
성 1377
 
2.1%
Other values (375) 48090
73.4%
Latin
ValueCountFrequency (%)
e 190
17.3%
S 139
12.7%
K 102
 
9.3%
C 76
 
6.9%
L 55
 
5.0%
H 46
 
4.2%
l 46
 
4.2%
G 45
 
4.1%
D 40
 
3.6%
M 40
 
3.6%
Other values (19) 318
29.0%
Common
ValueCountFrequency (%)
2 1197
23.9%
1 1173
23.4%
516
10.3%
3 502
10.0%
4 265
 
5.3%
5 206
 
4.1%
) 151
 
3.0%
( 151
 
3.0%
6 146
 
2.9%
- 135
 
2.7%
Other values (7) 571
11.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 65539
91.5%
ASCII 6105
 
8.5%
Number Forms 5
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 2111
 
3.2%
파 2036
 
3.1%
대 1911
 
2.9%
지 1878
 
2.9%
트 1843
 
2.8%
동 1680
 
2.6%
차 1570
 
2.4%
신 1568
 
2.4%
단 1475
 
2.3%
성 1377
 
2.1%
Other values (375) 48090
73.4%
ASCII
ValueCountFrequency (%)
2 1197
19.6%
1 1173
19.2%
516
 
8.5%
3 502
 
8.2%
4 265
 
4.3%
5 206
 
3.4%
e 190
 
3.1%
) 151
 
2.5%
( 151
 
2.5%
6 146
 
2.4%
Other values (35) 1608
26.3%
Number Forms
ValueCountFrequency (%)
Ⅰ 5
100.0%

아파트코드
Text

Distinct2113
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T15:02:18.156150image/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

Unique85 ?
Unique (%)0.9%

Sample

1st rowA15679107
2nd rowA15209203
3rd rowA14206001
4th rowA14003001
5th rowA13184804
ValueCountFrequency (%)
a12208102 17
 
0.2%
a12220002 13
 
0.1%
a13508009 13
 
0.1%
a13522003 12
 
0.1%
a13613011 12
 
0.1%
a15681110 11
 
0.1%
a10027221 11
 
0.1%
a13519006 11
 
0.1%
a13204510 11
 
0.1%
a13528102 11
 
0.1%
Other values (2103) 9878
98.8%
2024-05-11T15:02:18.916149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18174
20.2%
1 17716
19.7%
A 9988
11.1%
3 9048
10.1%
2 7975
8.9%
5 6148
 
6.8%
8 5815
 
6.5%
7 4847
 
5.4%
4 3852
 
4.3%
6 3363
 
3.7%
Other values (2) 3074
 
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 18174
22.7%
1 17716
22.1%
3 9048
11.3%
2 7975
10.0%
5 6148
 
7.7%
8 5815
 
7.3%
7 4847
 
6.1%
4 3852
 
4.8%
6 3363
 
4.2%
9 3062
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
A 9988
99.9%
B 12
 
0.1%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
0 18174
22.7%
1 17716
22.1%
3 9048
11.3%
2 7975
10.0%
5 6148
 
7.7%
8 5815
 
7.3%
7 4847
 
6.1%
4 3852
 
4.8%
6 3363
 
4.2%
9 3062
 
3.8%
Latin
ValueCountFrequency (%)
A 9988
99.9%
B 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18174
20.2%
1 17716
19.7%
A 9988
11.1%
3 9048
10.1%
2 7975
8.9%
5 6148
 
6.8%
8 5815
 
6.5%
7 4847
 
5.4%
4 3852
 
4.3%
6 3363
 
3.7%
Other values (2) 3074
 
3.4%

비용명
Text

Distinct77
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T15:02:19.402393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length9
Mean length5.9633
Min length2

Characters and Unicode

Total characters59633
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

Unique0 ?
Unique (%)0.0%

Sample

1st row예금
2nd row미수관리비예치금
3rd row임대보증금
4th row미지급비용
5th row기타재고자산
ValueCountFrequency (%)
관리비미수금 360
 
3.6%
공동주택적립금 326
 
3.3%
예금 322
 
3.2%
장기수선충당예금 322
 
3.2%
미처분이익잉여금 316
 
3.2%
현금 306
 
3.1%
선급비용 305
 
3.0%
예수금 304
 
3.0%
미지급금 299
 
3.0%
비품 299
 
3.0%
Other values (67) 6841
68.4%
2024-05-11T15:02:20.089315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
금 4842
 
8.1%
당 3735
 
6.3%
수 3246
 
5.4%
충 3096
 
5.2%
비 2981
 
5.0%
부 2908
 
4.9%
채 2609
 
4.4%
기 2328
 
3.9%
선 1954
 
3.3%
예 1838
 
3.1%
Other values (97) 30096
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 59633
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
금 4842
 
8.1%
당 3735
 
6.3%
수 3246
 
5.4%
충 3096
 
5.2%
비 2981
 
5.0%
부 2908
 
4.9%
채 2609
 
4.4%
기 2328
 
3.9%
선 1954
 
3.3%
예 1838
 
3.1%
Other values (97) 30096
50.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 59633
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
금 4842
 
8.1%
당 3735
 
6.3%
수 3246
 
5.4%
충 3096
 
5.2%
비 2981
 
5.0%
부 2908
 
4.9%
채 2609
 
4.4%
기 2328
 
3.9%
선 1954
 
3.3%
예 1838
 
3.1%
Other values (97) 30096
50.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 59633
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
금 4842
 
8.1%
당 3735
 
6.3%
수 3246
 
5.4%
충 3096
 
5.2%
비 2981
 
5.0%
부 2908
 
4.9%
채 2609
 
4.4%
기 2328
 
3.9%
선 1954
 
3.3%
예 1838
 
3.1%
Other values (97) 30096
50.5%

년월일
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
201901
10000 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201901
2nd row201901
3rd row201901
4th row201901
5th row201901

Common Values

ValueCountFrequency (%)
201901 10000
100.0%

Length

2024-05-11T15:02:20.321960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:02:20.506878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201901 10000
100.0%

금액
Real number (ℝ)

ZEROS 

Distinct7700
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73933154
Minimum-8.1675414 × 108
Maximum1.4377587 × 1010
Zeros1973
Zeros (%)19.7%
Negative377
Negative (%)3.8%
Memory size166.0 KiB
2024-05-11T15:02:20.684305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-8.1675414 × 108
5-th percentile0
Q126815
median3504734
Q338050525
95-th percentile3.5793195 × 108
Maximum1.4377587 × 1010
Range1.5194341 × 1010
Interquartile range (IQR)38023710

Descriptive statistics

Standard deviation3.1003553 × 108
Coefficient of variation (CV)4.1934574
Kurtosis606.72614
Mean73933154
Median Absolute Deviation (MAD)3504734
Skewness18.363537
Sum7.3933154 × 1011
Variance9.612203 × 1016
MonotonicityNot monotonic
2024-05-11T15:02:21.249060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1973
 
19.7%
250000 20
 
0.2%
500000 18
 
0.2%
484000 17
 
0.2%
300000 13
 
0.1%
100000 13
 
0.1%
200000 11
 
0.1%
1000000 9
 
0.1%
10000000 9
 
0.1%
242000 9
 
0.1%
Other values (7690) 7908
79.1%
ValueCountFrequency (%)
-816754141 1
< 0.1%
-282000000 1
< 0.1%
-229703050 1
< 0.1%
-188414870 1
< 0.1%
-167175316 1
< 0.1%
-140789241 1
< 0.1%
-121162794 1
< 0.1%
-119105700 1
< 0.1%
-117055590 1
< 0.1%
-110611780 1
< 0.1%
ValueCountFrequency (%)
14377587114 1
< 0.1%
9419487206 1
< 0.1%
5852909154 1
< 0.1%
5672176134 1
< 0.1%
5580403226 1
< 0.1%
5063163132 1
< 0.1%
4994129198 1
< 0.1%
4700267545 1
< 0.1%
4301918556 1
< 0.1%
4054104151 1
< 0.1%

Interactions

2024-05-11T15:02:16.418994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T15:02:21.441636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비용명금액
비용명1.0000.437
금액0.4371.000

Missing values

2024-05-11T15:02:16.612236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T15:02:16.753996image/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
57128흑석한강센트레빌A15679107예금201901267639194
51832개봉두산A15209203미수관리비예치금2019010
43569번동삼성A14206001임대보증금20190110300000
42342동부이촌동우성A14003001미지급비용20190162324820
14906묵동신도A13184804기타재고자산201901471900
51743개봉동아이파크A15209004수선유지비충당부채20190115713960
6368북아현두산A12079501공동주택적립금20190136816046
4202광화문풍림스페이스본 아파트A11005401공동주택적립금20190189242636
43090한가람아파트A14072701임차보증금201901500000
30175정릉1차e-편한세상A13676703미처분이익잉여금20190132599016
아파트명아파트코드비용명년월일금액
27699동소문동송산A13603401장기수선충당부채201901140016890
58232등촌주공10단지A15703306선수관리비2019010
59726화곡초록A15770801기타의비유동자산2019010
45057현대강변A14319201공동체활성화단체지원적립금20190117594510
51502고척벽산베스트블루밍A15208006미처분이익잉여금20190114507674
9102성산2차현대A12187703장기수선충당부채201901626197894
180e편한세상화랑대아파트A10025855퇴직급여충당부채20190130837480
20485사근중앙하이츠A13381701장기수선충당예금201901353686502
55339신대방현대A15601105연차수당충당부채20190123515102
3454SH황학롯데캐슬베네치아A10044001주차장충당부채2019018262500