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 is highly skewed (γ1 = 27.55818781)Skewed
금액 has 2245 (22.4%) zerosZeros

Reproduction

Analysis started2024-05-11 05:59:08.595582
Analysis finished2024-05-11 05:59:09.541305
Duration0.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

Distinct2230
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T14:59:09.771616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length7.2722
Min length2

Characters and Unicode

Total characters72722
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

Unique144 ?
Unique (%)1.4%

Sample

1st row상암월드컵1단지
2nd row노원 센트럴푸르지오
3rd row가락삼익맨션
4th row묵동금호어울림
5th row신길남서울
ValueCountFrequency (%)
아파트 143
 
1.3%
래미안 26
 
0.2%
아이파크 17
 
0.2%
래미안수유 16
 
0.2%
래미안밤섬리베뉴 14
 
0.1%
e편한세상 14
 
0.1%
신도림현대 14
 
0.1%
고덕 14
 
0.1%
신반포 13
 
0.1%
경남아너스빌 13
 
0.1%
Other values (2298) 10321
97.3%
2024-05-11T14:59:10.324174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 2530
 
3.5%
파 2440
 
3.4%
트 2244
 
3.1%
대 1811
 
2.5%
지 1794
 
2.5%
동 1661
 
2.3%
차 1512
 
2.1%
신 1443
 
2.0%
단 1408
 
1.9%
성 1309
 
1.8%
Other values (427) 54570
75.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66726
91.8%
Decimal Number 3684
 
5.1%
Uppercase Letter 724
 
1.0%
Space Separator 685
 
0.9%
Lowercase Letter 318
 
0.4%
Open Punctuation 156
 
0.2%
Close Punctuation 156
 
0.2%
Dash Punctuation 136
 
0.2%
Other Punctuation 131
 
0.2%
Letter Number 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 2530
 
3.8%
파 2440
 
3.7%
트 2244
 
3.4%
대 1811
 
2.7%
지 1794
 
2.7%
동 1661
 
2.5%
차 1512
 
2.3%
신 1443
 
2.2%
단 1408
 
2.1%
성 1309
 
2.0%
Other values (382) 48574
72.8%
Uppercase Letter
ValueCountFrequency (%)
S 133
18.4%
K 91
12.6%
C 85
11.7%
L 69
9.5%
H 55
7.6%
M 52
 
7.2%
D 52
 
7.2%
I 38
 
5.2%
E 34
 
4.7%
G 26
 
3.6%
Other values (7) 89
12.3%
Lowercase Letter
ValueCountFrequency (%)
e 202
63.5%
l 26
 
8.2%
s 21
 
6.6%
i 19
 
6.0%
v 14
 
4.4%
h 12
 
3.8%
k 11
 
3.5%
c 4
 
1.3%
a 3
 
0.9%
w 3
 
0.9%
Decimal Number
ValueCountFrequency (%)
1 1098
29.8%
2 1097
29.8%
3 493
13.4%
4 242
 
6.6%
5 210
 
5.7%
6 152
 
4.1%
7 125
 
3.4%
9 104
 
2.8%
8 89
 
2.4%
0 74
 
2.0%
Other Punctuation
ValueCountFrequency (%)
, 107
81.7%
. 24
 
18.3%
Space Separator
ValueCountFrequency (%)
685
100.0%
Open Punctuation
ValueCountFrequency (%)
( 156
100.0%
Close Punctuation
ValueCountFrequency (%)
) 156
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 136
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66726
91.8%
Common 4948
 
6.8%
Latin 1048
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 2530
 
3.8%
파 2440
 
3.7%
트 2244
 
3.4%
대 1811
 
2.7%
지 1794
 
2.7%
동 1661
 
2.5%
차 1512
 
2.3%
신 1443
 
2.2%
단 1408
 
2.1%
성 1309
 
2.0%
Other values (382) 48574
72.8%
Latin
ValueCountFrequency (%)
e 202
19.3%
S 133
12.7%
K 91
 
8.7%
C 85
 
8.1%
L 69
 
6.6%
H 55
 
5.2%
M 52
 
5.0%
D 52
 
5.0%
I 38
 
3.6%
E 34
 
3.2%
Other values (19) 237
22.6%
Common
ValueCountFrequency (%)
1 1098
22.2%
2 1097
22.2%
685
13.8%
3 493
10.0%
4 242
 
4.9%
5 210
 
4.2%
( 156
 
3.2%
) 156
 
3.2%
6 152
 
3.1%
- 136
 
2.7%
Other values (6) 523
10.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66726
91.8%
ASCII 5990
 
8.2%
Number Forms 6
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 2530
 
3.8%
파 2440
 
3.7%
트 2244
 
3.4%
대 1811
 
2.7%
지 1794
 
2.7%
동 1661
 
2.5%
차 1512
 
2.3%
신 1443
 
2.2%
단 1408
 
2.1%
성 1309
 
2.0%
Other values (382) 48574
72.8%
ASCII
ValueCountFrequency (%)
1 1098
18.3%
2 1097
18.3%
685
11.4%
3 493
 
8.2%
4 242
 
4.0%
5 210
 
3.5%
e 202
 
3.4%
( 156
 
2.6%
) 156
 
2.6%
6 152
 
2.5%
Other values (34) 1499
25.0%
Number Forms
ValueCountFrequency (%)
Ⅰ 6
100.0%

아파트코드
Text

Distinct2235
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T14:59:10.785794image/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

Unique144 ?
Unique (%)1.4%

Sample

1st rowA12127007
2nd rowA10025133
3rd rowA13885306
4th rowA13114103
5th rowA15085805
ValueCountFrequency (%)
a14207202 16
 
0.2%
a13986306 13
 
0.1%
a13707016 12
 
0.1%
a12119004 12
 
0.1%
a13282510 12
 
0.1%
a13704404 12
 
0.1%
a12287204 11
 
0.1%
a13410001 11
 
0.1%
a12012203 11
 
0.1%
a15277302 11
 
0.1%
Other values (2225) 9879
98.8%
2024-05-11T14:59:11.334929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18396
20.4%
1 17600
19.6%
A 9995
11.1%
3 8892
9.9%
2 8229
9.1%
5 6266
 
7.0%
8 5674
 
6.3%
7 4770
 
5.3%
4 3924
 
4.4%
6 3299
 
3.7%
Other values (2) 2955
 
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 18396
23.0%
1 17600
22.0%
3 8892
11.1%
2 8229
10.3%
5 6266
 
7.8%
8 5674
 
7.1%
7 4770
 
6.0%
4 3924
 
4.9%
6 3299
 
4.1%
9 2950
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
A 9995
> 99.9%
B 5
 
< 0.1%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
0 18396
23.0%
1 17600
22.0%
3 8892
11.1%
2 8229
10.3%
5 6266
 
7.8%
8 5674
 
7.1%
7 4770
 
6.0%
4 3924
 
4.9%
6 3299
 
4.1%
9 2950
 
3.7%
Latin
ValueCountFrequency (%)
A 9995
> 99.9%
B 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18396
20.4%
1 17600
19.6%
A 9995
11.1%
3 8892
9.9%
2 8229
9.1%
5 6266
 
7.0%
8 5674
 
6.3%
7 4770
 
5.3%
4 3924
 
4.4%
6 3299
 
3.7%
Other values (2) 2955
 
3.3%

비용명
Text

Distinct77
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T14:59:11.664784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length5.9783
Min length2

Characters and Unicode

Total characters59783
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 row미수금
2nd row단기보증금
3rd row청소비충당부채
4th row비품감가상각누계액
5th row장기수선충당예금
ValueCountFrequency (%)
장기수선충당부채 326
 
3.3%
미처분이익잉여금 325
 
3.2%
예금 319
 
3.2%
연차수당충당부채 315
 
3.1%
비품 304
 
3.0%
예수금 298
 
3.0%
수선유지비충당부채 294
 
2.9%
퇴직급여충당부채 293
 
2.9%
공동주택적립금 293
 
2.9%
관리비미수금 290
 
2.9%
Other values (67) 6943
69.4%
2024-05-11T14:59:12.212133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
금 4615
 
7.7%
당 3884
 
6.5%
수 3263
 
5.5%
충 3089
 
5.2%
비 3055
 
5.1%
부 3007
 
5.0%
채 2706
 
4.5%
기 2451
 
4.1%
선 1949
 
3.3%
예 1759
 
2.9%
Other values (97) 30005
50.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 59783
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
금 4615
 
7.7%
당 3884
 
6.5%
수 3263
 
5.5%
충 3089
 
5.2%
비 3055
 
5.1%
부 3007
 
5.0%
채 2706
 
4.5%
기 2451
 
4.1%
선 1949
 
3.3%
예 1759
 
2.9%
Other values (97) 30005
50.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 59783
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
금 4615
 
7.7%
당 3884
 
6.5%
수 3263
 
5.5%
충 3089
 
5.2%
비 3055
 
5.1%
부 3007
 
5.0%
채 2706
 
4.5%
기 2451
 
4.1%
선 1949
 
3.3%
예 1759
 
2.9%
Other values (97) 30005
50.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 59783
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
금 4615
 
7.7%
당 3884
 
6.5%
수 3263
 
5.5%
충 3089
 
5.2%
비 3055
 
5.1%
부 3007
 
5.0%
채 2706
 
4.5%
기 2451
 
4.1%
선 1949
 
3.3%
예 1759
 
2.9%
Other values (97) 30005
50.2%

년월일
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
202105
10000 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202105
2nd row202105
3rd row202105
4th row202105
5th row202105

Common Values

ValueCountFrequency (%)
202105 10000
100.0%

Length

2024-05-11T14:59:12.423354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:59:12.556664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202105 10000
100.0%

금액
Real number (ℝ)

SKEWED  ZEROS 

Distinct7405
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75847863
Minimum-3.7940018 × 108
Maximum2.2280004 × 1010
Zeros2245
Zeros (%)22.4%
Negative309
Negative (%)3.1%
Memory size166.0 KiB
2024-05-11T14:59:12.712862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.7940018 × 108
5-th percentile0
Q10
median2991602
Q331624531
95-th percentile3.550007 × 108
Maximum2.2280004 × 1010
Range2.2659404 × 1010
Interquartile range (IQR)31624531

Descriptive statistics

Standard deviation3.7480733 × 108
Coefficient of variation (CV)4.9415675
Kurtosis1343.5004
Mean75847863
Median Absolute Deviation (MAD)2991602
Skewness27.558188
Sum7.5847863 × 1011
Variance1.4048054 × 1017
MonotonicityNot monotonic
2024-05-11T14:59:12.928549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2245
 
22.4%
500000 28
 
0.3%
250000 19
 
0.2%
300000 16
 
0.2%
100000 12
 
0.1%
10000000 12
 
0.1%
200000 12
 
0.1%
484000 11
 
0.1%
242000 10
 
0.1%
20000000 10
 
0.1%
Other values (7395) 7625
76.2%
ValueCountFrequency (%)
-379400176 1
< 0.1%
-282715690 1
< 0.1%
-244221450 1
< 0.1%
-188835170 1
< 0.1%
-174861035 1
< 0.1%
-164419830 1
< 0.1%
-151047428 1
< 0.1%
-105100222 1
< 0.1%
-102572890 1
< 0.1%
-94109790 1
< 0.1%
ValueCountFrequency (%)
22280004067 1
< 0.1%
9030477783 2
< 0.1%
7730136534 1
< 0.1%
6187259197 1
< 0.1%
5463363925 1
< 0.1%
5243342839 1
< 0.1%
4686700289 1
< 0.1%
4406242565 1
< 0.1%
3995284043 1
< 0.1%
3830644491 1
< 0.1%

Interactions

2024-05-11T14:59:09.135682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T14:59:13.064426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비용명금액
비용명1.0000.175
금액0.1751.000

Missing values

2024-05-11T14:59:09.298599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T14:59:09.463857image/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
10946상암월드컵1단지A12127007미수금2021050
1724노원 센트럴푸르지오A10025133단기보증금202105972000
41098가락삼익맨션A13885306청소비충당부채20210524530650
17232묵동금호어울림A13114103비품감가상각누계액202105-8136750
54260신길남서울A15085805장기수선충당예금202105335359375
50419구의현대7단지A14320001전신전화가입권2021050
29589개포4차우성A13527013선급비용202105898130
69810목동금호베스트빌A15880905선급비용20210532100608
53487영등포아트자이A15076702선급금202105296150
22725행당두산A13307001공동주택적립금2021050
아파트명아파트코드비용명년월일금액
51970당산현대3차A15004406저장품20210587450
12770대주피오레아파트A12201001퇴직급여충당예금2021050
1486보라매 sk뷰A10025070비품20210550976560
58785신도림쌍용플래티넘노블A15283801안전진단비충당부채2021051215680
63518사당극동A15681503주차장충당예금2021050
62616동작상떼빌주상복합A15670001장기수선충당부채202105981800486
49329미아경남아너스빌A14272306기타충당부채20210571741498
12631월드컵참누리A12187906주차장충당부채20210512659691
63133흑석한강푸르지오A15679108기타유동부채20210548134318
29651도곡개포한신아파트A13527016퇴직급여충당예금202105304511700