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 1023 (10.2%) zerosZeros

Reproduction

Analysis started2024-05-11 06:52:36.059761
Analysis finished2024-05-11 06:52:38.354073
Duration2.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

Distinct2234
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T06:52:38.620420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length19
Mean length7.4187
Min length2

Characters and Unicode

Total characters74187
Distinct characters434
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

Unique134 ?
Unique (%)1.3%

Sample

1st row신구로자이
2nd row당산강변래미안3차
3rd row고척벽산베스트블루밍
4th row잠실우성4차
5th row마곡13단지 힐스테이트마스터 아파트
ValueCountFrequency (%)
아파트 202
 
1.9%
e편한세상 29
 
0.3%
래미안 29
 
0.3%
아이파크 21
 
0.2%
고덕 19
 
0.2%
해모로 18
 
0.2%
북한산 18
 
0.2%
이편한세상 17
 
0.2%
푸르지오 16
 
0.1%
sk뷰 16
 
0.1%
Other values (2314) 10493
96.5%
2024-05-11T06:52:39.416674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 2632
 
3.5%
파 2613
 
3.5%
트 2427
 
3.3%
지 1895
 
2.6%
대 1774
 
2.4%
동 1691
 
2.3%
단 1486
 
2.0%
차 1476
 
2.0%
이 1442
 
1.9%
신 1402
 
1.9%
Other values (424) 55349
74.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67792
91.4%
Decimal Number 3738
 
5.0%
Space Separator 962
 
1.3%
Uppercase Letter 825
 
1.1%
Lowercase Letter 316
 
0.4%
Dash Punctuation 148
 
0.2%
Close Punctuation 136
 
0.2%
Open Punctuation 136
 
0.2%
Other Punctuation 126
 
0.2%
Letter Number 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 2632
 
3.9%
파 2613
 
3.9%
트 2427
 
3.6%
지 1895
 
2.8%
대 1774
 
2.6%
동 1691
 
2.5%
단 1486
 
2.2%
차 1476
 
2.2%
이 1442
 
2.1%
신 1402
 
2.1%
Other values (379) 48954
72.2%
Uppercase Letter
ValueCountFrequency (%)
S 152
18.4%
C 99
12.0%
K 99
12.0%
D 73
8.8%
M 73
8.8%
H 63
7.6%
L 61
7.4%
E 45
 
5.5%
I 43
 
5.2%
V 30
 
3.6%
Other values (7) 87
10.5%
Lowercase Letter
ValueCountFrequency (%)
e 180
57.0%
l 38
 
12.0%
i 25
 
7.9%
v 19
 
6.0%
s 14
 
4.4%
k 13
 
4.1%
c 10
 
3.2%
h 8
 
2.5%
w 5
 
1.6%
g 2
 
0.6%
Decimal Number
ValueCountFrequency (%)
1 1118
29.9%
2 1060
28.4%
3 513
13.7%
4 261
 
7.0%
5 232
 
6.2%
6 153
 
4.1%
7 136
 
3.6%
9 108
 
2.9%
8 85
 
2.3%
0 72
 
1.9%
Other Punctuation
ValueCountFrequency (%)
, 97
77.0%
. 29
 
23.0%
Space Separator
ValueCountFrequency (%)
962
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 148
100.0%
Close Punctuation
ValueCountFrequency (%)
) 136
100.0%
Open Punctuation
ValueCountFrequency (%)
( 136
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67792
91.4%
Common 5246
 
7.1%
Latin 1149
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 2632
 
3.9%
파 2613
 
3.9%
트 2427
 
3.6%
지 1895
 
2.8%
대 1774
 
2.6%
동 1691
 
2.5%
단 1486
 
2.2%
차 1476
 
2.2%
이 1442
 
2.1%
신 1402
 
2.1%
Other values (379) 48954
72.2%
Latin
ValueCountFrequency (%)
e 180
15.7%
S 152
13.2%
C 99
 
8.6%
K 99
 
8.6%
D 73
 
6.4%
M 73
 
6.4%
H 63
 
5.5%
L 61
 
5.3%
E 45
 
3.9%
I 43
 
3.7%
Other values (19) 261
22.7%
Common
ValueCountFrequency (%)
1 1118
21.3%
2 1060
20.2%
962
18.3%
3 513
9.8%
4 261
 
5.0%
5 232
 
4.4%
6 153
 
2.9%
- 148
 
2.8%
7 136
 
2.6%
) 136
 
2.6%
Other values (6) 527
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67792
91.4%
ASCII 6387
 
8.6%
Number Forms 8
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 2632
 
3.9%
파 2613
 
3.9%
트 2427
 
3.6%
지 1895
 
2.8%
대 1774
 
2.6%
동 1691
 
2.5%
단 1486
 
2.2%
차 1476
 
2.2%
이 1442
 
2.1%
신 1402
 
2.1%
Other values (379) 48954
72.2%
ASCII
ValueCountFrequency (%)
1 1118
17.5%
2 1060
16.6%
962
15.1%
3 513
 
8.0%
4 261
 
4.1%
5 232
 
3.6%
e 180
 
2.8%
6 153
 
2.4%
S 152
 
2.4%
- 148
 
2.3%
Other values (34) 1608
25.2%
Number Forms
ValueCountFrequency (%)
Ⅰ 8
100.0%

아파트코드
Text

Distinct2240
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T06:52:40.140144image/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

Unique135 ?
Unique (%)1.4%

Sample

1st rowA15205508
2nd rowA15081002
3rd rowA15208006
4th rowA13822902
5th rowA10026879
ValueCountFrequency (%)
a15601102 14
 
0.1%
a15681802 12
 
0.1%
a13879102 12
 
0.1%
a14206002 12
 
0.1%
a15721004 11
 
0.1%
a13122104 11
 
0.1%
a13876112 11
 
0.1%
a13204203 11
 
0.1%
a13785302 11
 
0.1%
a15210209 10
 
0.1%
Other values (2230) 9885
98.9%
2024-05-11T06:52:41.312362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18439
20.5%
1 17371
19.3%
A 10000
11.1%
3 8687
9.7%
2 8444
9.4%
5 6232
 
6.9%
8 5663
 
6.3%
7 4637
 
5.2%
4 4202
 
4.7%
6 3309
 
3.7%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18439
23.0%
1 17371
21.7%
3 8687
10.9%
2 8444
10.6%
5 6232
 
7.8%
8 5663
 
7.1%
7 4637
 
5.8%
4 4202
 
5.3%
6 3309
 
4.1%
9 3016
 
3.8%
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 18439
23.0%
1 17371
21.7%
3 8687
10.9%
2 8444
10.6%
5 6232
 
7.8%
8 5663
 
7.1%
7 4637
 
5.8%
4 4202
 
5.3%
6 3309
 
4.1%
9 3016
 
3.8%
Latin
ValueCountFrequency (%)
A 10000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18439
20.5%
1 17371
19.3%
A 10000
11.1%
3 8687
9.7%
2 8444
9.4%
5 6232
 
6.9%
8 5663
 
6.3%
7 4637
 
5.2%
4 4202
 
4.7%
6 3309
 
3.7%

비용명
Text

Distinct85
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T06:52:41.917711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length4.8434
Min length2

Characters and Unicode

Total characters48434
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st row제수당
2nd row수선유지비
3rd row복리후생비
4th row위탁관리수수료
5th row고용안정사업비용
ValueCountFrequency (%)
수선유지비 230
 
2.3%
소독비 229
 
2.3%
세대수도료 226
 
2.3%
국민연금 225
 
2.2%
경비비 225
 
2.2%
승강기유지비 222
 
2.2%
통신비 222
 
2.2%
입주자대표회의운영비 219
 
2.2%
보험료 219
 
2.2%
청소비 217
 
2.2%
Other values (75) 7766
77.7%
2024-05-11T06:52:43.040084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
비 5385
 
11.1%
수 3600
 
7.4%
료 2201
 
4.5%
익 1969
 
4.1%
용 1706
 
3.5%
기 1270
 
2.6%
대 1091
 
2.3%
보 881
 
1.8%
리 849
 
1.8%
험 833
 
1.7%
Other values (110) 28649
59.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 48434
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
비 5385
 
11.1%
수 3600
 
7.4%
료 2201
 
4.5%
익 1969
 
4.1%
용 1706
 
3.5%
기 1270
 
2.6%
대 1091
 
2.3%
보 881
 
1.8%
리 849
 
1.8%
험 833
 
1.7%
Other values (110) 28649
59.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 48434
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
비 5385
 
11.1%
수 3600
 
7.4%
료 2201
 
4.5%
익 1969
 
4.1%
용 1706
 
3.5%
기 1270
 
2.6%
대 1091
 
2.3%
보 881
 
1.8%
리 849
 
1.8%
험 833
 
1.7%
Other values (110) 28649
59.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 48434
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
비 5385
 
11.1%
수 3600
 
7.4%
료 2201
 
4.5%
익 1969
 
4.1%
용 1706
 
3.5%
기 1270
 
2.6%
대 1091
 
2.3%
보 881
 
1.8%
리 849
 
1.8%
험 833
 
1.7%
Other values (110) 28649
59.2%

년월일
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
202204
10000 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202204
2nd row202204
3rd row202204
4th row202204
5th row202204

Common Values

ValueCountFrequency (%)
202204 10000
100.0%

Length

2024-05-11T06:52:43.416835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:52:43.694744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202204 10000
100.0%

금액
Real number (ℝ)

ZEROS 

Distinct7194
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3376058.2
Minimum-2949560
Maximum5.8392169 × 108
Zeros1023
Zeros (%)10.2%
Negative6
Negative (%)0.1%
Memory size166.0 KiB
2024-05-11T06:52:44.036012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2949560
5-th percentile0
Q186839.5
median322766.5
Q31446710
95-th percentile16971636
Maximum5.8392169 × 108
Range5.8687125 × 108
Interquartile range (IQR)1359870.5

Descriptive statistics

Standard deviation12889295
Coefficient of variation (CV)3.8178533
Kurtosis548.21984
Mean3376058.2
Median Absolute Deviation (MAD)316808
Skewness17.183434
Sum3.3760582 × 1010
Variance1.6613392 × 1014
MonotonicityNot monotonic
2024-05-11T06:52:44.473316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1023
 
10.2%
200000 88
 
0.9%
100000 59
 
0.6%
300000 59
 
0.6%
50000 47
 
0.5%
150000 41
 
0.4%
400000 36
 
0.4%
30000 32
 
0.3%
110000 29
 
0.3%
550000 28
 
0.3%
Other values (7184) 8558
85.6%
ValueCountFrequency (%)
-2949560 1
 
< 0.1%
-362150 1
 
< 0.1%
-258470 1
 
< 0.1%
-142030 1
 
< 0.1%
-4650 1
 
< 0.1%
-1976 1
 
< 0.1%
0 1023
10.2%
1 1
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%
ValueCountFrequency (%)
583921688 1
< 0.1%
341468332 1
< 0.1%
312322600 1
< 0.1%
280601040 1
< 0.1%
228214410 1
< 0.1%
182993400 1
< 0.1%
165411020 1
< 0.1%
163363380 1
< 0.1%
155620300 1
< 0.1%
155064000 1
< 0.1%

Interactions

2024-05-11T06:52:37.108107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T06:52:44.764761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비용명금액
비용명1.0000.264
금액0.2641.000

Missing values

2024-05-11T06:52:37.819426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T06:52:38.202632image/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
80294신구로자이A15205508제수당2022041634900
76102당산강변래미안3차A15081002수선유지비2022047417430
80523고척벽산베스트블루밍A15208006복리후생비202204787830
56895잠실우성4차A13822902위탁관리수수료202204405466
8692마곡13단지 힐스테이트마스터 아파트A10026879고용안정사업비용202204378890
50732서초대우아이빌A13707003승강기유지비202204440000
26572용마산금호어울림A13120701승강기유지비202204654500
47879월곡3SH-villA13613003급여2022046431200
20657응암푸르지오A12201103주차장수익202204815000
25446전농우성A13084803기타운영수익202204932000
아파트명아파트코드비용명년월일금액
20525녹번역센트레빌A12201005주차장수익2022041509730
50157정릉중앙하이츠A13684701고용안정사업수익202204216000
69930한일유앤아이A14272303수도광열비20220448650
31696방학우성2차A13282510청소비2022047485450
47993월곡래미안루나밸리A13613006세대수도료20220415321990
36703한신무학A13385705입주자대표회의운영비202204602800
13030쌍용남산플래티넘A10072501통신비202204124830
53113서초3차e편한세상A13786803지급수수료202204159840
42432개포우성3차A13524004광고료수익202204350000
35732서울숲푸르지오A13380803급여20220420817450