Overview

Dataset statistics

Number of variables2
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.7 KiB
Average record size in memory17.1 B

Variable types

Numeric1
Text1

Dataset

Description한국주택금융공사 채권관리부 업무 관련 공개 공공데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073180/fileData.do

Alerts

ADMIN_ORG_CD has unique valuesUnique
ADMIN_ORG_NM has unique valuesUnique

Reproduction

Analysis started2023-12-12 06:38:05.991387
Analysis finished2023-12-12 06:38:06.637083
Duration0.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ADMIN_ORG_CD
Real number (ℝ)

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7794241 × 109
Minimum4.678031 × 109
Maximum4.972032 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T15:38:06.728152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.678031 × 109
5-th percentile4.68104 × 109
Q14.7150318 × 109
median4.779519 × 109
Q34.8250582 × 109
95-th percentile4.911053 × 109
Maximum4.972032 × 109
Range2.94001 × 108
Interquartile range (IQR)1.100265 × 108

Descriptive statistics

Standard deviation76379848
Coefficient of variation (CV)0.015980973
Kurtosis-0.8281328
Mean4.7794241 × 109
Median Absolute Deviation (MAD)62451500
Skewness0.42969086
Sum4.7794241 × 1012
Variance5.8338812 × 1015
MonotonicityStrictly decreasing
2023-12-12T15:38:06.877959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4972032000 1
 
0.1%
4719062200 1
 
0.1%
4721034000 1
 
0.1%
4721033000 1
 
0.1%
4721032000 1
 
0.1%
4721031000 1
 
0.1%
4721025000 1
 
0.1%
4721000000 1
 
0.1%
4719069000 1
 
0.1%
4719068000 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
4678031000 1
0.1%
4678032000 1
0.1%
4678033000 1
0.1%
4678034000 1
0.1%
4678035000 1
0.1%
4678036000 1
0.1%
4678037000 1
0.1%
4678038000 1
0.1%
4678038500 1
0.1%
4678039000 1
0.1%
ValueCountFrequency (%)
4972032000 1
0.1%
4972031000 1
0.1%
4972013500 1
0.1%
4972013000 1
0.1%
4972012500 1
0.1%
4972012000 1
0.1%
4972011500 1
0.1%
4972011000 1
0.1%
4972000000 1
0.1%
4971033000 1
0.1%

ADMIN_ORG_NM
Text

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-12-12T15:38:07.156200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length12
Mean length12.281
Min length3

Characters and Unicode

Total characters12281
Distinct characters242
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st row제주도 남제주군 표선면
2nd row제주도 남제주군 안덕면
3rd row제주도 남제주군 성산읍신산출장소
4th row제주도 남제주군 성산읍
5th row제주도 남제주군 남원읍위미출장소
ValueCountFrequency (%)
경상북도 409
 
13.9%
경상남도 363
 
12.4%
전라남도 173
 
5.9%
제주도 55
 
1.9%
진주시 38
 
1.3%
마산시 36
 
1.2%
안동시 31
 
1.1%
구미시 31
 
1.1%
상주시 30
 
1.0%
경주시 30
 
1.0%
Other values (935) 1736
59.2%
2023-12-12T15:38:07.665285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1972
16.1%
도 1093
 
8.9%
경 843
 
6.9%
상 829
 
6.8%
남 639
 
5.2%
면 554
 
4.5%
시 523
 
4.3%
군 494
 
4.0%
북 487
 
4.0%
동 387
 
3.2%
Other values (232) 4460
36.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10250
83.5%
Space Separator 1972
 
16.1%
Decimal Number 59
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 1093
 
10.7%
경 843
 
8.2%
상 829
 
8.1%
남 639
 
6.2%
면 554
 
5.4%
시 523
 
5.1%
군 494
 
4.8%
북 487
 
4.8%
동 387
 
3.8%
주 243
 
2.4%
Other values (227) 4158
40.6%
Decimal Number
ValueCountFrequency (%)
2 27
45.8%
1 27
45.8%
3 4
 
6.8%
4 1
 
1.7%
Space Separator
ValueCountFrequency (%)
1972
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10250
83.5%
Common 2031
 
16.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 1093
 
10.7%
경 843
 
8.2%
상 829
 
8.1%
남 639
 
6.2%
면 554
 
5.4%
시 523
 
5.1%
군 494
 
4.8%
북 487
 
4.8%
동 387
 
3.8%
주 243
 
2.4%
Other values (227) 4158
40.6%
Common
ValueCountFrequency (%)
1972
97.1%
2 27
 
1.3%
1 27
 
1.3%
3 4
 
0.2%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10250
83.5%
ASCII 2031
 
16.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1972
97.1%
2 27
 
1.3%
1 27
 
1.3%
3 4
 
0.2%
4 1
 
< 0.1%
Hangul
ValueCountFrequency (%)
도 1093
 
10.7%
경 843
 
8.2%
상 829
 
8.1%
남 639
 
6.2%
면 554
 
5.4%
시 523
 
5.1%
군 494
 
4.8%
북 487
 
4.8%
동 387
 
3.8%
주 243
 
2.4%
Other values (227) 4158
40.6%

Interactions

2023-12-12T15:38:06.132466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-12-12T15:38:06.535296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:38:06.602648image/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

ADMIN_ORG_CDADMIN_ORG_NM
04972032000제주도 남제주군 표선면
14972031000제주도 남제주군 안덕면
24972013500제주도 남제주군 성산읍신산출장소
34972013000제주도 남제주군 성산읍
44972012500제주도 남제주군 남원읍위미출장소
54972012000제주도 남제주군 남원읍
64972011500제주도 남제주군 대정읍무릉출장소
74972011000제주도 남제주군 대정읍
84972000000제주도 남제주군
94971033000제주도 북제주군 우도면
ADMIN_ORG_CDADMIN_ORG_NM
9904678039000전라남도 보성군 회천면
9914678038500전라남도 보성군 득량면예당출장소
9924678038000전라남도 보성군 득량면
9934678037000전라남도 보성군 조성면
9944678036000전라남도 보성군 문덕면
9954678035000전라남도 보성군 복내면
9964678034000전라남도 보성군 율어면
9974678033000전라남도 보성군 겸백면
9984678032000전라남도 보성군 미력면
9994678031000전라남도 보성군 노동면