Overview

Dataset statistics

Number of variables5
Number of observations178
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 KiB
Average record size in memory42.7 B

Variable types

Numeric2
Text2
Categorical1

Dataset

Description한국주택금융공사 주택연금부 업무 관련 공개 데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터) 센터아이디,센터이름,지역구분코드,우편코드,주소 등의 정보가 포함되어있습니다.
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073070/fileData.do

Alerts

센터아이디 is highly overall correlated with 지역구분코드High correlation
우편코드 is highly overall correlated with 지역구분코드High correlation
지역구분코드 is highly overall correlated with 센터아이디 and 1 other fieldsHigh correlation
센터아이디 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:07:29.152609
Analysis finished2023-12-12 22:07:29.888156
Duration0.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

센터아이디
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct178
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141134.02
Minimum10001
Maximum280005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T07:07:29.965915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10001
5-th percentile10009.85
Q122507
median210010.5
Q3230004.75
95-th percentile270001.15
Maximum280005
Range270004
Interquartile range (IQR)207497.75

Descriptive statistics

Standard deviation103162.54
Coefficient of variation (CV)0.73095448
Kurtosis-1.8017557
Mean141134.02
Median Absolute Deviation (MAD)54992.5
Skewness-0.20434101
Sum25121855
Variance1.064251 × 1010
MonotonicityNot monotonic
2023-12-13T07:07:30.092665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
210028 1
 
0.6%
220010 1
 
0.6%
240005 1
 
0.6%
240004 1
 
0.6%
240003 1
 
0.6%
240002 1
 
0.6%
240001 1
 
0.6%
230010 1
 
0.6%
230009 1
 
0.6%
230008 1
 
0.6%
Other values (168) 168
94.4%
ValueCountFrequency (%)
10001 1
0.6%
10002 1
0.6%
10003 1
0.6%
10004 1
0.6%
10005 1
0.6%
10006 1
0.6%
10007 1
0.6%
10008 1
0.6%
10009 1
0.6%
10010 1
0.6%
ValueCountFrequency (%)
280005 1
0.6%
280004 1
0.6%
280003 1
0.6%
280002 1
0.6%
280001 1
0.6%
270005 1
0.6%
270004 1
0.6%
270003 1
0.6%
270002 1
0.6%
270001 1
0.6%

센터이름
Text

Distinct176
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-13T07:07:30.309991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length9.5449438
Min length7

Characters and Unicode

Total characters1699
Distinct characters149
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique174 ?
Unique (%)97.8%

Sample

1st row의왕시 노인복지관 아름채
2nd row평택남부노인복지관
3rd row광주시노인종합복지관
4th row김포시노인종합복지관
5th row수원시립버드내노인복지관
ValueCountFrequency (%)
남구노인복지관 2
 
1.1%
동구노인복지관 2
 
1.1%
노인복지관 2
 
1.1%
시립 2
 
1.1%
의왕시 2
 
1.1%
동구노인종합복지관 2
 
1.1%
서원노인복지관 1
 
0.5%
아름채 1
 
0.5%
당진군남부노인복지관 1
 
0.5%
정읍시노인종합복지관 1
 
0.5%
Other values (169) 169
91.4%
2023-12-13T07:07:30.676157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
인 180
 
10.6%
지 177
 
10.4%
복 176
 
10.4%
노 175
 
10.3%
관 170
 
10.0%
종 79
 
4.6%
합 78
 
4.6%
시 65
 
3.8%
구 49
 
2.9%
립 32
 
1.9%
Other values (139) 518
30.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1667
98.1%
Space Separator 32
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 180
 
10.8%
지 177
 
10.6%
복 176
 
10.6%
노 175
 
10.5%
관 170
 
10.2%
종 79
 
4.7%
합 78
 
4.7%
시 65
 
3.9%
구 49
 
2.9%
립 32
 
1.9%
Other values (138) 486
29.2%
Space Separator
ValueCountFrequency (%)
32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1667
98.1%
Common 32
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 180
 
10.8%
지 177
 
10.6%
복 176
 
10.6%
노 175
 
10.5%
관 170
 
10.2%
종 79
 
4.7%
합 78
 
4.7%
시 65
 
3.9%
구 49
 
2.9%
립 32
 
1.9%
Other values (138) 486
29.2%
Common
ValueCountFrequency (%)
32
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1667
98.1%
ASCII 32
 
1.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
인 180
 
10.8%
지 177
 
10.6%
복 176
 
10.6%
노 175
 
10.5%
관 170
 
10.2%
종 79
 
4.7%
합 78
 
4.7%
시 65
 
3.9%
구 49
 
2.9%
립 32
 
1.9%
Other values (138) 486
29.2%
ASCII
ValueCountFrequency (%)
32
100.0%

지역구분코드
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
서울
36 
경기
35 
인천
22 
충북
15 
울산
12 
Other values (8)
58 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 36
20.2%
경기 35
19.7%
인천 22
12.4%
충북 15
8.4%
울산 12
 
6.7%
전남 11
 
6.2%
충남 10
 
5.6%
부산 9
 
5.1%
광주 7
 
3.9%
대전 6
 
3.4%
Other values (3) 15
8.4%

Length

2023-12-13T07:07:30.827955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 36
20.2%
경기 35
19.7%
인천 22
12.4%
충북 15
8.4%
울산 12
 
6.7%
전남 11
 
6.2%
충남 10
 
5.6%
부산 9
 
5.1%
광주 7
 
3.9%
대전 6
 
3.4%
Other values (3) 15
8.4%

우편코드
Real number (ℝ)

HIGH CORRELATION 

Distinct176
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean413043.21
Minimum100453
Maximum769801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T07:07:30.961457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100453
5-th percentile131716.5
Q1303597
median426333.5
Q3554557.5
95-th percentile702257
Maximum769801
Range669348
Interquartile range (IQR)250960.5

Descriptive statistics

Standard deviation181347.97
Coefficient of variation (CV)0.43905326
Kurtosis-0.90502596
Mean413043.21
Median Absolute Deviation (MAD)124501
Skewness-0.16363428
Sum73521692
Variance3.2887086 × 1010
MonotonicityNot monotonic
2023-12-13T07:07:31.132610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
576140 2
 
1.1%
336012 2
 
1.1%
437800 1
 
0.6%
320808 1
 
0.6%
580190 1
 
0.6%
560838 1
 
0.6%
570090 1
 
0.6%
561831 1
 
0.6%
325871 1
 
0.6%
321900 1
 
0.6%
Other values (166) 166
93.3%
ValueCountFrequency (%)
100453 1
0.6%
110310 1
0.6%
110500 1
0.6%
120040 1
0.6%
121881 1
0.6%
122901 1
0.6%
122945 1
0.6%
130866 1
0.6%
131130 1
0.6%
131820 1
0.6%
ValueCountFrequency (%)
769801 1
0.6%
757805 1
0.6%
740973 1
0.6%
718805 1
0.6%
712100 1
0.6%
711843 1
0.6%
706850 1
0.6%
705803 1
0.6%
704807 1
0.6%
701807 1
0.6%

주소
Text

UNIQUE 

Distinct178
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-13T07:07:31.433672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length27
Mean length19.769663
Min length13

Characters and Unicode

Total characters3519
Distinct characters208
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique178 ?
Unique (%)100.0%

Sample

1st row경기도 의왕시 문화공원길 6 (고천동 100번지)
2nd row경기도 평택시 비전동 631번지
3rd row경기도 광주시 탄벌동 18-1
4th row경기도 김포시 사우동 865번지
5th row경기도 수원시 권선구 세류3동 483
ValueCountFrequency (%)
서울특별시 36
 
4.6%
경기도 34
 
4.3%
충북 15
 
1.9%
전북 12
 
1.5%
전남 10
 
1.3%
충남 10
 
1.3%
부산광역시 9
 
1.1%
인천광역시 9
 
1.1%
남구 8
 
1.0%
동구 7
 
0.9%
Other values (545) 637
80.9%
2023-12-13T07:07:31.881560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
637
 
18.1%
1 165
 
4.7%
동 162
 
4.6%
시 155
 
4.4%
- 120
 
3.4%
구 116
 
3.3%
2 92
 
2.6%
3 77
 
2.2%
6 72
 
2.0%
4 72
 
2.0%
Other values (198) 1851
52.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2017
57.3%
Decimal Number 731
 
20.8%
Space Separator 637
 
18.1%
Dash Punctuation 120
 
3.4%
Close Punctuation 6
 
0.2%
Open Punctuation 6
 
0.2%
Uppercase Letter 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 162
 
8.0%
시 155
 
7.7%
구 116
 
5.8%
광 56
 
2.8%
남 56
 
2.8%
서 51
 
2.5%
지 50
 
2.5%
산 49
 
2.4%
경 48
 
2.4%
도 47
 
2.3%
Other values (182) 1227
60.8%
Decimal Number
ValueCountFrequency (%)
1 165
22.6%
2 92
12.6%
3 77
10.5%
6 72
9.8%
4 72
9.8%
5 68
9.3%
8 59
 
8.1%
7 48
 
6.6%
0 44
 
6.0%
9 34
 
4.7%
Space Separator
ValueCountFrequency (%)
637
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 120
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Uppercase Letter
ValueCountFrequency (%)
E 1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2017
57.3%
Common 1501
42.7%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 162
 
8.0%
시 155
 
7.7%
구 116
 
5.8%
광 56
 
2.8%
남 56
 
2.8%
서 51
 
2.5%
지 50
 
2.5%
산 49
 
2.4%
경 48
 
2.4%
도 47
 
2.3%
Other values (182) 1227
60.8%
Common
ValueCountFrequency (%)
637
42.4%
1 165
 
11.0%
- 120
 
8.0%
2 92
 
6.1%
3 77
 
5.1%
6 72
 
4.8%
4 72
 
4.8%
5 68
 
4.5%
8 59
 
3.9%
7 48
 
3.2%
Other values (5) 91
 
6.1%
Latin
ValueCountFrequency (%)
E 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2017
57.3%
ASCII 1502
42.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
637
42.4%
1 165
 
11.0%
- 120
 
8.0%
2 92
 
6.1%
3 77
 
5.1%
6 72
 
4.8%
4 72
 
4.8%
5 68
 
4.5%
8 59
 
3.9%
7 48
 
3.2%
Other values (6) 92
 
6.1%
Hangul
ValueCountFrequency (%)
동 162
 
8.0%
시 155
 
7.7%
구 116
 
5.8%
광 56
 
2.8%
남 56
 
2.8%
서 51
 
2.5%
지 50
 
2.5%
산 49
 
2.4%
경 48
 
2.4%
도 47
 
2.3%
Other values (182) 1227
60.8%

Interactions

2023-12-13T07:07:29.560507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:07:29.389590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:07:29.636760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:07:29.477401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:07:31.964951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
센터아이디지역구분코드우편코드
센터아이디1.0000.9630.878
지역구분코드0.9631.0000.947
우편코드0.8780.9471.000
2023-12-13T07:07:32.046998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
센터아이디우편코드지역구분코드
센터아이디1.0000.4570.867
우편코드0.4571.0000.792
지역구분코드0.8670.7921.000

Missing values

2023-12-13T07:07:29.760920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:07:29.849460image/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

센터아이디센터이름지역구분코드우편코드주소
0210028의왕시 노인복지관 아름채경기437800경기도 의왕시 문화공원길 6 (고천동 100번지)
1210027평택남부노인복지관경기450150경기도 평택시 비전동 631번지
2210026광주시노인종합복지관경기464100경기도 광주시 탄벌동 18-1
3210025김포시노인종합복지관경기415728경기도 김포시 사우동 865번지
4210024수원시립버드내노인복지관경기441870경기도 수원시 권선구 세류3동 483
5210023남양주시노인복지관경기472835경기도 남양주시 진건읍 송능리 109-1
6210022남양주시동부노인복지관경기472852경기도 남양주시 수동면 운수리 361
7210021여주군노인복지관경기469804경기도 여주군 여주읍 상리 351-4
8210020송산노인복지관경기480868경기도 의정부시 민락동 736-2
9210019파주시노인복지관경기413030경기도 파주시 금릉동 230번지
센터아이디센터이름지역구분코드우편코드주소
16820002실버벨노인종합복지관부산616809부산광역시 북구 구포3동 1255-2
16920001부산광역시노인종합복지관부산611820부산광역시 연제구 연산4동 578-3
170210035화성시남부노인복지관경기445926경기도 화성시 향남읍 행정리 산 11번지
171210034광명시노인종합복지관경기447854경기도광명시소하1동 택지개발지구 내E-1블럭
172210033팽성노인복지관경기451803경기도 평택시 팽성읍 남산리 406-16번지
173210032단원구노인복지관경기425830경기도 안산시 단원구 선부동 1077-9번지
174210031평택북부노인복지관경기459812경기도 평택시 서정동 342번지
175210030분당노인종합복지관경기463815경기도 성남시 분당구 정자동 253번지
176210029중원노인종합복지관경기437801경기도 성남시 중원구 성남동 3440번지
17750007빛고을노인건강타운광주503320광주광역시 남구 노대동 592번지