Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory410.2 KiB
Average record size in memory42.0 B

Variable types

Categorical3
Text1

Dataset

Description의료 환자와 의료기관의 거리과 소요시간을 측정하여 의료 취약지역 파악 및 국민건강 모니터링을 위한 의료이용지도 데이터 구축장애인의 건강검진 의료기관 이용에 대한 경로탐색 네트워크 분석으로 이용 접근 이동거리 측면의 취약지역을 도출
Author국토교통부
URLhttps://www.data.go.kr/data/15123155/fileData.do

Alerts

거리등급 is highly overall correlated with 시간등급High correlation
시간등급 is highly overall correlated with 거리등급High correlation
공간정보 has unique valuesUnique

Reproduction

Analysis started2023-12-12 06:00:25.763427
Analysis finished2023-12-12 06:00:26.208807
Duration0.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경상북도
1459 
경기도
1347 
경상남도
1113 
전라남도
976 
서울특별시
975 
Other values (12)
4130 

Length

Max length7
Median length4
Mean length4.1171
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전라북도
2nd row경상북도
3rd row경기도
4th row충청남도
5th row경기도

Common Values

ValueCountFrequency (%)
경상북도 1459
14.6%
경기도 1347
13.5%
경상남도 1113
11.1%
전라남도 976
9.8%
서울특별시 975
9.8%
충청남도 818
8.2%
전라북도 683
6.8%
강원도 545
 
5.5%
부산광역시 402
 
4.0%
대구광역시 346
 
3.5%
Other values (7) 1336
13.4%

Length

2023-12-12T15:00:26.288575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상북도 1459
14.6%
경기도 1347
13.5%
경상남도 1113
11.1%
전라남도 976
9.8%
서울특별시 975
9.8%
충청남도 818
8.2%
전라북도 683
6.8%
강원도 545
 
5.5%
부산광역시 402
 
4.0%
대구광역시 346
 
3.5%
Other values (7) 1336
13.4%

거리등급
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
5328 
2
2370 
3
1286 
4
703 
5
 
313

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 5328
53.3%
2 2370
23.7%
3 1286
 
12.9%
4 703
 
7.0%
5 313
 
3.1%

Length

2023-12-12T15:00:26.433754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:00:26.564643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5328
53.3%
2 2370
23.7%
3 1286
 
12.9%
4 703
 
7.0%
5 313
 
3.1%

시간등급
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
6121 
2
1686 
3
1362 
4
 
429
5
 
402

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 6121
61.2%
2 1686
 
16.9%
3 1362
 
13.6%
4 429
 
4.3%
5 402
 
4.0%

Length

2023-12-12T15:00:26.701815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:00:26.845325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 6121
61.2%
2 1686
 
16.9%
3 1362
 
13.6%
4 429
 
4.3%
5 402
 
4.0%

공간정보
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T15:00:27.163687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length41
Mean length40.7843
Min length37

Characters and Unicode

Total characters407843
Distinct characters19
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st rowPOINT (127.581267751558 35.4822190876502)
2nd rowPOINT (129.37672405009 36.0602918761132)
3rd rowPOINT (126.974649386003 37.2192900242374)
4th rowPOINT (127.112758004625 36.8993581863583)
5th rowPOINT (127.393092935622 37.0870866659503)
ValueCountFrequency (%)
point 10000
33.3%
37.538167334878 2
 
< 0.1%
37.5760655233525 2
 
< 0.1%
37.5553374958948 2
 
< 0.1%
37.7887281951442 2
 
< 0.1%
37.5787717884308 2
 
< 0.1%
37.5481479184461 2
 
< 0.1%
127.000567655545 2
 
< 0.1%
127.707479180632 2
 
< 0.1%
37.6189412631183 2
 
< 0.1%
Other values (19970) 19982
66.6%
2023-12-12T15:00:27.594960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 35072
 
8.6%
1 35020
 
8.6%
2 34362
 
8.4%
7 31191
 
7.6%
6 30466
 
7.5%
5 28714
 
7.0%
8 27714
 
6.8%
4 26380
 
6.5%
9 26082
 
6.4%
0 22842
 
5.6%
Other values (9) 110000
27.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 297843
73.0%
Uppercase Letter 50000
 
12.3%
Space Separator 20000
 
4.9%
Other Punctuation 20000
 
4.9%
Open Punctuation 10000
 
2.5%
Close Punctuation 10000
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 35072
11.8%
1 35020
11.8%
2 34362
11.5%
7 31191
10.5%
6 30466
10.2%
5 28714
9.6%
8 27714
9.3%
4 26380
8.9%
9 26082
8.8%
0 22842
7.7%
Uppercase Letter
ValueCountFrequency (%)
P 10000
20.0%
O 10000
20.0%
T 10000
20.0%
N 10000
20.0%
I 10000
20.0%
Space Separator
ValueCountFrequency (%)
20000
100.0%
Other Punctuation
ValueCountFrequency (%)
. 20000
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10000
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 357843
87.7%
Latin 50000
 
12.3%

Most frequent character per script

Common
ValueCountFrequency (%)
3 35072
9.8%
1 35020
9.8%
2 34362
9.6%
7 31191
8.7%
6 30466
8.5%
5 28714
8.0%
8 27714
7.7%
4 26380
7.4%
9 26082
7.3%
0 22842
 
6.4%
Other values (4) 60000
16.8%
Latin
ValueCountFrequency (%)
P 10000
20.0%
O 10000
20.0%
T 10000
20.0%
N 10000
20.0%
I 10000
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 407843
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 35072
 
8.6%
1 35020
 
8.6%
2 34362
 
8.4%
7 31191
 
7.6%
6 30466
 
7.5%
5 28714
 
7.0%
8 27714
 
6.8%
4 26380
 
6.5%
9 26082
 
6.4%
0 22842
 
5.6%
Other values (9) 110000
27.0%

Correlations

2023-12-12T15:00:27.745089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명거리등급시간등급
시도명1.0000.3760.337
거리등급0.3761.0000.978
시간등급0.3370.9781.000
2023-12-12T15:00:28.174748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시간등급거리등급시도명
시간등급1.0000.7880.181
거리등급0.7881.0000.204
시도명0.1810.2041.000
2023-12-12T15:00:28.289755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명거리등급시간등급
시도명1.0000.2040.181
거리등급0.2041.0000.788
시간등급0.1810.7881.000

Missing values

2023-12-12T15:00:26.080389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:00:26.168236image/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

시도명거리등급시간등급공간정보
80627전라북도33POINT (127.581267751558 35.4822190876502)
79977경상북도11POINT (129.37672405009 36.0602918761132)
74349경기도11POINT (126.974649386003 37.2192900242374)
60006충청남도11POINT (127.112758004625 36.8993581863583)
65314경기도22POINT (127.393092935622 37.0870866659503)
38310서울특별시44POINT (127.028880299548 37.610331497905)
4277경상북도33POINT (128.783235935859 36.8004355937245)
28922경상북도33POINT (127.959106847625 36.3900234999089)
22909전라남도22POINT (126.448358821891 35.1768105734032)
36661충청남도11POINT (127.083953845819 36.2045952660053)
시도명거리등급시간등급공간정보
83058경기도11POINT (127.5689455157 37.4395772523431)
82667경상북도33POINT (128.604749426527 35.6405689127932)
62988전라남도11POINT (127.634296113455 34.7924236603046)
74664강원도11POINT (127.940675880732 37.3471042827457)
84108경기도11POINT (126.78926440272 37.4371546727778)
61483부산광역시11POINT (128.973073469928 35.1277502882889)
95158부산광역시11POINT (129.109588269661 35.1470779092736)
51006전라북도11POINT (126.856506125469 35.9260608097194)
17606경상남도11POINT (128.5196052997 35.3406706197842)
29131전라남도11POINT (126.403887042033 34.8007292462516)