Overview

Dataset statistics

Number of variables4
Number of observations48
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory34.8 B

Variable types

Text3
Categorical1

Dataset

Description경상남도 밀양시의 관내 의원 현황입니다.
Author경상남도 밀양시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3079246

Alerts

병원 has constant value ""Constant
이름 has unique valuesUnique

Reproduction

Analysis started2024-04-17 11:03:32.190282
Analysis finished2024-04-17 11:03:32.520033
Duration0.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

이름
Text

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size516.0 B
2024-04-17T20:03:32.668455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length8.5
Mean length7.3125
Min length3

Characters and Unicode

Total characters351
Distinct characters102
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)100.0%

Sample

1st row금송의원
2nd row송광호의원
3rd row오용창 내과의원
4th row신통의원
5th row인성의원
ValueCountFrequency (%)
의원 4
 
7.1%
내과의원 2
 
3.6%
금송의원 1
 
1.8%
밀양선비뇨기과의원 1
 
1.8%
서울마취통증의학과의 1
 
1.8%
고운의원 1
 
1.8%
바로곧은의원 1
 
1.8%
밀양삼성안과의원 1
 
1.8%
신일외과의원 1
 
1.8%
우리이비인후과의원 1
 
1.8%
Other values (42) 42
75.0%
2024-04-17T20:03:32.972670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
의 49
 
14.0%
원 42
 
12.0%
과 31
 
8.8%
정 9
 
2.6%
양 8
 
2.3%
8
 
2.3%
밀 8
 
2.3%
안 7
 
2.0%
내 7
 
2.0%
외 6
 
1.7%
Other values (92) 176
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 331
94.3%
Space Separator 8
 
2.3%
Decimal Number 6
 
1.7%
Open Punctuation 3
 
0.9%
Close Punctuation 3
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 49
 
14.8%
원 42
 
12.7%
과 31
 
9.4%
정 9
 
2.7%
양 8
 
2.4%
밀 8
 
2.4%
안 7
 
2.1%
내 7
 
2.1%
외 6
 
1.8%
아 5
 
1.5%
Other values (87) 159
48.0%
Decimal Number
ValueCountFrequency (%)
2 5
83.3%
9 1
 
16.7%
Space Separator
ValueCountFrequency (%)
8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 331
94.3%
Common 20
 
5.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 49
 
14.8%
원 42
 
12.7%
과 31
 
9.4%
정 9
 
2.7%
양 8
 
2.4%
밀 8
 
2.4%
안 7
 
2.1%
내 7
 
2.1%
외 6
 
1.8%
아 5
 
1.5%
Other values (87) 159
48.0%
Common
ValueCountFrequency (%)
8
40.0%
2 5
25.0%
( 3
 
15.0%
) 3
 
15.0%
9 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 331
94.3%
ASCII 20
 
5.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
의 49
 
14.8%
원 42
 
12.7%
과 31
 
9.4%
정 9
 
2.7%
양 8
 
2.4%
밀 8
 
2.4%
안 7
 
2.1%
내 7
 
2.1%
외 6
 
1.8%
아 5
 
1.5%
Other values (87) 159
48.0%
ASCII
ValueCountFrequency (%)
8
40.0%
2 5
25.0%
( 3
 
15.0%
) 3
 
15.0%
9 1
 
5.0%

병원
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size516.0 B
의원
48 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
의원 48
100.0%

Length

2024-04-17T20:03:33.089899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T20:03:33.163111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의원 48
100.0%

주소
Text

Distinct44
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size516.0 B
2024-04-17T20:03:33.331731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22
Mean length17.083333
Min length9

Characters and Unicode

Total characters820
Distinct characters71
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)85.4%

Sample

1st row밀양시 삼랑진읍 천태로 50
2nd row밀양시 삼랑진읍 천태로 14-1
3rd row밀양시 삼랑진읍 천태로 14-1
4th row삼랑진 천태로 75
5th row밀양시 하남읍 수산중앙로 21
ValueCountFrequency (%)
밀양시 45
25.9%
중앙로 10
 
5.7%
삼문중앙로 7
 
4.0%
북성로 7
 
4.0%
하남읍 5
 
2.9%
수산중앙로 4
 
2.3%
천태로 4
 
2.3%
삼문동 3
 
1.7%
49 3
 
1.7%
2층 3
 
1.7%
Other values (67) 83
47.7%
2024-04-17T20:03:33.652120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
126
 
15.4%
밀 46
 
5.6%
시 46
 
5.6%
양 45
 
5.5%
1 41
 
5.0%
로 40
 
4.9%
동 30
 
3.7%
) 29
 
3.5%
( 29
 
3.5%
4 27
 
3.3%
Other values (61) 361
44.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 467
57.0%
Decimal Number 149
 
18.2%
Space Separator 126
 
15.4%
Close Punctuation 29
 
3.5%
Open Punctuation 29
 
3.5%
Other Punctuation 11
 
1.3%
Dash Punctuation 9
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
밀 46
 
9.9%
시 46
 
9.9%
양 45
 
9.6%
로 40
 
8.6%
동 30
 
6.4%
삼 24
 
5.1%
앙 21
 
4.5%
중 21
 
4.5%
문 20
 
4.3%
내 20
 
4.3%
Other values (46) 154
33.0%
Decimal Number
ValueCountFrequency (%)
1 41
27.5%
4 27
18.1%
2 22
14.8%
3 17
11.4%
6 10
 
6.7%
0 8
 
5.4%
5 7
 
4.7%
9 6
 
4.0%
7 6
 
4.0%
8 5
 
3.4%
Space Separator
ValueCountFrequency (%)
126
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Other Punctuation
ValueCountFrequency (%)
, 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 467
57.0%
Common 353
43.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
밀 46
 
9.9%
시 46
 
9.9%
양 45
 
9.6%
로 40
 
8.6%
동 30
 
6.4%
삼 24
 
5.1%
앙 21
 
4.5%
중 21
 
4.5%
문 20
 
4.3%
내 20
 
4.3%
Other values (46) 154
33.0%
Common
ValueCountFrequency (%)
126
35.7%
1 41
 
11.6%
) 29
 
8.2%
( 29
 
8.2%
4 27
 
7.6%
2 22
 
6.2%
3 17
 
4.8%
, 11
 
3.1%
6 10
 
2.8%
- 9
 
2.5%
Other values (5) 32
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 467
57.0%
ASCII 353
43.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
126
35.7%
1 41
 
11.6%
) 29
 
8.2%
( 29
 
8.2%
4 27
 
7.6%
2 22
 
6.2%
3 17
 
4.8%
, 11
 
3.1%
6 10
 
2.8%
- 9
 
2.5%
Other values (5) 32
 
9.1%
Hangul
ValueCountFrequency (%)
밀 46
 
9.9%
시 46
 
9.9%
양 45
 
9.6%
로 40
 
8.6%
동 30
 
6.4%
삼 24
 
5.1%
앙 21
 
4.5%
중 21
 
4.5%
문 20
 
4.3%
내 20
 
4.3%
Other values (46) 154
33.0%

전화번호
Text

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size516.0 B
2024-04-17T20:03:33.856031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters576
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)95.8%

Sample

1st row055-351-1370
2nd row055-351-0367
3rd row055-353-6933
4th row055-351-5005
5th row055-391-2288
ValueCountFrequency (%)
055-352-5965 2
 
4.2%
055-356-8075 1
 
2.1%
055-356-0775 1
 
2.1%
055-355-7588 1
 
2.1%
055-354-0202 1
 
2.1%
055-355-3877 1
 
2.1%
055-354-6433 1
 
2.1%
055-353-5354 1
 
2.1%
055-351-0094 1
 
2.1%
055-351-2027 1
 
2.1%
Other values (37) 37
77.1%
2024-04-17T20:03:34.158186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 179
31.1%
- 96
16.7%
0 86
14.9%
3 70
 
12.2%
7 29
 
5.0%
6 24
 
4.2%
1 24
 
4.2%
2 21
 
3.6%
9 19
 
3.3%
4 14
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 480
83.3%
Dash Punctuation 96
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 179
37.3%
0 86
17.9%
3 70
 
14.6%
7 29
 
6.0%
6 24
 
5.0%
1 24
 
5.0%
2 21
 
4.4%
9 19
 
4.0%
4 14
 
2.9%
8 14
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 96
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 576
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 179
31.1%
- 96
16.7%
0 86
14.9%
3 70
 
12.2%
7 29
 
5.0%
6 24
 
4.2%
1 24
 
4.2%
2 21
 
3.6%
9 19
 
3.3%
4 14
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 179
31.1%
- 96
16.7%
0 86
14.9%
3 70
 
12.2%
7 29
 
5.0%
6 24
 
4.2%
1 24
 
4.2%
2 21
 
3.6%
9 19
 
3.3%
4 14
 
2.4%

Correlations

2024-04-17T20:03:34.246645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이름주소전화번호
이름1.0001.0001.000
주소1.0001.0000.986
전화번호1.0000.9861.000

Missing values

2024-04-17T20:03:32.417777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T20:03:32.486567image/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

이름병원주소전화번호
0금송의원의원밀양시 삼랑진읍 천태로 50055-351-1370
1송광호의원의원밀양시 삼랑진읍 천태로 14-1055-351-0367
2오용창 내과의원의원밀양시 삼랑진읍 천태로 14-1055-353-6933
3신통의원의원삼랑진 천태로 75055-351-5005
4인성의원의원밀양시 하남읍 수산중앙로 21055-391-2288
5하준호 내과의원의원밀양시 하남읍 수산중앙로 16055-391-6991
6승진의원의원밀양시 하남읍 시서안길 34055-391-3131
7수산의원의원밀양시 하남읍 수산중앙로 7055-391-0237
8성모신경외과의원의원밀양시 하남읍 수산중앙로 11055-391-0777
9밀양구치소 부설의원의원밀양시 부북면 춘화로 124055-350-7705
이름병원주소전화번호
38밀양삼성정형외과(22병상)의원삼문동 165번지 6호 (1층,3층)055-353-7582
39정소아과 의원의원밀양시 삼문동 165번지 6호(2층)055-356-5758
40안상준내과의원의원밀양시 삼문중앙로 49, 4층(삼문동)055-352-5965
41아름다운자모산부인과의원밀양시 삼문중앙로 14(삼문동)055-355-0348
42하나정형외과의원(22병상)의원밀양시 삼문중앙로 14(삼문동)055-352-7075
43미즈산부인과의원의원밀양시 삼문중앙로 49, 2층(삼문동)055-356-0025
44엔젤소아과의원의원밀양시 삼문중앙로 49, 2층 201호(삼문동)055-355-5900
45밀양이빈웰빙미의원의원밀양시 삼문강변로 344-6(삼문동)055-356-7017
46세광가정의학과 의원의원밀양시 가곡7길 3(가곡동)055-355-7571
47김보영안과의원의원삼문동 171번지 14호 세종메디칼빌딩055-352-5965