Overview

Dataset statistics

Number of variables5
Number of observations47
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory43.8 B

Variable types

Numeric1
Text3
Categorical1

Dataset

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

Alerts

의원 has constant value ""Constant
1 has unique valuesUnique
금송의원 has unique valuesUnique

Reproduction

Analysis started2023-12-10 22:51:54.714341
Analysis finished2023-12-10 22:51:55.181556
Duration0.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

1
Real number (ℝ)

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25
Minimum2
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-11T07:51:55.249175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.3
Q113.5
median25
Q336.5
95-th percentile45.7
Maximum48
Range46
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.711309
Coefficient of variation (CV)0.54845237
Kurtosis-1.2
Mean25
Median Absolute Deviation (MAD)12
Skewness0
Sum1175
Variance188
MonotonicityStrictly increasing
2023-12-11T07:51:55.383635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
2 1
 
2.1%
3 1
 
2.1%
28 1
 
2.1%
29 1
 
2.1%
30 1
 
2.1%
31 1
 
2.1%
32 1
 
2.1%
33 1
 
2.1%
34 1
 
2.1%
35 1
 
2.1%
Other values (37) 37
78.7%
ValueCountFrequency (%)
2 1
2.1%
3 1
2.1%
4 1
2.1%
5 1
2.1%
6 1
2.1%
7 1
2.1%
8 1
2.1%
9 1
2.1%
10 1
2.1%
11 1
2.1%
ValueCountFrequency (%)
48 1
2.1%
47 1
2.1%
46 1
2.1%
45 1
2.1%
44 1
2.1%
43 1
2.1%
42 1
2.1%
41 1
2.1%
40 1
2.1%
39 1
2.1%

금송의원
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-11T07:51:55.579052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length8
Mean length7.3829787
Min length3

Characters and Unicode

Total characters347
Distinct characters101
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

Unique47 ?
Unique (%)100.0%

Sample

1st row송광호의원
2nd row오용창 내과의원
3rd row신통의원
4th row인성의원
5th row하준호 내과의원
ValueCountFrequency (%)
의원 4
 
7.3%
내과의원 2
 
3.6%
송광호의원 1
 
1.8%
고운의원 1
 
1.8%
연세이비인후과의원 1
 
1.8%
밀양삼성안과의원 1
 
1.8%
신일외과의원 1
 
1.8%
우리이비인후과의원 1
 
1.8%
김내과의원 1
 
1.8%
서울피부과의원 1
 
1.8%
Other values (41) 41
74.5%
2023-12-11T07:51:55.896544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
의 48
 
13.8%
원 41
 
11.8%
과 31
 
8.9%
정 9
 
2.6%
8
 
2.3%
밀 8
 
2.3%
양 8
 
2.3%
내 7
 
2.0%
안 7
 
2.0%
외 6
 
1.7%
Other values (91) 174
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 327
94.2%
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 48
 
14.7%
원 41
 
12.5%
과 31
 
9.5%
정 9
 
2.8%
밀 8
 
2.4%
양 8
 
2.4%
내 7
 
2.1%
안 7
 
2.1%
외 6
 
1.8%
이 5
 
1.5%
Other values (86) 157
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 327
94.2%
Common 20
 
5.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 48
 
14.7%
원 41
 
12.5%
과 31
 
9.5%
정 9
 
2.8%
밀 8
 
2.4%
양 8
 
2.4%
내 7
 
2.1%
안 7
 
2.1%
외 6
 
1.8%
이 5
 
1.5%
Other values (86) 157
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 327
94.2%
ASCII 20
 
5.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
의 48
 
14.7%
원 41
 
12.5%
과 31
 
9.5%
정 9
 
2.8%
밀 8
 
2.4%
양 8
 
2.4%
내 7
 
2.1%
안 7
 
2.1%
외 6
 
1.8%
이 5
 
1.5%
Other values (86) 157
48.0%
ASCII
ValueCountFrequency (%)
8
40.0%
2 5
25.0%
( 3
 
15.0%
) 3
 
15.0%
9 1
 
5.0%

의원
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size508.0 B
의원
47 

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 47
100.0%

Length

2023-12-11T07:51:56.016361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:51:56.100515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의원 47
100.0%

밀양시 삼랑진읍 천태로 50
Text

Distinct43
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-11T07:51:56.284143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22
Mean length17.12766
Min length9

Characters and Unicode

Total characters805
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

Unique40 ?
Unique (%)85.1%

Sample

1st row밀양시 삼랑진읍 천태로 14-1
2nd row밀양시 삼랑진읍 천태로 14-1
3rd row삼랑진 천태로 75
4th row밀양시 하남읍 수산중앙로 21
5th row밀양시 하남읍 수산중앙로 16
ValueCountFrequency (%)
밀양시 44
25.9%
중앙로 10
 
5.9%
삼문중앙로 7
 
4.1%
북성로 7
 
4.1%
하남읍 5
 
2.9%
수산중앙로 4
 
2.4%
14(삼문동 3
 
1.8%
천태로 3
 
1.8%
49 3
 
1.8%
2층 3
 
1.8%
Other values (66) 81
47.6%
2023-12-11T07:51:56.665658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
123
 
15.3%
밀 45
 
5.6%
시 45
 
5.6%
양 44
 
5.5%
1 41
 
5.1%
로 39
 
4.8%
동 30
 
3.7%
( 29
 
3.6%
) 29
 
3.6%
4 27
 
3.4%
Other values (61) 353
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 457
56.8%
Decimal Number 147
 
18.3%
Space Separator 123
 
15.3%
Open Punctuation 29
 
3.6%
Close Punctuation 29
 
3.6%
Other Punctuation 11
 
1.4%
Dash Punctuation 9
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
밀 45
 
9.8%
시 45
 
9.8%
양 44
 
9.6%
로 39
 
8.5%
동 30
 
6.6%
삼 23
 
5.0%
중 21
 
4.6%
앙 21
 
4.6%
문 20
 
4.4%
내 20
 
4.4%
Other values (46) 149
32.6%
Decimal Number
ValueCountFrequency (%)
1 41
27.9%
4 27
18.4%
2 22
15.0%
3 17
11.6%
6 10
 
6.8%
0 7
 
4.8%
5 6
 
4.1%
7 6
 
4.1%
9 6
 
4.1%
8 5
 
3.4%
Space Separator
ValueCountFrequency (%)
123
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Other Punctuation
ValueCountFrequency (%)
, 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 457
56.8%
Common 348
43.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
밀 45
 
9.8%
시 45
 
9.8%
양 44
 
9.6%
로 39
 
8.5%
동 30
 
6.6%
삼 23
 
5.0%
중 21
 
4.6%
앙 21
 
4.6%
문 20
 
4.4%
내 20
 
4.4%
Other values (46) 149
32.6%
Common
ValueCountFrequency (%)
123
35.3%
1 41
 
11.8%
( 29
 
8.3%
) 29
 
8.3%
4 27
 
7.8%
2 22
 
6.3%
3 17
 
4.9%
, 11
 
3.2%
6 10
 
2.9%
- 9
 
2.6%
Other values (5) 30
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 457
56.8%
ASCII 348
43.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
123
35.3%
1 41
 
11.8%
( 29
 
8.3%
) 29
 
8.3%
4 27
 
7.8%
2 22
 
6.3%
3 17
 
4.9%
, 11
 
3.2%
6 10
 
2.9%
- 9
 
2.6%
Other values (5) 30
 
8.6%
Hangul
ValueCountFrequency (%)
밀 45
 
9.8%
시 45
 
9.8%
양 44
 
9.6%
로 39
 
8.5%
동 30
 
6.6%
삼 23
 
5.0%
중 21
 
4.6%
앙 21
 
4.6%
문 20
 
4.4%
내 20
 
4.4%
Other values (46) 149
32.6%

351-1370
Text

Distinct46
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-11T07:51:56.858224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters376
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

Unique45 ?
Unique (%)95.7%

Sample

1st row351-0367
2nd row353-6933
3rd row351-5005
4th row391-2288
5th row391-6991
ValueCountFrequency (%)
352-5965 2
 
4.3%
355-8600 1
 
2.1%
356-7017 1
 
2.1%
356-0775 1
 
2.1%
355-7588 1
 
2.1%
354-0202 1
 
2.1%
355-3877 1
 
2.1%
354-6433 1
 
2.1%
353-5354 1
 
2.1%
351-0094 1
 
2.1%
Other values (36) 36
76.6%
2023-12-11T07:51:57.171504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 82
21.8%
3 68
18.1%
- 47
12.5%
0 37
9.8%
7 28
 
7.4%
6 24
 
6.4%
1 22
 
5.9%
2 21
 
5.6%
9 19
 
5.1%
8 14
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 329
87.5%
Dash Punctuation 47
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 82
24.9%
3 68
20.7%
0 37
11.2%
7 28
 
8.5%
6 24
 
7.3%
1 22
 
6.7%
2 21
 
6.4%
9 19
 
5.8%
8 14
 
4.3%
4 14
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 376
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 82
21.8%
3 68
18.1%
- 47
12.5%
0 37
9.8%
7 28
 
7.4%
6 24
 
6.4%
1 22
 
5.9%
2 21
 
5.6%
9 19
 
5.1%
8 14
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 82
21.8%
3 68
18.1%
- 47
12.5%
0 37
9.8%
7 28
 
7.4%
6 24
 
6.4%
1 22
 
5.9%
2 21
 
5.6%
9 19
 
5.1%
8 14
 
3.7%

Interactions

2023-12-11T07:51:54.955402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:51:57.254779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1금송의원밀양시 삼랑진읍 천태로 50351-1370
11.0001.0000.9330.941
금송의원1.0001.0001.0001.000
밀양시 삼랑진읍 천태로 500.9331.0001.0000.985
351-13700.9411.0000.9851.000

Missing values

2023-12-11T07:51:55.056534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:51:55.147797image/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

1금송의원의원밀양시 삼랑진읍 천태로 50351-1370
02송광호의원의원밀양시 삼랑진읍 천태로 14-1351-0367
13오용창 내과의원의원밀양시 삼랑진읍 천태로 14-1353-6933
24신통의원의원삼랑진 천태로 75351-5005
35인성의원의원밀양시 하남읍 수산중앙로 21391-2288
46하준호 내과의원의원밀양시 하남읍 수산중앙로 16391-6991
57승진의원의원밀양시 하남읍 시서안길 34391-3131
68수산의원의원밀양시 하남읍 수산중앙로 7391-0237
79성모신경외과의원의원밀양시 하남읍 수산중앙로 11391-0777
810밀양구치소 부설의원의원밀양시 부북면 춘화로 124350-7705
911산내의원의원밀양시 산내면 산내로 346-1, 2층353-3785
1금송의원의원밀양시 삼랑진읍 천태로 50351-1370
3739밀양삼성정형외과(22병상)의원삼문동 165번지 6호 (1층,3층)353-7582
3840정소아과 의원의원밀양시 삼문동 165번지 6호(2층)356-5758
3941안상준내과의원의원밀양시 삼문중앙로 49, 4층(삼문동)352-5965
4042아름다운자모산부인과의원밀양시 삼문중앙로 14(삼문동)355-0348
4143하나정형외과의원(22병상)의원밀양시 삼문중앙로 14(삼문동)352-7075
4244미즈산부인과의원의원밀양시 삼문중앙로 49, 2층(삼문동)356-0025
4345엔젤소아과의원의원밀양시 삼문중앙로 49, 2층 201호(삼문동)355-5900
4446밀양이빈웰빙미의원의원밀양시 삼문강변로 344-6(삼문동)356-7017
4547세광가정의학과 의원의원밀양시 가곡7길 3(가곡동)355-7571
4648김보영안과의원의원삼문동 171번지 14호 세종메디칼빌딩352-5965