Overview

Dataset statistics

Number of variables6
Number of observations108
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 KiB
Average record size in memory50.2 B

Variable types

Numeric1
Categorical1
Text4

Dataset

Description충청남도 홍성군 의료기관(병원,의원,부속의원,치과의원,한의원) 데이터 정보입니다.(연번, 분류, 의료기관명, 개설자명, 주소, 전화번호)
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=47&beforeMenuCd=DOM_000000201001001000&publicdatapk=15113344

Alerts

연번 is highly overall correlated with 분류High correlation
분류 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
의료기관명 has unique valuesUnique
전화번호 has unique valuesUnique

Reproduction

Analysis started2024-01-09 20:58:39.595530
Analysis finished2024-01-09 20:58:40.113247
Duration0.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct108
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.5
Minimum1
Maximum108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T05:58:40.174929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.35
Q127.75
median54.5
Q381.25
95-th percentile102.65
Maximum108
Range107
Interquartile range (IQR)53.5

Descriptive statistics

Standard deviation31.32092
Coefficient of variation (CV)0.57469577
Kurtosis-1.2
Mean54.5
Median Absolute Deviation (MAD)27
Skewness0
Sum5886
Variance981
MonotonicityStrictly increasing
2024-01-10T05:58:40.288314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.9%
70 1
 
0.9%
81 1
 
0.9%
80 1
 
0.9%
79 1
 
0.9%
78 1
 
0.9%
77 1
 
0.9%
76 1
 
0.9%
75 1
 
0.9%
74 1
 
0.9%
Other values (98) 98
90.7%
ValueCountFrequency (%)
1 1
0.9%
2 1
0.9%
3 1
0.9%
4 1
0.9%
5 1
0.9%
6 1
0.9%
7 1
0.9%
8 1
0.9%
9 1
0.9%
10 1
0.9%
ValueCountFrequency (%)
108 1
0.9%
107 1
0.9%
106 1
0.9%
105 1
0.9%
104 1
0.9%
103 1
0.9%
102 1
0.9%
101 1
0.9%
100 1
0.9%
99 1
0.9%

분류
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size996.0 B
의원
49 
치과의원
27 
한의원
23 
병원
8 
부속의원
 
1

Length

Max length4
Median length2
Mean length2.7314815
Min length2

Unique

Unique1 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
의원 49
45.4%
치과의원 27
25.0%
한의원 23
21.3%
병원 8
 
7.4%
부속의원 1
 
0.9%

Length

2024-01-10T05:58:40.400888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:58:40.498446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의원 49
45.4%
치과의원 27
25.0%
한의원 23
21.3%
병원 8
 
7.4%
부속의원 1
 
0.9%

의료기관명
Text

UNIQUE 

Distinct108
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size996.0 B
2024-01-10T05:58:40.698561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length6.787037
Min length4

Characters and Unicode

Total characters733
Distinct characters148
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

Unique108 ?
Unique (%)100.0%

Sample

1st row충청남도홍성의료원
2nd row충남한방병원
3rd row내포홍성한방병원
4th row홍주요양병원
5th row신동환병원
ValueCountFrequency (%)
충청남도홍성의료원 1
 
0.9%
홍성미소치과의원 1
 
0.9%
박현수치과의원 1
 
0.9%
고정치과의원 1
 
0.9%
정치과의원 1
 
0.9%
홍성치과의원 1
 
0.9%
수치주치과의원 1
 
0.9%
서울연세치과의원 1
 
0.9%
서울은치과의원 1
 
0.9%
연세미치과의원 1
 
0.9%
Other values (99) 99
90.8%
2024-01-10T05:58:41.098255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 109
 
14.9%
의 102
 
13.9%
과 62
 
8.5%
한 30
 
4.1%
치 28
 
3.8%
성 20
 
2.7%
홍 19
 
2.6%
내 16
 
2.2%
이 12
 
1.6%
연 9
 
1.2%
Other values (138) 326
44.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 730
99.6%
Space Separator 2
 
0.3%
Decimal Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 109
 
14.9%
의 102
 
14.0%
과 62
 
8.5%
한 30
 
4.1%
치 28
 
3.8%
성 20
 
2.7%
홍 19
 
2.6%
내 16
 
2.2%
이 12
 
1.6%
연 9
 
1.2%
Other values (136) 323
44.2%
Space Separator
ValueCountFrequency (%)
2
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 730
99.6%
Common 3
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 109
 
14.9%
의 102
 
14.0%
과 62
 
8.5%
한 30
 
4.1%
치 28
 
3.8%
성 20
 
2.7%
홍 19
 
2.6%
내 16
 
2.2%
이 12
 
1.6%
연 9
 
1.2%
Other values (136) 323
44.2%
Common
ValueCountFrequency (%)
2
66.7%
3 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 730
99.6%
ASCII 3
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 109
 
14.9%
의 102
 
14.0%
과 62
 
8.5%
한 30
 
4.1%
치 28
 
3.8%
성 20
 
2.7%
홍 19
 
2.6%
내 16
 
2.2%
이 12
 
1.6%
연 9
 
1.2%
Other values (136) 323
44.2%
ASCII
ValueCountFrequency (%)
2
66.7%
3 1
33.3%

개설자명
Text

Distinct107
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size996.0 B
2024-01-10T05:58:41.387435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.962963
Min length2

Characters and Unicode

Total characters320
Distinct characters106
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)98.1%

Sample

1st row김건식
2nd row노승만
3rd row박영준
4th row위승철
5th row신동환
ValueCountFrequency (%)
박태수 2
 
1.9%
김용태 1
 
0.9%
김기서 1
 
0.9%
정철순 1
 
0.9%
손성수 1
 
0.9%
최지연 1
 
0.9%
허윤준 1
 
0.9%
이창훈 1
 
0.9%
김선 1
 
0.9%
김준열 1
 
0.9%
Other values (97) 97
89.8%
2024-01-10T05:58:41.770686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 20
 
6.2%
이 18
 
5.6%
박 12
 
3.8%
수 11
 
3.4%
영 10
 
3.1%
승 10
 
3.1%
성 9
 
2.8%
상 7
 
2.2%
재 7
 
2.2%
윤 7
 
2.2%
Other values (96) 209
65.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 320
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 20
 
6.2%
이 18
 
5.6%
박 12
 
3.8%
수 11
 
3.4%
영 10
 
3.1%
승 10
 
3.1%
성 9
 
2.8%
상 7
 
2.2%
재 7
 
2.2%
윤 7
 
2.2%
Other values (96) 209
65.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 320
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 20
 
6.2%
이 18
 
5.6%
박 12
 
3.8%
수 11
 
3.4%
영 10
 
3.1%
승 10
 
3.1%
성 9
 
2.8%
상 7
 
2.2%
재 7
 
2.2%
윤 7
 
2.2%
Other values (96) 209
65.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 320
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 20
 
6.2%
이 18
 
5.6%
박 12
 
3.8%
수 11
 
3.4%
영 10
 
3.1%
승 10
 
3.1%
성 9
 
2.8%
상 7
 
2.2%
재 7
 
2.2%
윤 7
 
2.2%
Other values (96) 209
65.3%

주소
Text

Distinct97
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Memory size996.0 B
2024-01-10T05:58:42.049835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length30
Mean length24.074074
Min length18

Characters and Unicode

Total characters2600
Distinct characters96
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)80.6%

Sample

1st row충청남도 홍성군 홍성읍 조양로 244
2nd row충청남도 홍성군 홍성읍 조양로 244
3rd row충청남도 홍성군 홍성읍 조양로 231
4th row충청남도 홍성군 홍성읍 백월로 130
5th row충청남도 홍성군 홍성읍 홍성천길214, 두리프라자4~6층
ValueCountFrequency (%)
충청남도 108
18.0%
홍성군 108
18.0%
홍성읍 71
 
11.8%
내포로 25
 
4.2%
조양로 19
 
3.2%
홍북읍 19
 
3.2%
홍성천길 12
 
2.0%
광천읍 11
 
1.8%
8 9
 
1.5%
청사로 9
 
1.5%
Other values (140) 210
34.9%
2024-01-10T05:58:42.439462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
493
19.0%
홍 231
 
8.9%
성 196
 
7.5%
청 119
 
4.6%
남 115
 
4.4%
충 112
 
4.3%
도 108
 
4.2%
군 108
 
4.2%
읍 101
 
3.9%
로 90
 
3.5%
Other values (86) 927
35.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1621
62.3%
Space Separator 493
 
19.0%
Decimal Number 398
 
15.3%
Other Punctuation 52
 
2.0%
Open Punctuation 13
 
0.5%
Close Punctuation 13
 
0.5%
Dash Punctuation 9
 
0.3%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
홍 231
14.3%
성 196
12.1%
청 119
 
7.3%
남 115
 
7.1%
충 112
 
6.9%
도 108
 
6.7%
군 108
 
6.7%
읍 101
 
6.2%
로 90
 
5.6%
길 34
 
2.1%
Other values (69) 407
25.1%
Decimal Number
ValueCountFrequency (%)
1 75
18.8%
2 66
16.6%
4 51
12.8%
3 46
11.6%
0 37
9.3%
8 32
8.0%
6 32
8.0%
5 23
 
5.8%
9 20
 
5.0%
7 16
 
4.0%
Other Punctuation
ValueCountFrequency (%)
, 51
98.1%
/ 1
 
1.9%
Space Separator
ValueCountFrequency (%)
493
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1621
62.3%
Common 979
37.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
홍 231
14.3%
성 196
12.1%
청 119
 
7.3%
남 115
 
7.1%
충 112
 
6.9%
도 108
 
6.7%
군 108
 
6.7%
읍 101
 
6.2%
로 90
 
5.6%
길 34
 
2.1%
Other values (69) 407
25.1%
Common
ValueCountFrequency (%)
493
50.4%
1 75
 
7.7%
2 66
 
6.7%
4 51
 
5.2%
, 51
 
5.2%
3 46
 
4.7%
0 37
 
3.8%
8 32
 
3.3%
6 32
 
3.3%
5 23
 
2.3%
Other values (7) 73
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1621
62.3%
ASCII 979
37.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
493
50.4%
1 75
 
7.7%
2 66
 
6.7%
4 51
 
5.2%
, 51
 
5.2%
3 46
 
4.7%
0 37
 
3.8%
8 32
 
3.3%
6 32
 
3.3%
5 23
 
2.3%
Other values (7) 73
 
7.5%
Hangul
ValueCountFrequency (%)
홍 231
14.3%
성 196
12.1%
청 119
 
7.3%
남 115
 
7.1%
충 112
 
6.9%
도 108
 
6.7%
군 108
 
6.7%
읍 101
 
6.2%
로 90
 
5.6%
길 34
 
2.1%
Other values (69) 407
25.1%

전화번호
Text

UNIQUE 

Distinct108
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size996.0 B
2024-01-10T05:58:42.655587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.018519
Min length12

Characters and Unicode

Total characters1298
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

Unique108 ?
Unique (%)100.0%

Sample

1st row041-630-6251
2nd row041-633-8888
3rd row041-632-0001
4th row041-632-2332
5th row041-631-7541
ValueCountFrequency (%)
041-630-6251 1
 
0.9%
041-631-2829 1
 
0.9%
041-634-5828 1
 
0.9%
041-632-3610 1
 
0.9%
041-641-3699 1
 
0.9%
041-631-2879 1
 
0.9%
041-631-7528 1
 
0.9%
041-631-2725 1
 
0.9%
041-634-2879 1
 
0.9%
041-641-2872 1
 
0.9%
Other values (98) 98
90.7%
2024-01-10T05:58:42.999472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 216
16.6%
1 182
14.0%
0 178
13.7%
4 150
11.6%
3 143
11.0%
6 125
9.6%
2 95
7.3%
5 72
 
5.5%
7 60
 
4.6%
8 53
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1082
83.4%
Dash Punctuation 216
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 182
16.8%
0 178
16.5%
4 150
13.9%
3 143
13.2%
6 125
11.6%
2 95
8.8%
5 72
 
6.7%
7 60
 
5.5%
8 53
 
4.9%
9 24
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 216
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1298
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 216
16.6%
1 182
14.0%
0 178
13.7%
4 150
11.6%
3 143
11.0%
6 125
9.6%
2 95
7.3%
5 72
 
5.5%
7 60
 
4.6%
8 53
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1298
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 216
16.6%
1 182
14.0%
0 178
13.7%
4 150
11.6%
3 143
11.0%
6 125
9.6%
2 95
7.3%
5 72
 
5.5%
7 60
 
4.6%
8 53
 
4.1%

Interactions

2024-01-10T05:58:39.897297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:58:43.100727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번분류주소
연번1.0000.9750.898
분류0.9751.0000.986
주소0.8980.9861.000
2024-01-10T05:58:43.181721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번분류
연번1.0000.760
분류0.7601.000

Missing values

2024-01-10T05:58:39.994975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:58:40.080550image/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

연번분류의료기관명개설자명주소전화번호
01병원충청남도홍성의료원김건식충청남도 홍성군 홍성읍 조양로 244041-630-6251
12병원충남한방병원노승만충청남도 홍성군 홍성읍 조양로 244041-633-8888
23병원내포홍성한방병원박영준충청남도 홍성군 홍성읍 조양로 231041-632-0001
34병원홍주요양병원위승철충청남도 홍성군 홍성읍 백월로 130041-632-2332
45병원신동환병원신동환충청남도 홍성군 홍성읍 홍성천길214, 두리프라자4~6층041-631-7541
56병원장수요양병원곽정욱충청남도 홍성군 은하면 구성남로 381041-642-0001
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