Overview

Dataset statistics

Number of variables6
Number of observations63
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory51.1 B

Variable types

Numeric1
Text4
Categorical1

Dataset

Description전북특별자치도 김제시 병의원(병원/한의원/치과) 현황입니다.
Author전라북도
URLhttps://www.bigdatahub.go.kr/index.jeonbuk?startPage=6&menuCd=DOM_000000103007001000&pListTypeStr=&pId=3068570

Alerts

순번 is highly overall correlated with 층수High correlation
층수 is highly overall correlated with 순번High correlation
층수 is highly imbalanced (62.2%)Imbalance
순번 has unique valuesUnique
의료기관명 has unique valuesUnique

Reproduction

Analysis started2024-03-14 02:36:26.975873
Analysis finished2024-03-14 02:36:27.491369
Duration0.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct63
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32
Minimum1
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2024-03-14T11:36:27.548420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.1
Q116.5
median32
Q347.5
95-th percentile59.9
Maximum63
Range62
Interquartile range (IQR)31

Descriptive statistics

Standard deviation18.330303
Coefficient of variation (CV)0.57282196
Kurtosis-1.2
Mean32
Median Absolute Deviation (MAD)16
Skewness0
Sum2016
Variance336
MonotonicityStrictly increasing
2024-03-14T11:36:27.869117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.6%
2 1
 
1.6%
35 1
 
1.6%
36 1
 
1.6%
37 1
 
1.6%
38 1
 
1.6%
39 1
 
1.6%
40 1
 
1.6%
41 1
 
1.6%
42 1
 
1.6%
Other values (53) 53
84.1%
ValueCountFrequency (%)
1 1
1.6%
2 1
1.6%
3 1
1.6%
4 1
1.6%
5 1
1.6%
6 1
1.6%
7 1
1.6%
8 1
1.6%
9 1
1.6%
10 1
1.6%
ValueCountFrequency (%)
63 1
1.6%
62 1
1.6%
61 1
1.6%
60 1
1.6%
59 1
1.6%
58 1
1.6%
57 1
1.6%
56 1
1.6%
55 1
1.6%
54 1
1.6%

의료기관명
Text

UNIQUE 

Distinct63
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size636.0 B
2024-03-14T11:36:28.211492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length13
Mean length8.2063492
Min length3

Characters and Unicode

Total characters517
Distinct characters124
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)100.0%

Sample

1st row(의) 예림의료재단 김제요양병원
2nd row(의)백상의료재단 가족사랑요양병원
3rd row믿음병원
4th row김제병원
5th row의료법인 명지원의료재단김제중앙병원
ValueCountFrequency (%)
의 1
 
1.4%
김제미래영상의학과의원 1
 
1.4%
우리정형외과의원 1
 
1.4%
원평연합의원 1
 
1.4%
튼튼가정의학과의원 1
 
1.4%
참좋은내과의원 1
 
1.4%
우리들마취통증의학과의원 1
 
1.4%
신피부과의원 1
 
1.4%
푸른가정의학과의원 1
 
1.4%
나방주내과의원 1
 
1.4%
Other values (60) 60
85.7%
2024-03-14T11:36:28.540379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
의 68
 
13.2%
원 65
 
12.6%
과 40
 
7.7%
김 15
 
2.9%
병 13
 
2.5%
제 13
 
2.5%
한 10
 
1.9%
정 9
 
1.7%
내 9
 
1.7%
학 8
 
1.5%
Other values (114) 267
51.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 504
97.5%
Space Separator 7
 
1.4%
Open Punctuation 3
 
0.6%
Close Punctuation 3
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 68
 
13.5%
원 65
 
12.9%
과 40
 
7.9%
김 15
 
3.0%
병 13
 
2.6%
제 13
 
2.6%
한 10
 
2.0%
정 9
 
1.8%
내 9
 
1.8%
학 8
 
1.6%
Other values (111) 254
50.4%
Space Separator
ValueCountFrequency (%)
7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 504
97.5%
Common 13
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 68
 
13.5%
원 65
 
12.9%
과 40
 
7.9%
김 15
 
3.0%
병 13
 
2.6%
제 13
 
2.6%
한 10
 
2.0%
정 9
 
1.8%
내 9
 
1.8%
학 8
 
1.6%
Other values (111) 254
50.4%
Common
ValueCountFrequency (%)
7
53.8%
( 3
23.1%
) 3
23.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 504
97.5%
ASCII 13
 
2.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
의 68
 
13.5%
원 65
 
12.9%
과 40
 
7.9%
김 15
 
3.0%
병 13
 
2.6%
제 13
 
2.6%
한 10
 
2.0%
정 9
 
1.8%
내 9
 
1.8%
학 8
 
1.6%
Other values (111) 254
50.4%
ASCII
ValueCountFrequency (%)
7
53.8%
( 3
23.1%
) 3
23.1%

의료기관주소(도로명)
Text

Distinct57
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Memory size636.0 B
2024-03-14T11:36:28.746084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length22
Mean length18.174603
Min length15

Characters and Unicode

Total characters1145
Distinct characters65
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

Unique51 ?
Unique (%)81.0%

Sample

1st row 김제시 남북로 202 (요촌동)
2nd row 김제시 하동1길 13 (하동)
3rd row 김제시 금성로 90-0 (신풍동)
4th row 김제시 중앙로 55 (서암동)
5th row 김제시 동서로 77 (교동)
ValueCountFrequency (%)
김제시 63
24.2%
요촌동 37
 
14.2%
동서로 14
 
5.4%
남북로 12
 
4.6%
중앙로 9
 
3.5%
검산동 5
 
1.9%
금산면 5
 
1.9%
신풍동 4
 
1.5%
금구면 3
 
1.2%
225 3
 
1.2%
Other values (79) 105
40.4%
2024-03-14T11:36:29.055748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
260
22.7%
동 72
 
6.3%
김 63
 
5.5%
제 63
 
5.5%
시 63
 
5.5%
( 51
 
4.5%
) 51
 
4.5%
로 45
 
3.9%
1 42
 
3.7%
2 38
 
3.3%
Other values (55) 397
34.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 583
50.9%
Space Separator 260
22.7%
Decimal Number 188
 
16.4%
Open Punctuation 51
 
4.5%
Close Punctuation 51
 
4.5%
Other Punctuation 6
 
0.5%
Dash Punctuation 6
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 72
12.3%
김 63
10.8%
제 63
10.8%
시 63
10.8%
로 45
 
7.7%
요 37
 
6.3%
촌 37
 
6.3%
서 22
 
3.8%
길 18
 
3.1%
산 14
 
2.4%
Other values (40) 149
25.6%
Decimal Number
ValueCountFrequency (%)
1 42
22.3%
2 38
20.2%
5 22
11.7%
3 16
 
8.5%
6 16
 
8.5%
0 15
 
8.0%
4 12
 
6.4%
9 10
 
5.3%
7 10
 
5.3%
8 7
 
3.7%
Space Separator
ValueCountFrequency (%)
260
100.0%
Open Punctuation
ValueCountFrequency (%)
( 51
100.0%
Close Punctuation
ValueCountFrequency (%)
) 51
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 583
50.9%
Common 562
49.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 72
12.3%
김 63
10.8%
제 63
10.8%
시 63
10.8%
로 45
 
7.7%
요 37
 
6.3%
촌 37
 
6.3%
서 22
 
3.8%
길 18
 
3.1%
산 14
 
2.4%
Other values (40) 149
25.6%
Common
ValueCountFrequency (%)
260
46.3%
( 51
 
9.1%
) 51
 
9.1%
1 42
 
7.5%
2 38
 
6.8%
5 22
 
3.9%
3 16
 
2.8%
6 16
 
2.8%
0 15
 
2.7%
4 12
 
2.1%
Other values (5) 39
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 583
50.9%
ASCII 562
49.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
260
46.3%
( 51
 
9.1%
) 51
 
9.1%
1 42
 
7.5%
2 38
 
6.8%
5 22
 
3.9%
3 16
 
2.8%
6 16
 
2.8%
0 15
 
2.7%
4 12
 
2.1%
Other values (5) 39
 
6.9%
Hangul
ValueCountFrequency (%)
동 72
12.3%
김 63
10.8%
제 63
10.8%
시 63
10.8%
로 45
 
7.7%
요 37
 
6.3%
촌 37
 
6.3%
서 22
 
3.8%
길 18
 
3.1%
산 14
 
2.4%
Other values (40) 149
25.6%

의료기관전화번호
Text

Distinct62
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size636.0 B
2024-03-14T11:36:29.250768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.015873
Min length11

Characters and Unicode

Total characters757
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)96.8%

Sample

1st row063-540-8500
2nd row063-540-1500
3rd row063543-5119
4th row063-770-8275
5th row063-547-9999
ValueCountFrequency (%)
063-545-8275 2
 
3.1%
063-544-2123 1
 
1.6%
063-542-0667 1
 
1.6%
063-548-0070 1
 
1.6%
063-545-3769 1
 
1.6%
063-542-7575 1
 
1.6%
063-546-3322 1
 
1.6%
063-545-0026 1
 
1.6%
063-544-7515 1
 
1.6%
063-544-1675 1
 
1.6%
Other values (53) 53
82.8%
2024-03-14T11:36:29.574284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 125
16.5%
0 124
16.4%
5 110
14.5%
3 88
11.6%
4 83
11.0%
6 74
9.8%
7 43
 
5.7%
8 39
 
5.2%
1 25
 
3.3%
2 23
 
3.0%
Other values (2) 23
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 631
83.4%
Dash Punctuation 125
 
16.5%
Space Separator 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 124
19.7%
5 110
17.4%
3 88
13.9%
4 83
13.2%
6 74
11.7%
7 43
 
6.8%
8 39
 
6.2%
1 25
 
4.0%
2 23
 
3.6%
9 22
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 125
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 757
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 125
16.5%
0 124
16.4%
5 110
14.5%
3 88
11.6%
4 83
11.0%
6 74
9.8%
7 43
 
5.7%
8 39
 
5.2%
1 25
 
3.3%
2 23
 
3.0%
Other values (2) 23
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 757
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 125
16.5%
0 124
16.4%
5 110
14.5%
3 88
11.6%
4 83
11.0%
6 74
9.8%
7 43
 
5.7%
8 39
 
5.2%
1 25
 
3.3%
2 23
 
3.0%
Other values (2) 23
 
3.0%

의료기관팩스번호
Text

Distinct52
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Memory size636.0 B
2024-03-14T11:36:29.785287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length10.238095
Min length2

Characters and Unicode

Total characters645
Distinct characters13
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

Unique50 ?
Unique (%)79.4%

Sample

1st row063-545-1875
2nd row063-540-1510
3rd row063543-5122
4th row063-770-8276
5th row없음
ValueCountFrequency (%)
없음 11
 
17.5%
063-545-7337 2
 
3.2%
063-544-8576 1
 
1.6%
063-545-1875 1
 
1.6%
063-548-0071 1
 
1.6%
063-546-8082 1
 
1.6%
063-545-0026 1
 
1.6%
063-546-7515 1
 
1.6%
063-545-1675 1
 
1.6%
063-542-3003 1
 
1.6%
Other values (42) 42
66.7%
2024-03-14T11:36:30.085452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 103
16.0%
5 92
14.3%
0 89
13.8%
3 80
12.4%
6 71
11.0%
4 64
9.9%
7 33
 
5.1%
2 33
 
5.1%
1 24
 
3.7%
8 19
 
2.9%
Other values (3) 37
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 520
80.6%
Dash Punctuation 103
 
16.0%
Other Letter 22
 
3.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 92
17.7%
0 89
17.1%
3 80
15.4%
6 71
13.7%
4 64
12.3%
7 33
 
6.3%
2 33
 
6.3%
1 24
 
4.6%
8 19
 
3.7%
9 15
 
2.9%
Other Letter
ValueCountFrequency (%)
없 11
50.0%
음 11
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 103
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 623
96.6%
Hangul 22
 
3.4%

Most frequent character per script

Common
ValueCountFrequency (%)
- 103
16.5%
5 92
14.8%
0 89
14.3%
3 80
12.8%
6 71
11.4%
4 64
10.3%
7 33
 
5.3%
2 33
 
5.3%
1 24
 
3.9%
8 19
 
3.0%
Hangul
ValueCountFrequency (%)
없 11
50.0%
음 11
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 623
96.6%
Hangul 22
 
3.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 103
16.5%
5 92
14.8%
0 89
14.3%
3 80
12.8%
6 71
11.4%
4 64
10.3%
7 33
 
5.3%
2 33
 
5.3%
1 24
 
3.9%
8 19
 
3.0%
Hangul
ValueCountFrequency (%)
없 11
50.0%
음 11
50.0%

층수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size636.0 B
5층이하
56 
5
 
5
6
 
2

Length

Max length4
Median length4
Mean length3.6666667
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row6
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5층이하 56
88.9%
5 5
 
7.9%
6 2
 
3.2%

Length

2024-03-14T11:36:30.229932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:36:30.418085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5층이하 56
88.9%
5 5
 
7.9%
6 2
 
3.2%

Interactions

2024-03-14T11:36:27.271932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T11:36:30.499454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번의료기관명의료기관주소(도로명)의료기관전화번호의료기관팩스번호층수
순번1.0001.0000.8341.0000.7640.768
의료기관명1.0001.0001.0001.0001.0001.000
의료기관주소(도로명)0.8341.0001.0001.0000.9140.915
의료기관전화번호1.0001.0001.0001.0001.0001.000
의료기관팩스번호0.7641.0000.9141.0001.0000.458
층수0.7681.0000.9151.0000.4581.000
2024-03-14T11:36:30.587447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번층수
순번1.0000.606
층수0.6061.000

Missing values

2024-03-14T11:36:27.372868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:36:27.458094image/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

순번의료기관명의료기관주소(도로명)의료기관전화번호의료기관팩스번호층수
01(의) 예림의료재단 김제요양병원김제시 남북로 202 (요촌동)063-540-8500063-545-18756
12(의)백상의료재단 가족사랑요양병원김제시 하동1길 13 (하동)063-540-1500063-540-15106
23믿음병원김제시 금성로 90-0 (신풍동)063543-5119063543-51225
34김제병원김제시 중앙로 55 (서암동)063-770-8275063-770-82765
45의료법인 명지원의료재단김제중앙병원김제시 동서로 77 (교동)063-547-9999없음5
56효드림요양병원김제시 화동길 135 (신풍동)063-547-5000없음5
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78김제우석병원김제시 서암4길 45 (서암동)063-540-5114063-540-51045층이하
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5354서울정형외과의원김제시 중앙로 129 (요촌동)063-544-0314063-543-88225층이하
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