Overview

Dataset statistics

Number of variables8
Number of observations45
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory67.9 B

Variable types

Numeric1
Text6
Categorical1

Dataset

Description요양보호사교육기관현황201411
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=201996

Alerts

번호 has unique valuesUnique
명 칭 has unique valuesUnique
소재지 has unique valuesUnique
전화번호 has unique valuesUnique

Reproduction

Analysis started2024-03-14 03:09:09.222286
Analysis finished2024-03-14 03:09:09.847647
Duration0.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23
Minimum1
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2024-03-14T12:09:09.919247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.2
Q112
median23
Q334
95-th percentile42.8
Maximum45
Range44
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.133926
Coefficient of variation (CV)0.57104024
Kurtosis-1.2
Mean23
Median Absolute Deviation (MAD)11
Skewness0
Sum1035
Variance172.5
MonotonicityStrictly increasing
2024-03-14T12:09:10.033360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1 1
 
2.2%
35 1
 
2.2%
26 1
 
2.2%
27 1
 
2.2%
28 1
 
2.2%
29 1
 
2.2%
30 1
 
2.2%
31 1
 
2.2%
32 1
 
2.2%
33 1
 
2.2%
Other values (35) 35
77.8%
ValueCountFrequency (%)
1 1
2.2%
2 1
2.2%
3 1
2.2%
4 1
2.2%
5 1
2.2%
6 1
2.2%
7 1
2.2%
8 1
2.2%
9 1
2.2%
10 1
2.2%
ValueCountFrequency (%)
45 1
2.2%
44 1
2.2%
43 1
2.2%
42 1
2.2%
41 1
2.2%
40 1
2.2%
39 1
2.2%
38 1
2.2%
37 1
2.2%
36 1
2.2%

명 칭
Text

UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size492.0 B
2024-03-14T12:09:10.216126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length20
Mean length14.488889
Min length4

Characters and Unicode

Total characters652
Distinct characters98
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)100.0%

Sample

1st row탑클래스
2nd row전주성신간호학원 부설 요양보호사교육원
3rd row김제성모 요양보호사교육원
4th row이리간호교육원 부설 요양보호사교육원
5th row군산간호교육원 부설 요양보호사교육원
ValueCountFrequency (%)
요양보호사교육원 14
 
18.4%
부설 11
 
14.5%
평생교육원 2
 
2.6%
탑클래스 1
 
1.3%
jk요양보호사교육원 1
 
1.3%
종로요양보호사교육원 1
 
1.3%
평화요양보호사교육원 1
 
1.3%
정읍간호학원부설요양보호사교육원 1
 
1.3%
메디컬요양보호사교육원 1
 
1.3%
남원한국요양보호사교육원 1
 
1.3%
Other values (42) 42
55.3%
2024-03-14T12:09:10.548145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 64
 
9.8%
호 60
 
9.2%
교 50
 
7.7%
육 49
 
7.5%
사 45
 
6.9%
보 45
 
6.9%
요 44
 
6.7%
양 44
 
6.7%
32
 
4.9%
간 16
 
2.5%
Other values (88) 203
31.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 616
94.5%
Space Separator 32
 
4.9%
Uppercase Letter 1
 
0.2%
Lowercase Letter 1
 
0.2%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 64
 
10.4%
호 60
 
9.7%
교 50
 
8.1%
육 49
 
8.0%
사 45
 
7.3%
보 45
 
7.3%
요 44
 
7.1%
양 44
 
7.1%
간 16
 
2.6%
부 16
 
2.6%
Other values (83) 183
29.7%
Space Separator
ValueCountFrequency (%)
32
100.0%
Uppercase Letter
ValueCountFrequency (%)
J 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
k 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 616
94.5%
Common 34
 
5.2%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 64
 
10.4%
호 60
 
9.7%
교 50
 
8.1%
육 49
 
8.0%
사 45
 
7.3%
보 45
 
7.3%
요 44
 
7.1%
양 44
 
7.1%
간 16
 
2.6%
부 16
 
2.6%
Other values (83) 183
29.7%
Common
ValueCountFrequency (%)
32
94.1%
( 1
 
2.9%
) 1
 
2.9%
Latin
ValueCountFrequency (%)
J 1
50.0%
k 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 616
94.5%
ASCII 36
 
5.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 64
 
10.4%
호 60
 
9.7%
교 50
 
8.1%
육 49
 
8.0%
사 45
 
7.3%
보 45
 
7.3%
요 44
 
7.1%
양 44
 
7.1%
간 16
 
2.6%
부 16
 
2.6%
Other values (83) 183
29.7%
ASCII
ValueCountFrequency (%)
32
88.9%
J 1
 
2.8%
k 1
 
2.8%
( 1
 
2.8%
) 1
 
2.8%

소재지
Text

UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size492.0 B
2024-03-14T12:09:10.795795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length23
Mean length20.2
Min length15

Characters and Unicode

Total characters909
Distinct characters113
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

Unique45 ?
Unique (%)100.0%

Sample

1st row전주시 완산구 팔달로 212-8 (경원동3가)
2nd row전주시 완산구 팔달로 250 (서노송동)
3rd row김제시 동서로 222 (요촌동)
4th row익산시 익산대로 58-7 (평화동)
5th row군산시 경포천로 153 (경장동)
ValueCountFrequency (%)
전주시 18
 
8.9%
완산구 12
 
5.9%
익산시 8
 
3.9%
덕진구 6
 
3.0%
군산시 6
 
3.0%
팔달로 5
 
2.5%
3층 5
 
2.5%
무왕로 4
 
2.0%
익산대로 4
 
2.0%
2가 4
 
2.0%
Other values (116) 131
64.5%
2024-03-14T12:09:11.153738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
158
 
17.4%
1 41
 
4.5%
로 41
 
4.5%
시 39
 
4.3%
2 35
 
3.9%
산 35
 
3.9%
동 35
 
3.9%
( 33
 
3.6%
) 33
 
3.6%
3 23
 
2.5%
Other values (103) 436
48.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 493
54.2%
Decimal Number 173
 
19.0%
Space Separator 158
 
17.4%
Open Punctuation 33
 
3.6%
Close Punctuation 33
 
3.6%
Dash Punctuation 11
 
1.2%
Other Punctuation 8
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 41
 
8.3%
시 39
 
7.9%
산 35
 
7.1%
동 35
 
7.1%
전 21
 
4.3%
주 21
 
4.3%
구 19
 
3.9%
완 15
 
3.0%
가 15
 
3.0%
익 12
 
2.4%
Other values (88) 240
48.7%
Decimal Number
ValueCountFrequency (%)
1 41
23.7%
2 35
20.2%
3 23
13.3%
4 18
10.4%
7 12
 
6.9%
0 11
 
6.4%
8 9
 
5.2%
6 9
 
5.2%
5 8
 
4.6%
9 7
 
4.0%
Space Separator
ValueCountFrequency (%)
158
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 493
54.2%
Common 416
45.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 41
 
8.3%
시 39
 
7.9%
산 35
 
7.1%
동 35
 
7.1%
전 21
 
4.3%
주 21
 
4.3%
구 19
 
3.9%
완 15
 
3.0%
가 15
 
3.0%
익 12
 
2.4%
Other values (88) 240
48.7%
Common
ValueCountFrequency (%)
158
38.0%
1 41
 
9.9%
2 35
 
8.4%
( 33
 
7.9%
) 33
 
7.9%
3 23
 
5.5%
4 18
 
4.3%
7 12
 
2.9%
0 11
 
2.6%
- 11
 
2.6%
Other values (5) 41
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 493
54.2%
ASCII 416
45.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
158
38.0%
1 41
 
9.9%
2 35
 
8.4%
( 33
 
7.9%
) 33
 
7.9%
3 23
 
5.5%
4 18
 
4.3%
7 12
 
2.9%
0 11
 
2.6%
- 11
 
2.6%
Other values (5) 41
 
9.9%
Hangul
ValueCountFrequency (%)
로 41
 
8.3%
시 39
 
7.9%
산 35
 
7.1%
동 35
 
7.1%
전 21
 
4.3%
주 21
 
4.3%
구 19
 
3.9%
완 15
 
3.0%
가 15
 
3.0%
익 12
 
2.4%
Other values (88) 240
48.7%

대표자
Text

Distinct42
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size492.0 B
2024-03-14T12:09:11.355789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length3
Mean length3.2888889
Min length3

Characters and Unicode

Total characters148
Distinct characters70
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

Unique39 ?
Unique (%)86.7%

Sample

1st row송제기
2nd row이연주
3rd row박수임
4th row지정무
5th row지정무
ValueCountFrequency (%)
정선희 2
 
4.3%
지정무 2
 
4.3%
배순주 2
 
4.3%
오규만 1
 
2.2%
기정임 1
 
2.2%
학교법인 1
 
2.2%
전주기독학원(윤정길 1
 
2.2%
최정옥 1
 
2.2%
정성훈 1
 
2.2%
박진표 1
 
2.2%
Other values (33) 33
71.7%
2024-03-14T12:09:11.626908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
정 11
 
7.4%
김 7
 
4.7%
성 7
 
4.7%
박 6
 
4.1%
이 6
 
4.1%
선 5
 
3.4%
기 5
 
3.4%
순 5
 
3.4%
희 5
 
3.4%
옥 4
 
2.7%
Other values (60) 87
58.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 145
98.0%
Open Punctuation 1
 
0.7%
Close Punctuation 1
 
0.7%
Space Separator 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 11
 
7.6%
김 7
 
4.8%
성 7
 
4.8%
박 6
 
4.1%
이 6
 
4.1%
선 5
 
3.4%
기 5
 
3.4%
순 5
 
3.4%
희 5
 
3.4%
옥 4
 
2.8%
Other values (57) 84
57.9%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 145
98.0%
Common 3
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 11
 
7.6%
김 7
 
4.8%
성 7
 
4.8%
박 6
 
4.1%
이 6
 
4.1%
선 5
 
3.4%
기 5
 
3.4%
순 5
 
3.4%
희 5
 
3.4%
옥 4
 
2.8%
Other values (57) 84
57.9%
Common
ValueCountFrequency (%)
( 1
33.3%
) 1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 145
98.0%
ASCII 3
 
2.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
정 11
 
7.6%
김 7
 
4.8%
성 7
 
4.8%
박 6
 
4.1%
이 6
 
4.1%
선 5
 
3.4%
기 5
 
3.4%
순 5
 
3.4%
희 5
 
3.4%
옥 4
 
2.8%
Other values (57) 84
57.9%
ASCII
ValueCountFrequency (%)
( 1
33.3%
) 1
33.3%
1
33.3%

정원
Categorical

Distinct10
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size492.0 B
40명
17 
30명
17 
20명
3 
35명
2 
34명
 
1
Other values (5)
5 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique6 ?
Unique (%)13.3%

Sample

1st row40명
2nd row40명
3rd row34명
4th row40명
5th row30명

Common Values

ValueCountFrequency (%)
40명 17
37.8%
30명 17
37.8%
20명 3
 
6.7%
35명 2
 
4.4%
34명 1
 
2.2%
36명 1
 
2.2%
26명 1
 
2.2%
16명 1
 
2.2%
15명 1
 
2.2%
25명 1
 
2.2%

Length

2024-03-14T12:09:11.734988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:09:11.855041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
40명 17
37.8%
30명 17
37.8%
20명 3
 
6.7%
35명 2
 
4.4%
34명 1
 
2.2%
36명 1
 
2.2%
26명 1
 
2.2%
16명 1
 
2.2%
15명 1
 
2.2%
25명 1
 
2.2%

전화번호
Text

UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size492.0 B
2024-03-14T12:09:12.246984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length8
Mean length8.8666667
Min length8

Characters and Unicode

Total characters399
Distinct characters13
Distinct categories4 ?
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 (%)100.0%

Sample

1st row282-6500
2nd row285-1999 285-1990
3rd row545-9888
4th row851-2411
5th row446-1123
ValueCountFrequency (%)
282-6500 1
 
2.1%
285-1999 1
 
2.1%
858-9840 1
 
2.1%
538-3663 1
 
2.1%
225-1441 1
 
2.1%
535-0297 1
 
2.1%
446-5055 1
 
2.1%
626-2233 1
 
2.1%
433-1550 1
 
2.1%
277-7701 1
 
2.1%
Other values (38) 38
79.2%
2024-03-14T12:09:12.591751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 59
14.8%
- 50
12.5%
3 38
9.5%
8 37
9.3%
5 36
9.0%
4 36
9.0%
1 34
8.5%
0 33
8.3%
9 25
6.3%
7 25
6.3%
Other values (3) 26
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 345
86.5%
Dash Punctuation 50
 
12.5%
Control 3
 
0.8%
Math Symbol 1
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 59
17.1%
3 38
11.0%
8 37
10.7%
5 36
10.4%
4 36
10.4%
1 34
9.9%
0 33
9.6%
9 25
7.2%
7 25
7.2%
6 22
 
6.4%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%
Control
ValueCountFrequency (%)
3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 399
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 59
14.8%
- 50
12.5%
3 38
9.5%
8 37
9.3%
5 36
9.0%
4 36
9.0%
1 34
8.5%
0 33
8.3%
9 25
6.3%
7 25
6.3%
Other values (3) 26
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 399
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 59
14.8%
- 50
12.5%
3 38
9.5%
8 37
9.3%
5 36
9.0%
4 36
9.0%
1 34
8.5%
0 33
8.3%
9 25
6.3%
7 25
6.3%
Other values (3) 26
6.5%

FAX
Text

Distinct44
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size492.0 B
2024-03-14T12:09:12.781148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length8
Mean length8.0666667
Min length1

Characters and Unicode

Total characters363
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

Unique43 ?
Unique (%)95.6%

Sample

1st row282-6501
2nd row285-6991
3rd row545-9887
4th row842-2411
5th row446-1126
ValueCountFrequency (%)
853-8334 2
 
4.4%
282-6501 1
 
2.2%
1
 
2.2%
858-9841 1
 
2.2%
227-1010 1
 
2.2%
538-5371 1
 
2.2%
445-5056 1
 
2.2%
626-2283 1
 
2.2%
433-1507 1
 
2.2%
277-7702 1
 
2.2%
Other values (34) 34
75.6%
2024-03-14T12:09:13.065103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 47
12.9%
2 46
12.7%
1 39
10.7%
3 38
10.5%
5 36
9.9%
8 35
9.6%
4 30
8.3%
0 28
7.7%
6 25
6.9%
7 20
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316
87.1%
Dash Punctuation 47
 
12.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 46
14.6%
1 39
12.3%
3 38
12.0%
5 36
11.4%
8 35
11.1%
4 30
9.5%
0 28
8.9%
6 25
7.9%
7 20
6.3%
9 19
6.0%
Dash Punctuation
ValueCountFrequency (%)
- 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 363
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 47
12.9%
2 46
12.7%
1 39
10.7%
3 38
10.5%
5 36
9.9%
8 35
9.6%
4 30
8.3%
0 28
7.7%
6 25
6.9%
7 20
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 47
12.9%
2 46
12.7%
1 39
10.7%
3 38
10.5%
5 36
9.9%
8 35
9.6%
4 30
8.3%
0 28
7.7%
6 25
6.9%
7 20
5.5%

우편번호
Text

Distinct37
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Memory size492.0 B
2024-03-14T12:09:13.229812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters315
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

Unique31 ?
Unique (%)68.9%

Sample

1st row560-023
2nd row560-913
3rd row576-805
4th row570-010
5th row573-420
ValueCountFrequency (%)
560-023 4
 
8.9%
573-420 2
 
4.4%
561-870 2
 
4.4%
561-820 2
 
4.4%
570-993 2
 
4.4%
570-982 2
 
4.4%
580-030 1
 
2.2%
560-912 1
 
2.2%
573-871 1
 
2.2%
597-842 1
 
2.2%
Other values (27) 27
60.0%
2024-03-14T12:09:13.521966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 59
18.7%
5 56
17.8%
- 45
14.3%
7 30
9.5%
6 26
8.3%
8 24
7.6%
1 19
 
6.0%
2 17
 
5.4%
3 16
 
5.1%
9 16
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 270
85.7%
Dash Punctuation 45
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59
21.9%
5 56
20.7%
7 30
11.1%
6 26
9.6%
8 24
8.9%
1 19
 
7.0%
2 17
 
6.3%
3 16
 
5.9%
9 16
 
5.9%
4 7
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 315
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59
18.7%
5 56
17.8%
- 45
14.3%
7 30
9.5%
6 26
8.3%
8 24
7.6%
1 19
 
6.0%
2 17
 
5.4%
3 16
 
5.1%
9 16
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 315
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59
18.7%
5 56
17.8%
- 45
14.3%
7 30
9.5%
6 26
8.3%
8 24
7.6%
1 19
 
6.0%
2 17
 
5.4%
3 16
 
5.1%
9 16
 
5.1%

Interactions

2024-03-14T12:09:09.627250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T12:09:13.613307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호명 칭소재지대표자정원전화번호FAX우편번호
번호1.0001.0001.0000.9290.4071.0000.9450.789
명 칭1.0001.0001.0001.0001.0001.0001.0001.000
소재지1.0001.0001.0001.0001.0001.0001.0001.000
대표자0.9291.0001.0001.0000.9961.0000.9920.979
정원0.4071.0001.0000.9961.0001.0000.9920.906
전화번호1.0001.0001.0001.0001.0001.0001.0001.000
FAX0.9451.0001.0000.9920.9921.0001.0000.994
우편번호0.7891.0001.0000.9790.9061.0000.9941.000
2024-03-14T12:09:13.705429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호정원
번호1.0000.113
정원0.1131.000

Missing values

2024-03-14T12:09:09.716835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T12:09:09.808382image/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

번호명 칭소재지대표자정원전화번호FAX우편번호
01탑클래스전주시 완산구 팔달로 212-8 (경원동3가)송제기40명282-6500282-6501560-023
12전주성신간호학원 부설 요양보호사교육원전주시 완산구 팔달로 250 (서노송동)이연주40명285-1999 285-1990285-6991560-913
23김제성모 요양보호사교육원김제시 동서로 222 (요촌동)박수임34명545-9888545-9887576-805
34이리간호교육원 부설 요양보호사교육원익산시 익산대로 58-7 (평화동)지정무40명851-2411842-2411570-010
45군산간호교육원 부설 요양보호사교육원군산시 경포천로 153 (경장동)지정무30명446-1123446-1126573-420
56온누리요양보호사교육원전주시 완산구 팔달로 202-16박흥순30명231-4554231-4553560-023
67남원간호요양보호사교육원남원시 의총로 77(왕정동)박성희36명632-2993 010-9004-4563631-2993590-120
78전주성모간호교육원 부설 전주성모요양보호사교육원전주시 완산구 어진길 114 (경원동 1가)윤석길35명283-6662~5286-6661560-020
89전주요양보호사교육원전주시 덕진구 떡전 4길 8 (금암동)신성호30명284-1199232-8001561-180
910원광효도마을 요양보호사교육원익산시 무왕로 864,3층(신동)오순옥40명852-0129852-0139570-976
번호명 칭소재지대표자정원전화번호FAX우편번호
3536봉동간호학원 부설 요양보호사교육원완주군 봉동읍 봉동로 140(장기리 225-1)배순주30명261-1125261-1165565-907
3637남전주요양보호사교육원전주시 완산구 모악로 4692(평화동2가)김송이20명221-89980505-300-8900560-865
3738늘푸른요양보호사교육원장수군 장계면 한들로 107신유림25명010-7384-0825353-6699597-842
3839솔빛간호학원부설요양보호사교육원전주시 덕진구 솔내로 164(송천동 2가, 4층)이수정30명274-9858275-1089561-820
3940전주안골간호학원부설요양보호사교육원전주시 덕진구 안덕원로 262, 5층배순주30명246-1160243-1165561-870
4041평화간호전문학원 요양보호사교육원익산시 무왕로 1073, 4층박월순30명833-4477841-2323570-982
4142중앙간호학원요양보호사교육원군산시 공단대로 381 4층오규만30명471-2970471-2971573-871
4243제이성모요양보호사교육원익산시 무왕로 1071, 3층이태영30명833-3370853-8334570-982
4344고창요양보호사교육원고창군 고창읍 중앙로 180, 3층김재홍30명563-0038563-0018585-808
4445아중안골요양보호사교육원전주시 덕진구 견훤로 289, 3층김은희35명282-2323242-1441561-870