Overview

Dataset statistics

Number of variables8
Number of observations49
Missing cells17
Missing cells (%)4.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory66.7 B

Variable types

Text7
Categorical1

Dataset

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

Alerts

요양보호사 교육원 has 3 (6.1%) missing valuesMissing
Unnamed: 1 has 1 (2.0%) missing valuesMissing
Unnamed: 2 has 1 (2.0%) missing valuesMissing
Unnamed: 3 has 3 (6.1%) missing valuesMissing
Unnamed: 5 has 3 (6.1%) missing valuesMissing
Unnamed: 6 has 4 (8.2%) missing valuesMissing
Unnamed: 7 has 2 (4.1%) missing valuesMissing

Reproduction

Analysis started2024-03-14 03:09:14.509172
Analysis finished2024-03-14 03:09:15.316915
Duration0.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

요양보호사 교육원
Text

MISSING 

Distinct46
Distinct (%)100.0%
Missing3
Missing (%)6.1%
Memory size524.0 B
2024-03-14T12:09:15.475085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.8043478
Min length1

Characters and Unicode

Total characters83
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)100.0%

Sample

1st row번호
2nd row1
3rd row2
4th row3
5th row4
ValueCountFrequency (%)
11 1
 
2.2%
23 1
 
2.2%
25 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%
Other values (36) 36
78.3%
2024-03-14T12:09:15.764632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 15
18.1%
2 15
18.1%
3 15
18.1%
4 11
13.3%
5 5
 
6.0%
6 4
 
4.8%
7 4
 
4.8%
8 4
 
4.8%
9 4
 
4.8%
0 4
 
4.8%
Other values (2) 2
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 81
97.6%
Other Letter 2
 
2.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15
18.5%
2 15
18.5%
3 15
18.5%
4 11
13.6%
5 5
 
6.2%
6 4
 
4.9%
7 4
 
4.9%
8 4
 
4.9%
9 4
 
4.9%
0 4
 
4.9%
Other Letter
ValueCountFrequency (%)
번 1
50.0%
호 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 81
97.6%
Hangul 2
 
2.4%

Most frequent character per script

Common
ValueCountFrequency (%)
1 15
18.5%
2 15
18.5%
3 15
18.5%
4 11
13.6%
5 5
 
6.2%
6 4
 
4.9%
7 4
 
4.9%
8 4
 
4.9%
9 4
 
4.9%
0 4
 
4.9%
Hangul
ValueCountFrequency (%)
번 1
50.0%
호 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81
97.6%
Hangul 2
 
2.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 15
18.5%
2 15
18.5%
3 15
18.5%
4 11
13.6%
5 5
 
6.2%
6 4
 
4.9%
7 4
 
4.9%
8 4
 
4.9%
9 4
 
4.9%
0 4
 
4.9%
Hangul
ValueCountFrequency (%)
번 1
50.0%
호 1
50.0%

Unnamed: 1
Text

MISSING 

Distinct48
Distinct (%)100.0%
Missing1
Missing (%)2.0%
Memory size524.0 B
2024-03-14T12:09:15.975487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length20
Mean length14.333333
Min length4

Characters and Unicode

Total characters688
Distinct characters106
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

Unique48 ?
Unique (%)100.0%

Sample

1st row명 칭
2nd row적정 요양보호사교육원 개소수
3rd row현재 운영 중인 교육원 개소수
4th row탑클래스
5th row전주성신간호학원 부설 요양보호사교육원
ValueCountFrequency (%)
요양보호사교육원 15
 
17.4%
부설 11
 
12.8%
평생교육원 2
 
2.3%
개소수 2
 
2.3%
평화요양보호사교육원 1
 
1.2%
무진장요양보호사교육원 1
 
1.2%
남원한국요양보호사교육원 1
 
1.2%
메디컬요양보호사교육원 1
 
1.2%
정읍간호학원부설요양보호사교육원 1
 
1.2%
명 1
 
1.2%
Other values (50) 50
58.1%
2024-03-14T12:09:16.332036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 66
 
9.6%
호 61
 
8.9%
교 52
 
7.6%
육 51
 
7.4%
사 46
 
6.7%
보 46
 
6.7%
요 45
 
6.5%
양 45
 
6.5%
41
 
6.0%
간 16
 
2.3%
Other values (96) 219
31.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 643
93.5%
Space Separator 41
 
6.0%
Lowercase Letter 1
 
0.1%
Uppercase Letter 1
 
0.1%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 66
 
10.3%
호 61
 
9.5%
교 52
 
8.1%
육 51
 
7.9%
사 46
 
7.2%
보 46
 
7.2%
요 45
 
7.0%
양 45
 
7.0%
간 16
 
2.5%
부 16
 
2.5%
Other values (91) 199
30.9%
Space Separator
ValueCountFrequency (%)
41
100.0%
Lowercase Letter
ValueCountFrequency (%)
k 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
J 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 643
93.5%
Common 43
 
6.2%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 66
 
10.3%
호 61
 
9.5%
교 52
 
8.1%
육 51
 
7.9%
사 46
 
7.2%
보 46
 
7.2%
요 45
 
7.0%
양 45
 
7.0%
간 16
 
2.5%
부 16
 
2.5%
Other values (91) 199
30.9%
Common
ValueCountFrequency (%)
41
95.3%
( 1
 
2.3%
) 1
 
2.3%
Latin
ValueCountFrequency (%)
k 1
50.0%
J 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 643
93.5%
ASCII 45
 
6.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 66
 
10.3%
호 61
 
9.5%
교 52
 
8.1%
육 51
 
7.9%
사 46
 
7.2%
보 46
 
7.2%
요 45
 
7.0%
양 45
 
7.0%
간 16
 
2.5%
부 16
 
2.5%
Other values (91) 199
30.9%
ASCII
ValueCountFrequency (%)
41
91.1%
k 1
 
2.2%
J 1
 
2.2%
( 1
 
2.2%
) 1
 
2.2%

Unnamed: 2
Text

MISSING 

Distinct48
Distinct (%)100.0%
Missing1
Missing (%)2.0%
Memory size524.0 B
2024-03-14T12:09:16.629340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length93
Median length25
Mean length22.520833
Min length3

Characters and Unicode

Total characters1081
Distinct characters120
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
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 row소재지
2nd row67개소(전주21, 군산9, 익산10, 정읍4, 남원3, 김제3, 완주3, 진안2, 무주2, 장수2, 임실2, 순창2, 고창2, 부안2)
3rd row45개소(전주20, 군산6, 익산7, 정읍2, 남원2, 김제2, 완주1, 진안1, 무주0, 장수1, 임실0, 순창1, 고창1, 부안1) ▶ 73개소 중 28개소 폐지
4th row전주시 완산구 팔달로 212-8 (경원동3가)
5th row전주시 완산구 팔달로 250 (서노송동)
ValueCountFrequency (%)
전주시 18
 
7.6%
완산구 12
 
5.1%
익산시 8
 
3.4%
덕진구 6
 
2.5%
군산시 6
 
2.5%
팔달로 5
 
2.1%
3층 5
 
2.1%
익산대로 4
 
1.7%
무왕로 4
 
1.7%
2가 4
 
1.7%
Other values (150) 165
69.6%
2024-03-14T12:09:17.006898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
189
 
17.5%
1 49
 
4.5%
2 48
 
4.4%
로 41
 
3.8%
시 39
 
3.6%
산 39
 
3.6%
( 35
 
3.2%
동 35
 
3.2%
) 35
 
3.2%
, 34
 
3.1%
Other values (110) 537
49.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 563
52.1%
Decimal Number 212
 
19.6%
Space Separator 189
 
17.5%
Open Punctuation 35
 
3.2%
Close Punctuation 35
 
3.2%
Other Punctuation 34
 
3.1%
Dash Punctuation 11
 
1.0%
Other Symbol 1
 
0.1%
Control 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 41
 
7.3%
시 39
 
6.9%
산 39
 
6.9%
동 35
 
6.2%
주 27
 
4.8%
전 23
 
4.1%
구 19
 
3.4%
완 17
 
3.0%
가 15
 
2.7%
군 14
 
2.5%
Other values (93) 294
52.2%
Decimal Number
ValueCountFrequency (%)
1 49
23.1%
2 48
22.6%
3 27
12.7%
4 20
9.4%
7 15
 
7.1%
0 15
 
7.1%
6 11
 
5.2%
8 10
 
4.7%
5 9
 
4.2%
9 8
 
3.8%
Space Separator
ValueCountFrequency (%)
189
100.0%
Open Punctuation
ValueCountFrequency (%)
( 35
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%
Other Punctuation
ValueCountFrequency (%)
, 34
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Other Symbol
ValueCountFrequency (%)
▶ 1
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 563
52.1%
Common 518
47.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 41
 
7.3%
시 39
 
6.9%
산 39
 
6.9%
동 35
 
6.2%
주 27
 
4.8%
전 23
 
4.1%
구 19
 
3.4%
완 17
 
3.0%
가 15
 
2.7%
군 14
 
2.5%
Other values (93) 294
52.2%
Common
ValueCountFrequency (%)
189
36.5%
1 49
 
9.5%
2 48
 
9.3%
( 35
 
6.8%
) 35
 
6.8%
, 34
 
6.6%
3 27
 
5.2%
4 20
 
3.9%
7 15
 
2.9%
0 15
 
2.9%
Other values (7) 51
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 563
52.1%
ASCII 517
47.8%
Geometric Shapes 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
189
36.6%
1 49
 
9.5%
2 48
 
9.3%
( 35
 
6.8%
) 35
 
6.8%
, 34
 
6.6%
3 27
 
5.2%
4 20
 
3.9%
7 15
 
2.9%
0 15
 
2.9%
Other values (6) 50
 
9.7%
Hangul
ValueCountFrequency (%)
로 41
 
7.3%
시 39
 
6.9%
산 39
 
6.9%
동 35
 
6.2%
주 27
 
4.8%
전 23
 
4.1%
구 19
 
3.4%
완 17
 
3.0%
가 15
 
2.7%
군 14
 
2.5%
Other values (93) 294
52.2%
Geometric Shapes
ValueCountFrequency (%)
▶ 1
100.0%

Unnamed: 3
Text

MISSING 

Distinct43
Distinct (%)93.5%
Missing3
Missing (%)6.1%
Memory size524.0 B
2024-03-14T12:09:17.205215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length3
Mean length3.2826087
Min length3

Characters and Unicode

Total characters151
Distinct characters71
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

Unique40 ?
Unique (%)87.0%

Sample

1st row대표자
2nd row송제기
3rd row이연주
4th row박수임
5th row지정무
ValueCountFrequency (%)
배순주 2
 
4.3%
지정무 2
 
4.3%
정선희 2
 
4.3%
이태영 1
 
2.1%
오규만 1
 
2.1%
김재홍 1
 
2.1%
박선애 1
 
2.1%
정성훈 1
 
2.1%
박진표 1
 
2.1%
최문성 1
 
2.1%
Other values (34) 34
72.3%
2024-03-14T12:09:17.522344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
정 11
 
7.3%
성 7
 
4.6%
김 7
 
4.6%
박 6
 
4.0%
이 6
 
4.0%
희 5
 
3.3%
순 5
 
3.3%
선 5
 
3.3%
기 5
 
3.3%
진 4
 
2.6%
Other values (61) 90
59.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 148
98.0%
Space Separator 1
 
0.7%
Close Punctuation 1
 
0.7%
Open Punctuation 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 11
 
7.4%
성 7
 
4.7%
김 7
 
4.7%
박 6
 
4.1%
이 6
 
4.1%
희 5
 
3.4%
순 5
 
3.4%
선 5
 
3.4%
기 5
 
3.4%
진 4
 
2.7%
Other values (58) 87
58.8%
Space Separator
ValueCountFrequency (%)
1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 148
98.0%
Common 3
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 11
 
7.4%
성 7
 
4.7%
김 7
 
4.7%
박 6
 
4.1%
이 6
 
4.1%
희 5
 
3.4%
순 5
 
3.4%
선 5
 
3.4%
기 5
 
3.4%
진 4
 
2.7%
Other values (58) 87
58.8%
Common
ValueCountFrequency (%)
1
33.3%
) 1
33.3%
( 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 148
98.0%
ASCII 3
 
2.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
정 11
 
7.4%
성 7
 
4.7%
김 7
 
4.7%
박 6
 
4.1%
이 6
 
4.1%
희 5
 
3.4%
순 5
 
3.4%
선 5
 
3.4%
기 5
 
3.4%
진 4
 
2.7%
Other values (58) 87
58.8%
ASCII
ValueCountFrequency (%)
1
33.3%
) 1
33.3%
( 1
33.3%

Unnamed: 4
Categorical

Distinct12
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Memory size524.0 B
40명
17 
30명
17 
<NA>
3 
20명
3 
35명
2 
Other values (7)
7 

Length

Max length4
Median length3
Mean length3.0408163
Min length2

Unique

Unique7 ?
Unique (%)14.3%

Sample

1st row<NA>
2nd row정원
3rd row<NA>
4th row<NA>
5th row40명

Common Values

ValueCountFrequency (%)
40명 17
34.7%
30명 17
34.7%
<NA> 3
 
6.1%
20명 3
 
6.1%
35명 2
 
4.1%
정원 1
 
2.0%
34명 1
 
2.0%
36명 1
 
2.0%
26명 1
 
2.0%
16명 1
 
2.0%
Other values (2) 2
 
4.1%

Length

2024-03-14T12:09:17.649469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
40명 17
34.7%
30명 17
34.7%
na 3
 
6.1%
20명 3
 
6.1%
35명 2
 
4.1%
정원 1
 
2.0%
34명 1
 
2.0%
36명 1
 
2.0%
26명 1
 
2.0%
16명 1
 
2.0%
Other values (2) 2
 
4.1%

Unnamed: 5
Text

MISSING 

Distinct46
Distinct (%)100.0%
Missing3
Missing (%)6.1%
Memory size524.0 B
2024-03-14T12:09:17.833628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length8
Mean length8.7608696
Min length4

Characters and Unicode

Total characters403
Distinct characters17
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

Unique46 ?
Unique (%)100.0%

Sample

1st row전화번호
2nd row282-6500
3rd row285-1999 285-1990
4th row545-9888
5th row851-2411
ValueCountFrequency (%)
229-3144 1
 
2.0%
287-9770 1
 
2.0%
232-0230 1
 
2.0%
538-3663 1
 
2.0%
225-1441 1
 
2.0%
535-0297 1
 
2.0%
446-5055 1
 
2.0%
626-2233 1
 
2.0%
433-1550 1
 
2.0%
277-7701 1
 
2.0%
Other values (39) 39
79.6%
2024-03-14T12:09:18.128721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 59
14.6%
- 50
12.4%
3 38
9.4%
8 37
9.2%
4 36
8.9%
5 36
8.9%
1 34
8.4%
0 33
8.2%
9 25
6.2%
7 25
6.2%
Other values (7) 30
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 345
85.6%
Dash Punctuation 50
 
12.4%
Other Letter 4
 
1.0%
Control 3
 
0.7%
Math Symbol 1
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 59
17.1%
3 38
11.0%
8 37
10.7%
4 36
10.4%
5 36
10.4%
1 34
9.9%
0 33
9.6%
9 25
7.2%
7 25
7.2%
6 22
 
6.4%
Other Letter
ValueCountFrequency (%)
전 1
25.0%
화 1
25.0%
번 1
25.0%
호 1
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%
Control
ValueCountFrequency (%)
3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 399
99.0%
Hangul 4
 
1.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 59
14.8%
- 50
12.5%
3 38
9.5%
8 37
9.3%
4 36
9.0%
5 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%
Hangul
ValueCountFrequency (%)
전 1
25.0%
화 1
25.0%
번 1
25.0%
호 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 399
99.0%
Hangul 4
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 59
14.8%
- 50
12.5%
3 38
9.5%
8 37
9.3%
4 36
9.0%
5 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%
Hangul
ValueCountFrequency (%)
전 1
25.0%
화 1
25.0%
번 1
25.0%
호 1
25.0%

Unnamed: 6
Text

MISSING 

Distinct44
Distinct (%)97.8%
Missing4
Missing (%)8.2%
Memory size524.0 B
2024-03-14T12:09:18.328076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length8
Mean length8.1111111
Min length3

Characters and Unicode

Total characters365
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
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 rowFAX
2nd row282-6501
3rd row285-6991
4th row545-9887
5th row842-2411
ValueCountFrequency (%)
853-8334 2
 
4.4%
231-6780 1
 
2.2%
277-7702 1
 
2.2%
255-4748 1
 
2.2%
858-9841 1
 
2.2%
538-3664 1
 
2.2%
227-1010 1
 
2.2%
538-5371 1
 
2.2%
445-5056 1
 
2.2%
626-2283 1
 
2.2%
Other values (34) 34
75.6%
2024-03-14T12:09:18.636312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 46
12.6%
2 46
12.6%
1 39
10.7%
3 38
10.4%
5 36
9.9%
8 35
9.6%
4 30
8.2%
0 28
7.7%
6 25
6.8%
7 20
5.5%
Other values (4) 22
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316
86.6%
Dash Punctuation 46
 
12.6%
Uppercase Letter 3
 
0.8%

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%
Uppercase Letter
ValueCountFrequency (%)
F 1
33.3%
A 1
33.3%
X 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 362
99.2%
Latin 3
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
- 46
12.7%
2 46
12.7%
1 39
10.8%
3 38
10.5%
5 36
9.9%
8 35
9.7%
4 30
8.3%
0 28
7.7%
6 25
6.9%
7 20
5.5%
Latin
ValueCountFrequency (%)
F 1
33.3%
A 1
33.3%
X 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 365
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 46
12.6%
2 46
12.6%
1 39
10.7%
3 38
10.4%
5 36
9.9%
8 35
9.6%
4 30
8.2%
0 28
7.7%
6 25
6.8%
7 20
5.5%
Other values (4) 22
6.0%

Unnamed: 7
Text

MISSING 

Distinct39
Distinct (%)83.0%
Missing2
Missing (%)4.1%
Memory size524.0 B
2024-03-14T12:09:18.834488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length7
Mean length7.0425532
Min length4

Characters and Unicode

Total characters331
Distinct characters20
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

Unique33 ?
Unique (%)70.2%

Sample

1st row2014. 11월 현재
2nd row우편번호
3rd row560-023
4th row560-913
5th row576-805
ValueCountFrequency (%)
560-023 4
 
8.2%
561-870 2
 
4.1%
573-420 2
 
4.1%
561-820 2
 
4.1%
570-993 2
 
4.1%
570-982 2
 
4.1%
560-021 1
 
2.0%
597-842 1
 
2.0%
560-865 1
 
2.0%
565-907 1
 
2.0%
Other values (31) 31
63.3%
2024-03-14T12:09:19.103178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 60
18.1%
5 56
16.9%
- 45
13.6%
7 30
9.1%
6 26
7.9%
8 24
 
7.3%
1 22
 
6.6%
2 18
 
5.4%
3 16
 
4.8%
9 16
 
4.8%
Other values (10) 18
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 276
83.4%
Dash Punctuation 45
 
13.6%
Other Letter 7
 
2.1%
Space Separator 2
 
0.6%
Other Punctuation 1
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 60
21.7%
5 56
20.3%
7 30
10.9%
6 26
9.4%
8 24
 
8.7%
1 22
 
8.0%
2 18
 
6.5%
3 16
 
5.8%
9 16
 
5.8%
4 8
 
2.9%
Other Letter
ValueCountFrequency (%)
우 1
14.3%
편 1
14.3%
번 1
14.3%
호 1
14.3%
월 1
14.3%
현 1
14.3%
재 1
14.3%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 324
97.9%
Hangul 7
 
2.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 60
18.5%
5 56
17.3%
- 45
13.9%
7 30
9.3%
6 26
8.0%
8 24
 
7.4%
1 22
 
6.8%
2 18
 
5.6%
3 16
 
4.9%
9 16
 
4.9%
Other values (3) 11
 
3.4%
Hangul
ValueCountFrequency (%)
우 1
14.3%
편 1
14.3%
번 1
14.3%
호 1
14.3%
월 1
14.3%
현 1
14.3%
재 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 324
97.9%
Hangul 7
 
2.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 60
18.5%
5 56
17.3%
- 45
13.9%
7 30
9.3%
6 26
8.0%
8 24
 
7.4%
1 22
 
6.8%
2 18
 
5.6%
3 16
 
4.9%
9 16
 
4.9%
Other values (3) 11
 
3.4%
Hangul
ValueCountFrequency (%)
우 1
14.3%
편 1
14.3%
번 1
14.3%
호 1
14.3%
월 1
14.3%
현 1
14.3%
재 1
14.3%

Correlations

2024-03-14T12:09:19.185720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
요양보호사 교육원Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7
요양보호사 교육원1.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 11.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 21.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 31.0001.0001.0001.0000.9971.0000.9920.980
Unnamed: 41.0001.0001.0000.9971.0001.0000.9980.929
Unnamed: 51.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 61.0001.0001.0000.9920.9981.0001.0000.994
Unnamed: 71.0001.0001.0000.9800.9291.0000.9941.000

Missing values

2024-03-14T12:09:15.011919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T12:09:15.125343image/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.
2024-03-14T12:09:15.239438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

요양보호사 교육원Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7
0<NA><NA><NA><NA><NA><NA><NA>2014. 11월 현재
1번호명 칭소재지대표자정원전화번호FAX우편번호
2<NA>적정 요양보호사교육원 개소수67개소(전주21, 군산9, 익산10, 정읍4, 남원3, 김제3, 완주3, 진안2, 무주2, 장수2, 임실2, 순창2, 고창2, 부안2)<NA><NA><NA><NA><NA>
3<NA>현재 운영 중인 교육원 개소수45개소(전주20, 군산6, 익산7, 정읍2, 남원2, 김제2, 완주1, 진안1, 무주0, 장수1, 임실0, 순창1, 고창1, 부안1) ▶ 73개소 중 28개소 폐지<NA><NA><NA><NA><NA>
41탑클래스전주시 완산구 팔달로 212-8 (경원동3가)송제기40명282-6500282-6501560-023
52전주성신간호학원 부설 요양보호사교육원전주시 완산구 팔달로 250 (서노송동)이연주40명285-1999 285-1990285-6991560-913
63김제성모 요양보호사교육원김제시 동서로 222 (요촌동)박수임34명545-9888545-9887576-805
74이리간호교육원 부설 요양보호사교육원익산시 익산대로 58-7 (평화동)지정무40명851-2411842-2411570-010
85군산간호교육원 부설 요양보호사교육원군산시 경포천로 153 (경장동)지정무30명446-1123446-1126573-420
96온누리요양보호사교육원전주시 완산구 팔달로 202-16박흥순30명231-4554231-4553560-023
요양보호사 교육원Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7
3936봉동간호학원 부설 요양보호사교육원완주군 봉동읍 봉동로 140(장기리 225-1)배순주30명261-1125261-1165565-907
4037남전주요양보호사교육원전주시 완산구 모악로 4692(평화동2가)김송이20명221-89980505-300-8900560-865
4138늘푸른요양보호사교육원장수군 장계면 한들로 107신유림25명010-7384-0825353-6699597-842
4239솔빛간호학원부설요양보호사교육원전주시 덕진구 솔내로 164(송천동 2가, 4층)이수정30명274-9858275-1089561-820
4340전주안골간호학원부설요양보호사교육원전주시 덕진구 안덕원로 262, 5층배순주30명246-1160243-1165561-870
4441평화간호전문학원 요양보호사교육원익산시 무왕로 1073, 4층박월순30명833-4477841-2323570-982
4542중앙간호학원요양보호사교육원군산시 공단대로 381 4층오규만30명471-2970471-2971573-871
4643제이성모요양보호사교육원익산시 무왕로 1071, 3층이태영30명833-3370853-8334570-982
4744고창요양보호사교육원고창군 고창읍 중앙로 180, 3층김재홍30명563-0038563-0018585-808
4845아중안골요양보호사교육원전주시 덕진구 견훤로 289, 3층김은희35명282-2323242-1441561-870