Overview

Dataset statistics

Number of variables9
Number of observations48
Missing cells20
Missing cells (%)4.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory74.8 B

Variable types

Text8
Categorical1

Dataset

Description요양보호사교육원현황게재지정제152월
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202344

Alerts

요양보호사 교육원 has 3 (6.2%) missing valuesMissing
Unnamed: 1 has 1 (2.1%) missing valuesMissing
Unnamed: 2 has 1 (2.1%) missing valuesMissing
Unnamed: 3 has 3 (6.2%) missing valuesMissing
Unnamed: 4 has 3 (6.2%) missing valuesMissing
Unnamed: 6 has 3 (6.2%) missing valuesMissing
Unnamed: 7 has 4 (8.3%) missing valuesMissing
Unnamed: 8 has 2 (4.2%) missing valuesMissing

Reproduction

Analysis started2024-03-13 23:55:24.661977
Analysis finished2024-03-13 23:55:25.564384
Duration0.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

요양보호사 교육원
Text

MISSING 

Distinct45
Distinct (%)100.0%
Missing3
Missing (%)6.2%
Memory size516.0 B
2024-03-14T08:55:25.707442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.8
Min length1

Characters and Unicode

Total characters81
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

Unique45 ?
Unique (%)100.0%

Sample

1st row번호
2nd row1
3rd row2
4th row3
5th row4
ValueCountFrequency (%)
9 1
 
2.2%
23 1
 
2.2%
24 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%
Other values (35) 35
77.8%
2024-03-14T08:55:25.979345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 15
18.5%
1 15
18.5%
3 15
18.5%
4 10
12.3%
9 4
 
4.9%
0 4
 
4.9%
5 4
 
4.9%
6 4
 
4.9%
7 4
 
4.9%
8 4
 
4.9%
Other values (2) 2
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79
97.5%
Other Letter 2
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 15
19.0%
1 15
19.0%
3 15
19.0%
4 10
12.7%
9 4
 
5.1%
0 4
 
5.1%
5 4
 
5.1%
6 4
 
5.1%
7 4
 
5.1%
8 4
 
5.1%
Other Letter
ValueCountFrequency (%)
번 1
50.0%
호 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 79
97.5%
Hangul 2
 
2.5%

Most frequent character per script

Common
ValueCountFrequency (%)
2 15
19.0%
1 15
19.0%
3 15
19.0%
4 10
12.7%
9 4
 
5.1%
0 4
 
5.1%
5 4
 
5.1%
6 4
 
5.1%
7 4
 
5.1%
8 4
 
5.1%
Hangul
ValueCountFrequency (%)
번 1
50.0%
호 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79
97.5%
Hangul 2
 
2.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 15
19.0%
1 15
19.0%
3 15
19.0%
4 10
12.7%
9 4
 
5.1%
0 4
 
5.1%
5 4
 
5.1%
6 4
 
5.1%
7 4
 
5.1%
8 4
 
5.1%
Hangul
ValueCountFrequency (%)
번 1
50.0%
호 1
50.0%

Unnamed: 1
Text

MISSING 

Distinct47
Distinct (%)100.0%
Missing1
Missing (%)2.1%
Memory size516.0 B
2024-03-14T08:55:26.185332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length20
Mean length14.702128
Min length4

Characters and Unicode

Total characters691
Distinct characters100
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

Unique47 ?
Unique (%)100.0%

Sample

1st row명 칭
2nd row적정 요양보호사교육원 개소수
3rd row현재 운영 중인 교육원 개소수
4th row군산성모간호전문학원 부설 성모요양보호사교육원
5th row전주성모간호교육원 부설 전주성모요양보호사교육원
ValueCountFrequency (%)
요양보호사교육원 14
 
16.7%
부설 11
 
13.1%
평생교육원 2
 
2.4%
개소수 2
 
2.4%
메디컬요양보호사교육원 1
 
1.2%
미래간호학원 1
 
1.2%
탑클래스간호학원 1
 
1.2%
송천 1
 
1.2%
남원한국요양보호사교육원 1
 
1.2%
평화요양보호사교육원 1
 
1.2%
Other values (49) 49
58.3%
2024-03-14T08:55:26.502934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 66
 
9.6%
호 61
 
8.8%
교 51
 
7.4%
육 50
 
7.2%
48
 
6.9%
보 45
 
6.5%
요 44
 
6.4%
양 44
 
6.4%
사 44
 
6.4%
간 17
 
2.5%
Other values (90) 221
32.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 639
92.5%
Space Separator 48
 
6.9%
Uppercase Letter 1
 
0.1%
Lowercase 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 51
 
8.0%
육 50
 
7.8%
보 45
 
7.0%
요 44
 
6.9%
양 44
 
6.9%
사 44
 
6.9%
간 17
 
2.7%
부 16
 
2.5%
Other values (85) 201
31.5%
Space Separator
ValueCountFrequency (%)
48
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 639
92.5%
Common 50
 
7.2%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 66
 
10.3%
호 61
 
9.5%
교 51
 
8.0%
육 50
 
7.8%
보 45
 
7.0%
요 44
 
6.9%
양 44
 
6.9%
사 44
 
6.9%
간 17
 
2.7%
부 16
 
2.5%
Other values (85) 201
31.5%
Common
ValueCountFrequency (%)
48
96.0%
( 1
 
2.0%
) 1
 
2.0%
Latin
ValueCountFrequency (%)
J 1
50.0%
k 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 639
92.5%
ASCII 52
 
7.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 66
 
10.3%
호 61
 
9.5%
교 51
 
8.0%
육 50
 
7.8%
보 45
 
7.0%
요 44
 
6.9%
양 44
 
6.9%
사 44
 
6.9%
간 17
 
2.7%
부 16
 
2.5%
Other values (85) 201
31.5%
ASCII
ValueCountFrequency (%)
48
92.3%
J 1
 
1.9%
k 1
 
1.9%
( 1
 
1.9%
) 1
 
1.9%

Unnamed: 2
Text

MISSING 

Distinct47
Distinct (%)100.0%
Missing1
Missing (%)2.1%
Memory size516.0 B
2024-03-14T08:55:26.822515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length93
Median length26
Mean length23.553191
Min length9

Characters and Unicode

Total characters1107
Distinct characters127
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
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 row소 재 지
2nd row65개소(전주21, 군산9, 익산10, 정읍3, 남원2, 김제3, 완주3, 진안2, 무주2, 장수2, 임실2, 순창2, 고창2, 부안2)
3rd row44개소(전주19, 군산6, 익산8, 정읍2, 남원2, 김제2, 완주1, 진안0, 무주0, 장수1, 임실0, 순창1, 고창1, 부안1) ▶ 73개소 중 28개소 폐지
4th row군산시 대학로 321 (나운동)
5th row전주시 완산구 어진길 114 (경원동 1가)
ValueCountFrequency (%)
전주시 19
 
7.8%
완산구 12
 
4.9%
익산시 8
 
3.3%
3층 7
 
2.9%
덕진구 7
 
2.9%
군산시 6
 
2.4%
무왕로 4
 
1.6%
2가 4
 
1.6%
익산대로 4
 
1.6%
4층 4
 
1.6%
Other values (152) 170
69.4%
2024-03-14T08:55:27.274386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
202
 
18.2%
2 48
 
4.3%
1 47
 
4.2%
로 41
 
3.7%
시 39
 
3.5%
) 38
 
3.4%
( 38
 
3.4%
산 37
 
3.3%
, 36
 
3.3%
동 33
 
3.0%
Other values (117) 548
49.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 572
51.7%
Decimal Number 208
 
18.8%
Space Separator 202
 
18.2%
Close Punctuation 38
 
3.4%
Open Punctuation 38
 
3.4%
Other Punctuation 37
 
3.3%
Dash Punctuation 7
 
0.6%
Uppercase Letter 3
 
0.3%
Control 1
 
0.1%
Other Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 41
 
7.2%
시 39
 
6.8%
산 37
 
6.5%
동 33
 
5.8%
주 28
 
4.9%
전 23
 
4.0%
층 19
 
3.3%
구 19
 
3.3%
완 16
 
2.8%
익 14
 
2.4%
Other values (96) 303
53.0%
Decimal Number
ValueCountFrequency (%)
2 48
23.1%
1 47
22.6%
3 27
13.0%
4 22
10.6%
0 14
 
6.7%
8 11
 
5.3%
7 11
 
5.3%
6 11
 
5.3%
5 9
 
4.3%
9 8
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
C 1
33.3%
Y 1
33.3%
B 1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 36
97.3%
. 1
 
2.7%
Space Separator
ValueCountFrequency (%)
202
100.0%
Close Punctuation
ValueCountFrequency (%)
) 38
100.0%
Open Punctuation
ValueCountFrequency (%)
( 38
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Control
ValueCountFrequency (%)
1
100.0%
Other Symbol
ValueCountFrequency (%)
▶ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 572
51.7%
Common 532
48.1%
Latin 3
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 41
 
7.2%
시 39
 
6.8%
산 37
 
6.5%
동 33
 
5.8%
주 28
 
4.9%
전 23
 
4.0%
층 19
 
3.3%
구 19
 
3.3%
완 16
 
2.8%
익 14
 
2.4%
Other values (96) 303
53.0%
Common
ValueCountFrequency (%)
202
38.0%
2 48
 
9.0%
1 47
 
8.8%
) 38
 
7.1%
( 38
 
7.1%
, 36
 
6.8%
3 27
 
5.1%
4 22
 
4.1%
0 14
 
2.6%
8 11
 
2.1%
Other values (8) 49
 
9.2%
Latin
ValueCountFrequency (%)
C 1
33.3%
Y 1
33.3%
B 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 572
51.7%
ASCII 534
48.2%
Geometric Shapes 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
202
37.8%
2 48
 
9.0%
1 47
 
8.8%
) 38
 
7.1%
( 38
 
7.1%
, 36
 
6.7%
3 27
 
5.1%
4 22
 
4.1%
0 14
 
2.6%
8 11
 
2.1%
Other values (10) 51
 
9.6%
Hangul
ValueCountFrequency (%)
로 41
 
7.2%
시 39
 
6.8%
산 37
 
6.5%
동 33
 
5.8%
주 28
 
4.9%
전 23
 
4.0%
층 19
 
3.3%
구 19
 
3.3%
완 16
 
2.8%
익 14
 
2.4%
Other values (96) 303
53.0%
Geometric Shapes
ValueCountFrequency (%)
▶ 1
100.0%

Unnamed: 3
Text

MISSING 

Distinct43
Distinct (%)95.6%
Missing3
Missing (%)6.2%
Memory size516.0 B
2024-03-14T08:55:27.461616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length3
Mean length3.3777778
Min length3

Characters and Unicode

Total characters152
Distinct characters66
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

Unique41 ?
Unique (%)91.1%

Sample

1st row대표자
2nd row정봉성
3rd row윤석길
4th row기성옥
5th row박성희
ValueCountFrequency (%)
지정무 2
 
4.3%
배순주 2
 
4.3%
백지연 1
 
2.1%
전주기독학원(윤정길 1
 
2.1%
정성훈 1
 
2.1%
기정임 1
 
2.1%
김송이 1
 
2.1%
박진표 1
 
2.1%
최문성 1
 
2.1%
이서현 1
 
2.1%
Other values (35) 35
74.5%
2024-03-14T08:55:27.760799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
정 12
 
7.9%
성 8
 
5.3%
김 7
 
4.6%
이 6
 
3.9%
박 6
 
3.9%
순 5
 
3.3%
기 5
 
3.3%
숙 4
 
2.6%
희 4
 
2.6%
옥 4
 
2.6%
Other values (56) 91
59.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 148
97.4%
Open Punctuation 1
 
0.7%
Close Punctuation 1
 
0.7%
Space Separator 1
 
0.7%
Control 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 12
 
8.1%
성 8
 
5.4%
김 7
 
4.7%
이 6
 
4.1%
박 6
 
4.1%
순 5
 
3.4%
기 5
 
3.4%
숙 4
 
2.7%
희 4
 
2.7%
옥 4
 
2.7%
Other values (52) 87
58.8%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 148
97.4%
Common 4
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 12
 
8.1%
성 8
 
5.4%
김 7
 
4.7%
이 6
 
4.1%
박 6
 
4.1%
순 5
 
3.4%
기 5
 
3.4%
숙 4
 
2.7%
희 4
 
2.7%
옥 4
 
2.7%
Other values (52) 87
58.8%
Common
ValueCountFrequency (%)
( 1
25.0%
) 1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 148
97.4%
ASCII 4
 
2.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
정 12
 
8.1%
성 8
 
5.4%
김 7
 
4.7%
이 6
 
4.1%
박 6
 
4.1%
순 5
 
3.4%
기 5
 
3.4%
숙 4
 
2.7%
희 4
 
2.7%
옥 4
 
2.7%
Other values (52) 87
58.8%
ASCII
ValueCountFrequency (%)
( 1
25.0%
) 1
25.0%
1
25.0%
1
25.0%

Unnamed: 4
Text

MISSING 

Distinct34
Distinct (%)75.6%
Missing3
Missing (%)6.2%
Memory size516.0 B
2024-03-14T08:55:27.923350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.7111111
Min length3

Characters and Unicode

Total characters437
Distinct characters14
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

Unique30 ?
Unique (%)66.7%

Sample

1st row지정일
2nd row2008.02.19
3rd row2008.02.28
4th row2008.02.28
5th row2008.10.01
ValueCountFrequency (%)
2010.10.28 7
 
15.6%
2010.10.20 4
 
8.9%
2010.10 2
 
4.4%
2008.02.28 2
 
4.4%
2014.12.11 1
 
2.2%
2014.12.02 1
 
2.2%
2014.11.07 1
 
2.2%
2014.10.22 1
 
2.2%
2014.10.15 1
 
2.2%
2012.08.01 1
 
2.2%
Other values (24) 24
53.3%
2024-03-14T08:55:28.191701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 120
27.5%
1 88
20.1%
. 88
20.1%
2 78
17.8%
8 20
 
4.6%
4 13
 
3.0%
3 8
 
1.8%
5 6
 
1.4%
9 6
 
1.4%
7 4
 
0.9%
Other values (4) 6
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 346
79.2%
Other Punctuation 88
 
20.1%
Other Letter 3
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 120
34.7%
1 88
25.4%
2 78
22.5%
8 20
 
5.8%
4 13
 
3.8%
3 8
 
2.3%
5 6
 
1.7%
9 6
 
1.7%
7 4
 
1.2%
6 3
 
0.9%
Other Letter
ValueCountFrequency (%)
지 1
33.3%
정 1
33.3%
일 1
33.3%
Other Punctuation
ValueCountFrequency (%)
. 88
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 434
99.3%
Hangul 3
 
0.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 120
27.6%
1 88
20.3%
. 88
20.3%
2 78
18.0%
8 20
 
4.6%
4 13
 
3.0%
3 8
 
1.8%
5 6
 
1.4%
9 6
 
1.4%
7 4
 
0.9%
Hangul
ValueCountFrequency (%)
지 1
33.3%
정 1
33.3%
일 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 434
99.3%
Hangul 3
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 120
27.6%
1 88
20.3%
. 88
20.3%
2 78
18.0%
8 20
 
4.6%
4 13
 
3.0%
3 8
 
1.8%
5 6
 
1.4%
9 6
 
1.4%
7 4
 
0.9%
Hangul
ValueCountFrequency (%)
지 1
33.3%
정 1
33.3%
일 1
33.3%

Unnamed: 5
Categorical

Distinct12
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size516.0 B
30명
19 
40명
14 
<NA>
3 
35명
2 
20명
2 
Other values (7)
8 

Length

Max length4
Median length3
Mean length3.0416667
Min length2

Unique

Unique6 ?
Unique (%)12.5%

Sample

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

Common Values

ValueCountFrequency (%)
30명 19
39.6%
40명 14
29.2%
<NA> 3
 
6.2%
35명 2
 
4.2%
20명 2
 
4.2%
15명 2
 
4.2%
정원 1
 
2.1%
36명 1
 
2.1%
34명 1
 
2.1%
26명 1
 
2.1%
Other values (2) 2
 
4.2%

Length

2024-03-14T08:55:28.308575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
30명 19
39.6%
40명 14
29.2%
na 3
 
6.2%
35명 2
 
4.2%
20명 2
 
4.2%
15명 2
 
4.2%
정원 1
 
2.1%
36명 1
 
2.1%
34명 1
 
2.1%
26명 1
 
2.1%
Other values (2) 2
 
4.2%

Unnamed: 6
Text

MISSING 

Distinct45
Distinct (%)100.0%
Missing3
Missing (%)6.2%
Memory size516.0 B
2024-03-14T08:55:28.481531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length8
Mean length8.5777778
Min length4

Characters and Unicode

Total characters386
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

Unique45 ?
Unique (%)100.0%

Sample

1st row전화번호
2nd row471-8420
3rd row283-6662~5
4th row858-9840
5th row632-2993 010-9004-4563
ValueCountFrequency (%)
851-2411 1
 
2.1%
261-1125 1
 
2.1%
229-6229 1
 
2.1%
535-0297 1
 
2.1%
446-5055 1
 
2.1%
626-2233 1
 
2.1%
277-7701 1
 
2.1%
272-4747 1
 
2.1%
280-5271 1
 
2.1%
582-6222 1
 
2.1%
Other values (37) 37
78.7%
2024-03-14T08:55:28.806517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 59
15.3%
- 48
12.4%
8 36
9.3%
3 35
9.1%
5 35
9.1%
4 33
8.5%
1 30
7.8%
0 30
7.8%
6 26
6.7%
9 24
6.2%
Other values (7) 30
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 331
85.8%
Dash Punctuation 48
 
12.4%
Other Letter 4
 
1.0%
Control 2
 
0.5%
Math Symbol 1
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 59
17.8%
8 36
10.9%
3 35
10.6%
5 35
10.6%
4 33
10.0%
1 30
9.1%
0 30
9.1%
6 26
7.9%
9 24
7.3%
7 23
 
6.9%
Other Letter
ValueCountFrequency (%)
전 1
25.0%
화 1
25.0%
번 1
25.0%
호 1
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%
Control
ValueCountFrequency (%)
2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 382
99.0%
Hangul 4
 
1.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 59
15.4%
- 48
12.6%
8 36
9.4%
3 35
9.2%
5 35
9.2%
4 33
8.6%
1 30
7.9%
0 30
7.9%
6 26
6.8%
9 24
6.3%
Other values (3) 26
6.8%
Hangul
ValueCountFrequency (%)
전 1
25.0%
화 1
25.0%
번 1
25.0%
호 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 382
99.0%
Hangul 4
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 59
15.4%
- 48
12.6%
8 36
9.4%
3 35
9.2%
5 35
9.2%
4 33
8.6%
1 30
7.9%
0 30
7.9%
6 26
6.8%
9 24
6.3%
Other values (3) 26
6.8%
Hangul
ValueCountFrequency (%)
전 1
25.0%
화 1
25.0%
번 1
25.0%
호 1
25.0%

Unnamed: 7
Text

MISSING 

Distinct43
Distinct (%)97.7%
Missing4
Missing (%)8.3%
Memory size516.0 B
2024-03-14T08:55:28.992729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length8
Mean length8.1136364
Min length3

Characters and Unicode

Total characters357
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

Unique42 ?
Unique (%)95.5%

Sample

1st rowFAX
2nd row471-8425
3rd row286-6661
4th row858-9841
5th row631-2993
ValueCountFrequency (%)
853-8334 2
 
4.5%
0505-300-8900 1
 
2.3%
910-9895 1
 
2.3%
538-5371 1
 
2.3%
445-5056 1
 
2.3%
626-2283 1
 
2.3%
277-7702 1
 
2.3%
255-4748 1
 
2.3%
0505-582-1233 1
 
2.3%
285-0230 1
 
2.3%
Other values (33) 33
75.0%
2024-03-14T08:55:29.378542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 47
13.2%
- 45
12.6%
5 36
10.1%
8 35
9.8%
3 35
9.8%
1 35
9.8%
4 29
8.1%
0 28
7.8%
6 27
7.6%
9 19
5.3%
Other values (4) 21
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 309
86.6%
Dash Punctuation 45
 
12.6%
Uppercase Letter 3
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 47
15.2%
5 36
11.7%
8 35
11.3%
3 35
11.3%
1 35
11.3%
4 29
9.4%
0 28
9.1%
6 27
8.7%
9 19
6.1%
7 18
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
F 1
33.3%
A 1
33.3%
X 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 354
99.2%
Latin 3
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
2 47
13.3%
- 45
12.7%
5 36
10.2%
8 35
9.9%
3 35
9.9%
1 35
9.9%
4 29
8.2%
0 28
7.9%
6 27
7.6%
9 19
5.4%
Latin
ValueCountFrequency (%)
F 1
33.3%
A 1
33.3%
X 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 357
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 47
13.2%
- 45
12.6%
5 36
10.1%
8 35
9.8%
3 35
9.8%
1 35
9.8%
4 29
8.1%
0 28
7.8%
6 27
7.6%
9 19
5.3%
Other values (4) 21
5.9%

Unnamed: 8
Text

MISSING 

Distinct37
Distinct (%)80.4%
Missing2
Missing (%)4.2%
Memory size516.0 B
2024-03-14T08:55:29.559462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length7
Mean length7.0217391
Min length4

Characters and Unicode

Total characters323
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

Unique29 ?
Unique (%)63.0%

Sample

1st row2015. 2월 현재
2nd row우편번호
3rd row573-870
4th row560-020
5th row570-993
ValueCountFrequency (%)
560-023 3
 
6.2%
570-982 2
 
4.2%
561-870 2
 
4.2%
561-820 2
 
4.2%
560-865 2
 
4.2%
561-810 2
 
4.2%
573-420 2
 
4.2%
570-993 2
 
4.2%
560-021 1
 
2.1%
560-842 1
 
2.1%
Other values (29) 29
60.4%
2024-03-14T08:55:29.842820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 57
17.6%
0 56
17.3%
- 44
13.6%
7 28
8.7%
6 26
8.0%
8 26
8.0%
1 21
 
6.5%
2 17
 
5.3%
3 16
 
5.0%
9 15
 
4.6%
Other values (10) 17
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 269
83.3%
Dash Punctuation 44
 
13.6%
Other Letter 7
 
2.2%
Space Separator 2
 
0.6%
Other Punctuation 1
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 57
21.2%
0 56
20.8%
7 28
10.4%
6 26
9.7%
8 26
9.7%
1 21
 
7.8%
2 17
 
6.3%
3 16
 
5.9%
9 15
 
5.6%
4 7
 
2.6%
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 (%)
- 44
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 316
97.8%
Hangul 7
 
2.2%

Most frequent character per script

Common
ValueCountFrequency (%)
5 57
18.0%
0 56
17.7%
- 44
13.9%
7 28
8.9%
6 26
8.2%
8 26
8.2%
1 21
 
6.6%
2 17
 
5.4%
3 16
 
5.1%
9 15
 
4.7%
Other values (3) 10
 
3.2%
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 316
97.8%
Hangul 7
 
2.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 57
18.0%
0 56
17.7%
- 44
13.9%
7 28
8.9%
6 26
8.2%
8 26
8.2%
1 21
 
6.6%
2 17
 
5.4%
3 16
 
5.1%
9 15
 
4.7%
Other values (3) 10
 
3.2%
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

2024-03-14T08:55:29.929542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
요양보호사 교육원Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
요양보호사 교육원1.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 11.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 21.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 31.0001.0001.0001.0000.9880.9961.0000.9860.972
Unnamed: 41.0001.0001.0000.9881.0000.9301.0000.9900.712
Unnamed: 51.0001.0001.0000.9960.9301.0001.0000.9930.815
Unnamed: 61.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 71.0001.0001.0000.9860.9900.9931.0001.0000.994
Unnamed: 81.0001.0001.0000.9720.7120.8151.0000.9941.000

Missing values

2024-03-14T08:55:25.211356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T08:55:25.326881image/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.
2024-03-14T08:55:25.464426image/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: 7Unnamed: 8
0<NA><NA><NA><NA><NA><NA><NA><NA>2015. 2월 현재
1번호명 칭소 재 지대표자지정일정원전화번호FAX우편번호
2<NA>적정 요양보호사교육원 개소수65개소(전주21, 군산9, 익산10, 정읍3, 남원2, 김제3, 완주3, 진안2, 무주2, 장수2, 임실2, 순창2, 고창2, 부안2)<NA><NA><NA><NA><NA><NA>
3<NA>현재 운영 중인 교육원 개소수44개소(전주19, 군산6, 익산8, 정읍2, 남원2, 김제2, 완주1, 진안0, 무주0, 장수1, 임실0, 순창1, 고창1, 부안1) ▶ 73개소 중 28개소 폐지<NA><NA><NA><NA><NA><NA>
41군산성모간호전문학원 부설 성모요양보호사교육원군산시 대학로 321 (나운동)정봉성2008.02.1940명471-8420471-8425573-870
52전주성모간호교육원 부설 전주성모요양보호사교육원전주시 완산구 어진길 114 (경원동 1가)윤석길2008.02.2835명283-6662~5286-6661560-020
63익산간호요양보호사교육원익산시 익산대로 133-2 (창인동 2가)기성옥2008.02.2840명858-9840858-9841570-993
74남원간호요양보호사교육원남원시 의총로 77. 3층 (왕정동)박성희2008.10.0136명632-2993 010-9004-4563631-2993590-120
85목민요양보호사교육원전주시 완산구 용머리로 203 (서완산동 2가)김옥숙2009.09.0630명229-3144229-3147560-152
96김제성모 요양보호사교육원김제시 동서로 222 (요촌동)박수임2010.01.34명545-9888545-9887576-805
요양보호사 교육원Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
3835솔빛간호학원부설요양보호사교육원전주시 덕진구 솔내로 164(송천동 2가, 4층)이수정2014.07.2330명274-9858275-1089561-820
3936전주안골간호학원부설요양보호사교육원전주시 덕진구 안덕원로 262, 5층배순주2014.07.2430명246-1160243-1165561-870
4037익산평화간호전문학원 요양보호사교육원익산시 무왕로 1073, (원창빌딩 4층)박월순2014.10.0630명833-4477841-2323570-982
4138중앙간호학원요양보호사교육원군산시 공단대로 381, 4층 (나운동)오규만2014.10.1530명471-2970471-2971573-871
4239제이성모요양보호사교육원익산시 무왕로 1071, 3층이태영2014.10.2230명833-3370853-8334570-982
4340고창요양보호사교육원고창군 고창읍 중앙로 180, 3층김재홍2014.11.0730명563-0038563-0018585-808
4441아중안골요양보호사교육원전주시 덕진구 견훤로 289, 3층김은희2014.12.0235명282-2323242-1441561-870
4542군산여성인력개발센터 요양보호사교육원군산시 백토로 119, 대주빌딩 (2층)백지연2014.12.1130명468-0055468-0058573-863
4643평화탑클래스요양보호사교육원전주시 완산구 소대배기로 5 (4층)기정임2015.01.1515명229-6229229-6228560-865
4744덕진간호학원부설요양보호사교육원전주시 덕진구 기린대로 481 (동원타워 5층)김지은2015.02.1730명276-6767276-6768561-810