Overview

Dataset statistics

Number of variables9
Number of observations95
Missing cells385
Missing cells (%)45.0%
Duplicate rows1
Duplicate rows (%)1.1%
Total size in memory6.8 KiB
Average record size in memory73.4 B

Variable types

Text8
Categorical1

Dataset

Description요양보호사교육원지정제현황20156
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202381

Alerts

Dataset has 1 (1.1%) duplicate rowsDuplicates
요양보호사 교육원 현황 has 45 (47.4%) missing valuesMissing
Unnamed: 1 has 48 (50.5%) missing valuesMissing
Unnamed: 2 has 46 (48.4%) missing valuesMissing
Unnamed: 3 has 48 (50.5%) missing valuesMissing
Unnamed: 4 has 48 (50.5%) missing valuesMissing
Unnamed: 6 has 49 (51.6%) missing valuesMissing
Unnamed: 7 has 51 (53.7%) missing valuesMissing
Unnamed: 8 has 50 (52.6%) missing valuesMissing

Reproduction

Analysis started2024-03-14 00:23:12.648607
Analysis finished2024-03-14 00:23:13.468023
Duration0.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

요양보호사 교육원 현황
Text

MISSING 

Distinct25
Distinct (%)50.0%
Missing45
Missing (%)47.4%
Memory size892.0 B
2024-03-14T09:23:13.563083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length1
Mean length2.44
Min length1

Characters and Unicode

Total characters122
Distinct characters36
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)34.0%

Sample

1st row적정 요양보호사교육원 개소수 ▶
2nd row현재 운영 중인 교육원 개소수 ▶
3rd row 《 73개소 중 29개소 폐지 》
4th row번호
5th row1
ValueCountFrequency (%)
1 11
17.5%
2 6
 
9.5%
3 4
 
6.3%
5 3
 
4.8%
4 3
 
4.8%
7 2
 
3.2%
8 2
 
3.2%
6 2
 
3.2%
개소수 2
 
3.2%
▶ 2
 
3.2%
Other values (26) 26
41.3%
2024-03-14T09:23:13.822768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 23
18.9%
22
18.0%
2 10
 
8.2%
3 6
 
4.9%
5 4
 
3.3%
4 4
 
3.3%
7 4
 
3.3%
소 4
 
3.3%
개 4
 
3.3%
8 3
 
2.5%
Other values (26) 38
31.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62
50.8%
Other Letter 34
27.9%
Space Separator 22
 
18.0%
Other Symbol 2
 
1.6%
Open Punctuation 1
 
0.8%
Close Punctuation 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 4
 
11.8%
개 4
 
11.8%
육 2
 
5.9%
호 2
 
5.9%
수 2
 
5.9%
원 2
 
5.9%
교 2
 
5.9%
중 2
 
5.9%
영 1
 
2.9%
운 1
 
2.9%
Other values (12) 12
35.3%
Decimal Number
ValueCountFrequency (%)
1 23
37.1%
2 10
16.1%
3 6
 
9.7%
5 4
 
6.5%
4 4
 
6.5%
7 4
 
6.5%
8 3
 
4.8%
6 3
 
4.8%
9 3
 
4.8%
0 2
 
3.2%
Space Separator
ValueCountFrequency (%)
22
100.0%
Other Symbol
ValueCountFrequency (%)
▶ 2
100.0%
Open Punctuation
ValueCountFrequency (%)
《 1
100.0%
Close Punctuation
ValueCountFrequency (%)
》 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 88
72.1%
Hangul 34
 
27.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
소 4
 
11.8%
개 4
 
11.8%
육 2
 
5.9%
호 2
 
5.9%
수 2
 
5.9%
원 2
 
5.9%
교 2
 
5.9%
중 2
 
5.9%
영 1
 
2.9%
운 1
 
2.9%
Other values (12) 12
35.3%
Common
ValueCountFrequency (%)
1 23
26.1%
22
25.0%
2 10
11.4%
3 6
 
6.8%
5 4
 
4.5%
4 4
 
4.5%
7 4
 
4.5%
8 3
 
3.4%
6 3
 
3.4%
9 3
 
3.4%
Other values (4) 6
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84
68.9%
Hangul 34
27.9%
Geometric Shapes 2
 
1.6%
None 2
 
1.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 23
27.4%
22
26.2%
2 10
11.9%
3 6
 
7.1%
5 4
 
4.8%
4 4
 
4.8%
7 4
 
4.8%
8 3
 
3.6%
6 3
 
3.6%
9 3
 
3.6%
Hangul
ValueCountFrequency (%)
소 4
 
11.8%
개 4
 
11.8%
육 2
 
5.9%
호 2
 
5.9%
수 2
 
5.9%
원 2
 
5.9%
교 2
 
5.9%
중 2
 
5.9%
영 1
 
2.9%
운 1
 
2.9%
Other values (12) 12
35.3%
Geometric Shapes
ValueCountFrequency (%)
▶ 2
100.0%
None
ValueCountFrequency (%)
《 1
50.0%
》 1
50.0%

Unnamed: 1
Text

MISSING 

Distinct47
Distinct (%)100.0%
Missing48
Missing (%)50.5%
Memory size892.0 B
2024-03-14T09:23:14.014627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length13.87234
Min length4

Characters and Unicode

Total characters652
Distinct characters92
Distinct categories3 ?
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 row전주성모간호요양보호사교육원
3rd row목민요양보호사교육원
4th row탑클래스
5th row전주성신간호학원 부설 요양보호사교육원
ValueCountFrequency (%)
요양보호사교육원 11
 
16.7%
부설 5
 
7.6%
평화요양보호사교육원 2
 
3.0%
명 1
 
1.5%
종로요양보호사교육원 1
 
1.5%
제이성모요양보호사교육원 1
 
1.5%
중앙간호학원요양보호사교육원 1
 
1.5%
군산여성인력개발센터 1
 
1.5%
익산간호요양보호사교육원 1
 
1.5%
이리간호교육원 1
 
1.5%
Other values (41) 41
62.1%
2024-03-14T09:23:14.361260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 65
 
10.0%
호 64
 
9.8%
교 49
 
7.5%
육 48
 
7.4%
보 46
 
7.1%
양 46
 
7.1%
요 45
 
6.9%
사 45
 
6.9%
28
 
4.3%
간 18
 
2.8%
Other values (82) 198
30.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 622
95.4%
Space Separator 28
 
4.3%
Uppercase Letter 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 65
 
10.5%
호 64
 
10.3%
교 49
 
7.9%
육 48
 
7.7%
보 46
 
7.4%
양 46
 
7.4%
요 45
 
7.2%
사 45
 
7.2%
간 18
 
2.9%
학 16
 
2.6%
Other values (79) 180
28.9%
Uppercase Letter
ValueCountFrequency (%)
J 1
50.0%
K 1
50.0%
Space Separator
ValueCountFrequency (%)
28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 622
95.4%
Common 28
 
4.3%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 65
 
10.5%
호 64
 
10.3%
교 49
 
7.9%
육 48
 
7.7%
보 46
 
7.4%
양 46
 
7.4%
요 45
 
7.2%
사 45
 
7.2%
간 18
 
2.9%
학 16
 
2.6%
Other values (79) 180
28.9%
Latin
ValueCountFrequency (%)
J 1
50.0%
K 1
50.0%
Common
ValueCountFrequency (%)
28
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 622
95.4%
ASCII 30
 
4.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 65
 
10.5%
호 64
 
10.3%
교 49
 
7.9%
육 48
 
7.7%
보 46
 
7.4%
양 46
 
7.4%
요 45
 
7.2%
사 45
 
7.2%
간 18
 
2.9%
학 16
 
2.6%
Other values (79) 180
28.9%
ASCII
ValueCountFrequency (%)
28
93.3%
J 1
 
3.3%
K 1
 
3.3%

Unnamed: 2
Text

MISSING 

Distinct49
Distinct (%)100.0%
Missing46
Missing (%)48.4%
Memory size892.0 B
2024-03-14T09:23:14.613767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length76
Median length25
Mean length22.979592
Min length9

Characters and Unicode

Total characters1126
Distinct characters126
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)100.0%

Sample

1st row65개소(전주21, 군산9, 익산10, 정읍3, 남원2, 김제3, 완주3, 진안2, 무주2, 장수2, 임실2, 순창2, 고창2, 부안2)
2nd row46개소(전주21, 군산5, 익산8, 정읍3, 남원2, 김제2, 완주1, 진안0, 무주0, 장수1, 임실0, 순창1, 고창1, 부안1)
3rd row소 재 지
4th row전주시 완산구 어진길 114 (경원동 1가)
5th row전주시 완산구 용머리로 203 (서완산동 2가)
ValueCountFrequency (%)
전주시 21
 
8.5%
완산구 13
 
5.2%
3층 8
 
3.2%
덕진구 8
 
3.2%
익산시 8
 
3.2%
군산시 5
 
2.0%
2층 5
 
2.0%
익산대로 4
 
1.6%
무왕로 4
 
1.6%
4층 4
 
1.6%
Other values (146) 168
67.7%
2024-03-14T09:23:14.961582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
206
 
18.3%
2 48
 
4.3%
1 48
 
4.3%
로 44
 
3.9%
시 41
 
3.6%
) 38
 
3.4%
산 38
 
3.4%
( 38
 
3.4%
, 37
 
3.3%
동 34
 
3.0%
Other values (116) 554
49.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 586
52.0%
Decimal Number 209
 
18.6%
Space Separator 206
 
18.3%
Close Punctuation 38
 
3.4%
Open Punctuation 38
 
3.4%
Other Punctuation 38
 
3.4%
Dash Punctuation 8
 
0.7%
Uppercase Letter 3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 44
 
7.5%
시 41
 
7.0%
산 38
 
6.5%
동 34
 
5.8%
주 30
 
5.1%
전 25
 
4.3%
층 22
 
3.8%
구 21
 
3.6%
완 17
 
2.9%
익 14
 
2.4%
Other values (97) 300
51.2%
Decimal Number
ValueCountFrequency (%)
2 48
23.0%
1 48
23.0%
3 27
12.9%
4 19
 
9.1%
0 16
 
7.7%
5 13
 
6.2%
6 12
 
5.7%
7 11
 
5.3%
8 9
 
4.3%
9 6
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
B 1
33.3%
Y 1
33.3%
C 1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 37
97.4%
. 1
 
2.6%
Space Separator
ValueCountFrequency (%)
206
100.0%
Close Punctuation
ValueCountFrequency (%)
) 38
100.0%
Open Punctuation
ValueCountFrequency (%)
( 38
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 586
52.0%
Common 537
47.7%
Latin 3
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 44
 
7.5%
시 41
 
7.0%
산 38
 
6.5%
동 34
 
5.8%
주 30
 
5.1%
전 25
 
4.3%
층 22
 
3.8%
구 21
 
3.6%
완 17
 
2.9%
익 14
 
2.4%
Other values (97) 300
51.2%
Common
ValueCountFrequency (%)
206
38.4%
2 48
 
8.9%
1 48
 
8.9%
) 38
 
7.1%
( 38
 
7.1%
, 37
 
6.9%
3 27
 
5.0%
4 19
 
3.5%
0 16
 
3.0%
5 13
 
2.4%
Other values (6) 47
 
8.8%
Latin
ValueCountFrequency (%)
B 1
33.3%
Y 1
33.3%
C 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 586
52.0%
ASCII 540
48.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
206
38.1%
2 48
 
8.9%
1 48
 
8.9%
) 38
 
7.0%
( 38
 
7.0%
, 37
 
6.9%
3 27
 
5.0%
4 19
 
3.5%
0 16
 
3.0%
5 13
 
2.4%
Other values (9) 50
 
9.3%
Hangul
ValueCountFrequency (%)
로 44
 
7.5%
시 41
 
7.0%
산 38
 
6.5%
동 34
 
5.8%
주 30
 
5.1%
전 25
 
4.3%
층 22
 
3.8%
구 21
 
3.6%
완 17
 
2.9%
익 14
 
2.4%
Other values (97) 300
51.2%

Unnamed: 3
Text

MISSING 

Distinct47
Distinct (%)100.0%
Missing48
Missing (%)50.5%
Memory size892.0 B
2024-03-14T09:23:15.156751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length3
Mean length3.3404255
Min length3

Characters and Unicode

Total characters157
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

Unique47 ?
Unique (%)100.0%

Sample

1st row대표자
2nd row윤석길
3rd row김옥숙
4th row송제기
5th row이연주
ValueCountFrequency (%)
대표자 1
 
2.1%
이서현 1
 
2.1%
오규만 1
 
2.1%
백지연 1
 
2.1%
기성옥 1
 
2.1%
지정무 1
 
2.1%
김인종 1
 
2.1%
정선희 1
 
2.1%
오순옥 1
 
2.1%
권선숙 1
 
2.1%
Other values (38) 38
79.2%
2024-03-14T09:23:15.540936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
정 11
 
7.0%
성 9
 
5.7%
박 9
 
5.7%
이 7
 
4.5%
김 7
 
4.5%
기 5
 
3.2%
숙 5
 
3.2%
순 4
 
2.5%
희 4
 
2.5%
선 4
 
2.5%
Other values (60) 92
58.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 154
98.1%
Control 1
 
0.6%
Open Punctuation 1
 
0.6%
Close Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 11
 
7.1%
성 9
 
5.8%
박 9
 
5.8%
이 7
 
4.5%
김 7
 
4.5%
기 5
 
3.2%
숙 5
 
3.2%
순 4
 
2.6%
희 4
 
2.6%
선 4
 
2.6%
Other values (57) 89
57.8%
Control
ValueCountFrequency (%)
1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 154
98.1%
Common 3
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 11
 
7.1%
성 9
 
5.8%
박 9
 
5.8%
이 7
 
4.5%
김 7
 
4.5%
기 5
 
3.2%
숙 5
 
3.2%
순 4
 
2.6%
희 4
 
2.6%
선 4
 
2.6%
Other values (57) 89
57.8%
Common
ValueCountFrequency (%)
1
33.3%
( 1
33.3%
) 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 154
98.1%
ASCII 3
 
1.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
정 11
 
7.1%
성 9
 
5.8%
박 9
 
5.8%
이 7
 
4.5%
김 7
 
4.5%
기 5
 
3.2%
숙 5
 
3.2%
순 4
 
2.6%
희 4
 
2.6%
선 4
 
2.6%
Other values (57) 89
57.8%
ASCII
ValueCountFrequency (%)
1
33.3%
( 1
33.3%
) 1
33.3%

Unnamed: 4
Text

MISSING 

Distinct37
Distinct (%)78.7%
Missing48
Missing (%)50.5%
Memory size892.0 B
2024-03-14T09:23:15.740199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.6595745
Min length3

Characters and Unicode

Total characters454
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

Unique34 ?
Unique (%)72.3%

Sample

1st row지정일
2nd row2008.02.28
3rd row2009.09.06
4th row2010.09.09
5th row2010.10.20
ValueCountFrequency (%)
2010.10.28 7
 
14.9%
2010.10.20 4
 
8.5%
2008.02.28 2
 
4.3%
2014.10.22 1
 
2.1%
2010.03.18 1
 
2.1%
2011.04.04 1
 
2.1%
2014.10.15 1
 
2.1%
2014.12.11 1
 
2.1%
2010.10 1
 
2.1%
2014.10.06 1
 
2.1%
Other values (27) 27
57.4%
2024-03-14T09:23:16.054787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 117
25.8%
. 92
20.3%
1 89
19.6%
2 81
17.8%
8 20
 
4.4%
4 15
 
3.3%
5 12
 
2.6%
9 8
 
1.8%
3 7
 
1.5%
6 5
 
1.1%
Other values (4) 8
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 359
79.1%
Other Punctuation 92
 
20.3%
Other Letter 3
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 117
32.6%
1 89
24.8%
2 81
22.6%
8 20
 
5.6%
4 15
 
4.2%
5 12
 
3.3%
9 8
 
2.2%
3 7
 
1.9%
6 5
 
1.4%
7 5
 
1.4%
Other Letter
ValueCountFrequency (%)
지 1
33.3%
정 1
33.3%
일 1
33.3%
Other Punctuation
ValueCountFrequency (%)
. 92
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 451
99.3%
Hangul 3
 
0.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 117
25.9%
. 92
20.4%
1 89
19.7%
2 81
18.0%
8 20
 
4.4%
4 15
 
3.3%
5 12
 
2.7%
9 8
 
1.8%
3 7
 
1.6%
6 5
 
1.1%
Hangul
ValueCountFrequency (%)
지 1
33.3%
정 1
33.3%
일 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 451
99.3%
Hangul 3
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 117
25.9%
. 92
20.4%
1 89
19.7%
2 81
18.0%
8 20
 
4.4%
4 15
 
3.3%
5 12
 
2.7%
9 8
 
1.8%
3 7
 
1.6%
6 5
 
1.1%
Hangul
ValueCountFrequency (%)
지 1
33.3%
정 1
33.3%
일 1
33.3%

Unnamed: 5
Categorical

Distinct13
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Memory size892.0 B
<NA>
48 
30
20 
40
13 
35
 
2
20
 
2
Other values (8)
10 

Length

Max length4
Median length4
Mean length3.0210526
Min length2

Unique

Unique6 ?
Unique (%)6.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 48
50.5%
30 20
21.1%
40 13
 
13.7%
35 2
 
2.1%
20 2
 
2.1%
15 2
 
2.1%
25 2
 
2.1%
정원 1
 
1.1%
26 1
 
1.1%
16 1
 
1.1%
Other values (3) 3
 
3.2%

Length

2024-03-14T09:23:16.167782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 48
50.5%
30 20
21.1%
40 13
 
13.7%
35 2
 
2.1%
20 2
 
2.1%
15 2
 
2.1%
25 2
 
2.1%
정원 1
 
1.1%
26 1
 
1.1%
16 1
 
1.1%
Other values (3) 3
 
3.2%

Unnamed: 6
Text

MISSING 

Distinct46
Distinct (%)100.0%
Missing49
Missing (%)51.6%
Memory size892.0 B
2024-03-14T09:23:16.352379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length8
Mean length8.3478261
Min length4

Characters and Unicode

Total characters384
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 row283-6662~5
3rd row229-3144
4th row282-6500
5th row285-1999 285-1990
ValueCountFrequency (%)
272-4747 1
 
2.1%
283-6662~5 1
 
2.1%
471-2970 1
 
2.1%
468-0055 1
 
2.1%
858-9840 1
 
2.1%
851-2411 1
 
2.1%
840-1534 1
 
2.1%
853-8331 1
 
2.1%
852-0129 1
 
2.1%
857-7698 1
 
2.1%
Other values (38) 38
79.2%
2024-03-14T09:23:16.639535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 62
16.1%
- 47
12.2%
8 37
9.6%
5 36
9.4%
3 35
9.1%
4 30
7.8%
1 29
7.6%
9 26
6.8%
0 26
6.8%
6 25
6.5%
Other values (7) 31
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
85.9%
Dash Punctuation 47
 
12.2%
Other Letter 4
 
1.0%
Control 2
 
0.5%
Math Symbol 1
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 62
18.8%
8 37
11.2%
5 36
10.9%
3 35
10.6%
4 30
9.1%
1 29
8.8%
9 26
7.9%
0 26
7.9%
6 25
7.6%
7 24
 
7.3%
Other Letter
ValueCountFrequency (%)
전 1
25.0%
화 1
25.0%
번 1
25.0%
호 1
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 47
100.0%
Control
ValueCountFrequency (%)
2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 380
99.0%
Hangul 4
 
1.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 62
16.3%
- 47
12.4%
8 37
9.7%
5 36
9.5%
3 35
9.2%
4 30
7.9%
1 29
7.6%
9 26
6.8%
0 26
6.8%
6 25
6.6%
Other values (3) 27
7.1%
Hangul
ValueCountFrequency (%)
전 1
25.0%
화 1
25.0%
번 1
25.0%
호 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 380
99.0%
Hangul 4
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 62
16.3%
- 47
12.4%
8 37
9.7%
5 36
9.5%
3 35
9.2%
4 30
7.9%
1 29
7.6%
9 26
6.8%
0 26
6.8%
6 25
6.6%
Other values (3) 27
7.1%
Hangul
ValueCountFrequency (%)
전 1
25.0%
화 1
25.0%
번 1
25.0%
호 1
25.0%

Unnamed: 7
Text

MISSING 

Distinct44
Distinct (%)100.0%
Missing51
Missing (%)53.7%
Memory size892.0 B
2024-03-14T09:23:16.836194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.8863636
Min length3

Characters and Unicode

Total characters347
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

Unique44 ?
Unique (%)100.0%

Sample

1st rowFAX
2nd row286-6661
3rd row229-3147
4th row282-6501
5th row285-6991
ValueCountFrequency (%)
fax 1
 
2.3%
286-6661 1
 
2.3%
471-2971 1
 
2.3%
468-0058 1
 
2.3%
858-9841 1
 
2.3%
842-2411 1
 
2.3%
840-1530 1
 
2.3%
853-8334 1
 
2.3%
852-0139 1
 
2.3%
837-7698 1
 
2.3%
Other values (34) 34
77.3%
2024-03-14T09:23:17.120238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 53
15.3%
- 43
12.4%
3 35
10.1%
8 33
9.5%
1 33
9.5%
5 32
9.2%
4 26
7.5%
6 25
7.2%
0 22
6.3%
9 21
 
6.1%
Other values (4) 24
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 301
86.7%
Dash Punctuation 43
 
12.4%
Uppercase Letter 3
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 53
17.6%
3 35
11.6%
8 33
11.0%
1 33
11.0%
5 32
10.6%
4 26
8.6%
6 25
8.3%
0 22
7.3%
9 21
 
7.0%
7 21
 
7.0%
Uppercase Letter
ValueCountFrequency (%)
F 1
33.3%
A 1
33.3%
X 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 344
99.1%
Latin 3
 
0.9%

Most frequent character per script

Common
ValueCountFrequency (%)
2 53
15.4%
- 43
12.5%
3 35
10.2%
8 33
9.6%
1 33
9.6%
5 32
9.3%
4 26
7.6%
6 25
7.3%
0 22
6.4%
9 21
 
6.1%
Latin
ValueCountFrequency (%)
F 1
33.3%
A 1
33.3%
X 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 53
15.3%
- 43
12.4%
3 35
10.1%
8 33
9.5%
1 33
9.5%
5 32
9.2%
4 26
7.5%
6 25
7.2%
0 22
6.3%
9 21
 
6.1%
Other values (4) 24
6.9%

Unnamed: 8
Text

MISSING 

Distinct35
Distinct (%)77.8%
Missing50
Missing (%)52.6%
Memory size892.0 B
2024-03-14T09:23:17.283661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9333333
Min length4

Characters and Unicode

Total characters312
Distinct characters15
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

Unique28 ?
Unique (%)62.2%

Sample

1st row우편번호
2nd row560-020
3rd row560-152
4th row560-023
5th row560-913
ValueCountFrequency (%)
561-810 4
 
8.9%
560-023 3
 
6.7%
561-870 2
 
4.4%
570-982 2
 
4.4%
561-820 2
 
4.4%
570-993 2
 
4.4%
580-805 2
 
4.4%
597-842 1
 
2.2%
570-010 1
 
2.2%
576-711 1
 
2.2%
Other values (25) 25
55.6%
2024-03-14T09:23:17.608902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 56
17.9%
0 56
17.9%
- 44
14.1%
8 28
9.0%
6 26
8.3%
7 26
8.3%
1 24
7.7%
2 14
 
4.5%
3 14
 
4.5%
9 14
 
4.5%
Other values (5) 10
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 264
84.6%
Dash Punctuation 44
 
14.1%
Other Letter 4
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 56
21.2%
0 56
21.2%
8 28
10.6%
6 26
9.8%
7 26
9.8%
1 24
9.1%
2 14
 
5.3%
3 14
 
5.3%
9 14
 
5.3%
4 6
 
2.3%
Other Letter
ValueCountFrequency (%)
우 1
25.0%
편 1
25.0%
번 1
25.0%
호 1
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 308
98.7%
Hangul 4
 
1.3%

Most frequent character per script

Common
ValueCountFrequency (%)
5 56
18.2%
0 56
18.2%
- 44
14.3%
8 28
9.1%
6 26
8.4%
7 26
8.4%
1 24
7.8%
2 14
 
4.5%
3 14
 
4.5%
9 14
 
4.5%
Hangul
ValueCountFrequency (%)
우 1
25.0%
편 1
25.0%
번 1
25.0%
호 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308
98.7%
Hangul 4
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 56
18.2%
0 56
18.2%
- 44
14.3%
8 28
9.1%
6 26
8.4%
7 26
8.4%
1 24
7.8%
2 14
 
4.5%
3 14
 
4.5%
9 14
 
4.5%
Hangul
ValueCountFrequency (%)
우 1
25.0%
편 1
25.0%
번 1
25.0%
호 1
25.0%

Correlations

2024-03-14T09:23:17.697518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
요양보호사 교육원 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
요양보호사 교육원 현황1.0001.0001.0001.0000.9590.8651.0001.0000.000
Unnamed: 11.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 21.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 31.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 40.9591.0001.0001.0001.0000.9361.0001.0000.450
Unnamed: 50.8651.0001.0001.0000.9361.0001.0001.0000.849
Unnamed: 61.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 71.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 80.0001.0001.0001.0000.4500.8491.0001.0001.000

Missing values

2024-03-14T09:23:13.090874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:23:13.253292image/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.
2024-03-14T09:23:13.377978image/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 교육원 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
0<NA><NA><NA><NA><NA><NA><NA><NA><NA>
1적정 요양보호사교육원 개소수 ▶<NA>65개소(전주21, 군산9, 익산10, 정읍3, 남원2, 김제3, 완주3, 진안2, 무주2, 장수2, 임실2, 순창2, 고창2, 부안2)<NA><NA><NA><NA><NA><NA>
2현재 운영 중인 교육원 개소수 ▶<NA>46개소(전주21, 군산5, 익산8, 정읍3, 남원2, 김제2, 완주1, 진안0, 무주0, 장수1, 임실0, 순창1, 고창1, 부안1)<NA><NA><NA><NA><NA><NA>
3《 73개소 중 29개소 폐지 》<NA><NA><NA><NA><NA><NA><NA><NA>
4번호명 칭소 재 지대표자지정일정원전화번호FAX우편번호
51전주성모간호요양보호사교육원전주시 완산구 어진길 114 (경원동 1가)윤석길2008.02.2835283-6662~5286-6661560-020
62목민요양보호사교육원전주시 완산구 용머리로 203 (서완산동 2가)김옥숙2009.09.0630229-3144229-3147560-152
73탑클래스전주시 완산구 팔달로 212-8 (경원동3가)송제기2010.09.0940282-6500282-6501560-023
84전주성신간호학원 부설 요양보호사교육원전주시 완산구 팔달로 250 (서노송동)이연주2010.10.2040285-1999 285-1990285-6991560-913
95온누리요양보호사교육원전주시 완산구 팔달로 202-16 (3층)박흥순2010.10.2030231-4554231-4553560-023
요양보호사 교육원 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
85<NA><NA><NA><NA><NA><NA><NA><NA><NA>
86<NA><NA><NA><NA><NA><NA><NA><NA><NA>
87<NA><NA><NA><NA><NA><NA><NA><NA><NA>
88<NA><NA><NA><NA><NA><NA><NA><NA><NA>
89<NA><NA><NA><NA><NA><NA><NA><NA><NA>
90<NA><NA><NA><NA><NA><NA><NA><NA><NA>
91<NA><NA><NA><NA><NA><NA><NA><NA><NA>
92<NA><NA><NA><NA><NA><NA><NA><NA><NA>
93<NA><NA><NA><NA><NA><NA><NA><NA><NA>
94<NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

요양보호사 교육원 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA>45