Overview

Dataset statistics

Number of variables7
Number of observations28
Missing cells33
Missing cells (%)16.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory60.7 B

Variable types

Text7

Dataset

Description전북스마트쉼센터운영현황20183
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202102

Alerts

2016년 목표 has 9 (32.1%) missing valuesMissing
2016년 실적 has 2 (7.1%) missing valuesMissing
2017년 목표 has 9 (32.1%) missing valuesMissing
2017년 실적 has 2 (7.1%) missing valuesMissing
2018년 목표 has 9 (32.1%) missing valuesMissing
2018년 3월 현재 실적 has 2 (7.1%) missing valuesMissing
구분 has unique valuesUnique

Reproduction

Analysis started2024-03-14 01:20:50.233283
Analysis finished2024-03-14 01:20:50.838957
Duration0.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-03-14T10:20:50.949219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length12.785714
Min length7

Characters and Unicode

Total characters358
Distinct characters62
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st row레몬교실(유아)-횟수
2nd row레몬교실(유아)-인원
3rd row레몬교실(청소년)-횟수
4th row레몬교실(청소년)-인원
5th row레몬교실(성인)-횟수
ValueCountFrequency (%)
예방교육 2
 
6.2%
총 2
 
6.2%
레몬교실(유아)-횟수 1
 
3.1%
레몬교실(유아)-인원 1
 
3.1%
예산(국비)-천원 1
 
3.1%
e-클린홍보-인원 1
 
3.1%
e-클린홍보-횟수 1
 
3.1%
놀이치료-인원 1
 
3.1%
놀이치료-횟수 1
 
3.1%
집단상담-인원 1
 
3.1%
Other values (20) 20
62.5%
2024-03-14T10:20:51.226073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 30
 
8.4%
인 23
 
6.4%
( 18
 
5.0%
) 18
 
5.0%
W 16
 
4.5%
원 15
 
4.2%
횟 13
 
3.6%
수 13
 
3.6%
교 10
 
2.8%
실 10
 
2.8%
Other values (52) 192
53.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 262
73.2%
Dash Punctuation 30
 
8.4%
Uppercase Letter 24
 
6.7%
Open Punctuation 18
 
5.0%
Close Punctuation 18
 
5.0%
Space Separator 4
 
1.1%
Lowercase Letter 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 23
 
8.8%
원 15
 
5.7%
횟 13
 
5.0%
수 13
 
5.0%
교 10
 
3.8%
실 10
 
3.8%
멘 8
 
3.1%
한 8
 
3.1%
터 8
 
3.1%
넷 8
 
3.1%
Other values (45) 146
55.7%
Uppercase Letter
ValueCountFrequency (%)
W 16
66.7%
O 8
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 262
73.2%
Common 70
 
19.6%
Latin 26
 
7.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 23
 
8.8%
원 15
 
5.7%
횟 13
 
5.0%
수 13
 
5.0%
교 10
 
3.8%
실 10
 
3.8%
멘 8
 
3.1%
한 8
 
3.1%
터 8
 
3.1%
넷 8
 
3.1%
Other values (45) 146
55.7%
Common
ValueCountFrequency (%)
- 30
42.9%
( 18
25.7%
) 18
25.7%
4
 
5.7%
Latin
ValueCountFrequency (%)
W 16
61.5%
O 8
30.8%
e 2
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 262
73.2%
ASCII 96
 
26.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 30
31.2%
( 18
18.8%
) 18
18.8%
W 16
16.7%
O 8
 
8.3%
4
 
4.2%
e 2
 
2.1%
Hangul
ValueCountFrequency (%)
인 23
 
8.8%
원 15
 
5.7%
횟 13
 
5.0%
수 13
 
5.0%
교 10
 
3.8%
실 10
 
3.8%
멘 8
 
3.1%
한 8
 
3.1%
터 8
 
3.1%
넷 8
 
3.1%
Other values (45) 146
55.7%

2016년 목표
Text

MISSING 

Distinct17
Distinct (%)89.5%
Missing9
Missing (%)32.1%
Memory size356.0 B
2024-03-14T10:20:51.394570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.2631579
Min length2

Characters and Unicode

Total characters81
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

Unique15 ?
Unique (%)78.9%

Sample

1st row2,590
2nd row28,540
3rd row3,020
4th row427
5th row34,150
ValueCountFrequency (%)
150 2
 
10.5%
10 2
 
10.5%
35,870 1
 
5.3%
2,590 1
 
5.3%
89,640 1
 
5.3%
2,500 1
 
5.3%
2,000 1
 
5.3%
125 1
 
5.3%
1,000 1
 
5.3%
1,720 1
 
5.3%
Other values (7) 7
36.8%
2024-03-14T10:20:51.891802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 24
29.6%
, 11
13.6%
5 9
 
11.1%
1 8
 
9.9%
2 8
 
9.9%
4 7
 
8.6%
8 4
 
4.9%
7 4
 
4.9%
3 3
 
3.7%
9 2
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70
86.4%
Other Punctuation 11
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24
34.3%
5 9
 
12.9%
1 8
 
11.4%
2 8
 
11.4%
4 7
 
10.0%
8 4
 
5.7%
7 4
 
5.7%
3 3
 
4.3%
9 2
 
2.9%
6 1
 
1.4%
Other Punctuation
ValueCountFrequency (%)
, 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 81
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24
29.6%
, 11
13.6%
5 9
 
11.1%
1 8
 
9.9%
2 8
 
9.9%
4 7
 
8.6%
8 4
 
4.9%
7 4
 
4.9%
3 3
 
3.7%
9 2
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24
29.6%
, 11
13.6%
5 9
 
11.1%
1 8
 
9.9%
2 8
 
9.9%
4 7
 
8.6%
8 4
 
4.9%
7 4
 
4.9%
3 3
 
3.7%
9 2
 
2.5%

2016년 실적
Text

MISSING 

Distinct26
Distinct (%)100.0%
Missing2
Missing (%)7.1%
Memory size356.0 B
2024-03-14T10:20:52.046881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.3846154
Min length1

Characters and Unicode

Total characters88
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

Unique26 ?
Unique (%)100.0%

Sample

1st row36
2nd row2,859
3rd row355
4th row30,200
5th row27
ValueCountFrequency (%)
2,859 1
 
3.8%
355 1
 
3.8%
2,974 1
 
3.8%
10 1
 
3.8%
13 1
 
3.8%
155 1
 
3.8%
2,403 1
 
3.8%
152 1
 
3.8%
126 1
 
3.8%
949 1
 
3.8%
Other values (16) 16
61.5%
2024-03-14T10:20:52.287384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 14
15.9%
3 11
12.5%
1 10
11.4%
5 9
10.2%
, 8
9.1%
0 8
9.1%
8 6
6.8%
9 6
6.8%
7 6
6.8%
4 6
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80
90.9%
Other Punctuation 8
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 14
17.5%
3 11
13.8%
1 10
12.5%
5 9
11.2%
0 8
10.0%
8 6
7.5%
9 6
7.5%
7 6
7.5%
4 6
7.5%
6 4
 
5.0%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 88
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 14
15.9%
3 11
12.5%
1 10
11.4%
5 9
10.2%
, 8
9.1%
0 8
9.1%
8 6
6.8%
9 6
6.8%
7 6
6.8%
4 6
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 14
15.9%
3 11
12.5%
1 10
11.4%
5 9
10.2%
, 8
9.1%
0 8
9.1%
8 6
6.8%
9 6
6.8%
7 6
6.8%
4 6
6.8%

2017년 목표
Text

MISSING 

Distinct18
Distinct (%)94.7%
Missing9
Missing (%)32.1%
Memory size356.0 B
2024-03-14T10:20:52.432462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.1578947
Min length2

Characters and Unicode

Total characters79
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

Unique17 ?
Unique (%)89.5%

Sample

1st row2,780
2nd row27,550
3rd row2,940
4th row335
5th row33,270
ValueCountFrequency (%)
10 2
 
10.5%
2,780 1
 
5.3%
880 1
 
5.3%
65,900 1
 
5.3%
2,500 1
 
5.3%
150 1
 
5.3%
1,300 1
 
5.3%
100 1
 
5.3%
110 1
 
5.3%
34,950 1
 
5.3%
Other values (8) 8
42.1%
2024-03-14T10:20:52.691047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 24
30.4%
, 10
12.7%
1 8
 
10.1%
5 8
 
10.1%
3 7
 
8.9%
2 5
 
6.3%
8 4
 
5.1%
9 4
 
5.1%
7 3
 
3.8%
4 3
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69
87.3%
Other Punctuation 10
 
12.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24
34.8%
1 8
 
11.6%
5 8
 
11.6%
3 7
 
10.1%
2 5
 
7.2%
8 4
 
5.8%
9 4
 
5.8%
7 3
 
4.3%
4 3
 
4.3%
6 3
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 79
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24
30.4%
, 10
12.7%
1 8
 
10.1%
5 8
 
10.1%
3 7
 
8.9%
2 5
 
6.3%
8 4
 
5.1%
9 4
 
5.1%
7 3
 
3.8%
4 3
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24
30.4%
, 10
12.7%
1 8
 
10.1%
5 8
 
10.1%
3 7
 
8.9%
2 5
 
6.3%
8 4
 
5.1%
9 4
 
5.1%
7 3
 
3.8%
4 3
 
3.8%

2017년 실적
Text

MISSING 

Distinct25
Distinct (%)96.2%
Missing2
Missing (%)7.1%
Memory size356.0 B
2024-03-14T10:20:52.903892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.4230769
Min length2

Characters and Unicode

Total characters89
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

Unique24 ?
Unique (%)92.3%

Sample

1st row42
2nd row3,207
3rd row248
4th row28,222
5th row39
ValueCountFrequency (%)
12 2
 
7.7%
1,720 1
 
3.8%
60 1
 
3.8%
7,093 1
 
3.8%
16 1
 
3.8%
154 1
 
3.8%
1,809 1
 
3.8%
100 1
 
3.8%
114 1
 
3.8%
834 1
 
3.8%
Other values (15) 15
57.7%
2024-03-14T10:20:53.197258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 13
14.6%
1 11
12.4%
0 11
12.4%
3 10
11.2%
, 8
9.0%
9 8
9.0%
4 7
7.9%
8 6
6.7%
7 6
6.7%
6 5
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 81
91.0%
Other Punctuation 8
 
9.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 13
16.0%
1 11
13.6%
0 11
13.6%
3 10
12.3%
9 8
9.9%
4 7
8.6%
8 6
7.4%
7 6
7.4%
6 5
 
6.2%
5 4
 
4.9%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 89
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 13
14.6%
1 11
12.4%
0 11
12.4%
3 10
11.2%
, 8
9.0%
9 8
9.0%
4 7
7.9%
8 6
6.7%
7 6
6.7%
6 5
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 13
14.6%
1 11
12.4%
0 11
12.4%
3 10
11.2%
, 8
9.0%
9 8
9.0%
4 7
7.9%
8 6
6.7%
7 6
6.7%
6 5
 
5.6%

2018년 목표
Text

MISSING 

Distinct17
Distinct (%)89.5%
Missing9
Missing (%)32.1%
Memory size356.0 B
2024-03-14T10:20:53.374254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.1052632
Min length2

Characters and Unicode

Total characters78
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

Unique15 ?
Unique (%)78.9%

Sample

1st row2,100
2nd row13,610
3rd row2,350
4th row226
5th row18,060
ValueCountFrequency (%)
150 2
 
10.5%
10 2
 
10.5%
19,100 1
 
5.3%
2,100 1
 
5.3%
49,480 1
 
5.3%
7,000 1
 
5.3%
2,200 1
 
5.3%
80 1
 
5.3%
640 1
 
5.3%
1,040 1
 
5.3%
Other values (7) 7
36.8%
2024-03-14T10:20:53.670985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 26
33.3%
1 12
15.4%
, 10
 
12.8%
2 7
 
9.0%
6 5
 
6.4%
4 5
 
6.4%
5 4
 
5.1%
3 3
 
3.8%
8 3
 
3.8%
9 2
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68
87.2%
Other Punctuation 10
 
12.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26
38.2%
1 12
17.6%
2 7
 
10.3%
6 5
 
7.4%
4 5
 
7.4%
5 4
 
5.9%
3 3
 
4.4%
8 3
 
4.4%
9 2
 
2.9%
7 1
 
1.5%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 78
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26
33.3%
1 12
15.4%
, 10
 
12.8%
2 7
 
9.0%
6 5
 
6.4%
4 5
 
6.4%
5 4
 
5.1%
3 3
 
3.8%
8 3
 
3.8%
9 2
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26
33.3%
1 12
15.4%
, 10
 
12.8%
2 7
 
9.0%
6 5
 
6.4%
4 5
 
6.4%
5 4
 
5.1%
3 3
 
3.8%
8 3
 
3.8%
9 2
 
2.6%

2018년 3월 현재 실적
Text

MISSING 

Distinct18
Distinct (%)69.2%
Missing2
Missing (%)7.1%
Memory size356.0 B
2024-03-14T10:20:53.778222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.0769231
Min length1

Characters and Unicode

Total characters54
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

Unique14 ?
Unique (%)53.8%

Sample

1st row2
2nd row165
3rd row13
4th row1,417
5th row0
ValueCountFrequency (%)
0 6
23.1%
6 2
 
7.7%
5 2
 
7.7%
15 2
 
7.7%
2 1
 
3.8%
272 1
 
3.8%
192 1
 
3.8%
11 1
 
3.8%
4 1
 
3.8%
25 1
 
3.8%
Other values (8) 8
30.8%
2024-03-14T10:20:54.009463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14
25.9%
5 9
16.7%
0 7
13.0%
2 7
13.0%
4 4
 
7.4%
6 3
 
5.6%
, 3
 
5.6%
7 3
 
5.6%
8 2
 
3.7%
3 1
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51
94.4%
Other Punctuation 3
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14
27.5%
5 9
17.6%
0 7
13.7%
2 7
13.7%
4 4
 
7.8%
6 3
 
5.9%
7 3
 
5.9%
8 2
 
3.9%
3 1
 
2.0%
9 1
 
2.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 54
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14
25.9%
5 9
16.7%
0 7
13.0%
2 7
13.0%
4 4
 
7.4%
6 3
 
5.6%
, 3
 
5.6%
7 3
 
5.6%
8 2
 
3.7%
3 1
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14
25.9%
5 9
16.7%
0 7
13.0%
2 7
13.0%
4 4
 
7.4%
6 3
 
5.6%
, 3
 
5.6%
7 3
 
5.6%
8 2
 
3.7%
3 1
 
1.9%

Correlations

2024-03-14T10:20:54.102267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분2016년 목표2016년 실적2017년 목표2017년 실적2018년 목표2018년 3월 현재 실적
구분1.0001.0001.0001.0001.0001.0001.000
2016년 목표1.0001.0001.0001.0001.0001.0000.922
2016년 실적1.0001.0001.0001.0001.0001.0001.000
2017년 목표1.0001.0001.0001.0001.0001.0000.971
2017년 실적1.0001.0001.0001.0001.0001.0000.976
2018년 목표1.0001.0001.0001.0001.0001.0000.922
2018년 3월 현재 실적1.0000.9221.0000.9710.9760.9221.000

Missing values

2024-03-14T10:20:50.499303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T10:20:50.638635image/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.
2024-03-14T10:20:50.752216image/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

구분2016년 목표2016년 실적2017년 목표2017년 실적2018년 목표2018년 3월 현재 실적
0레몬교실(유아)-횟수<NA>36<NA>42<NA>2
1레몬교실(유아)-인원2,5902,8592,7803,2072,100165
2레몬교실(청소년)-횟수<NA>355<NA>248<NA>13
3레몬교실(청소년)-인원28,54030,20027,55028,22213,6101,417
4레몬교실(성인)-횟수<NA>27<NA>39<NA>0
5레몬교실(성인)-인원3,0203,1712,9403,5612,3500
6레몬교실(종합)-횟수42741833532922615
7레몬교실(종합)-인원34,15036,23033,27034,99018,0601,582
8WOW건강한인터넷멘토링(초등학생)-횟수<NA>28<NA>28<NA>5
9WOW건강한인터넷멘토링(초등학생)-인원<NA>924<NA>539<NA>125
구분2016년 목표2016년 실적2017년 목표2017년 실적2018년 목표2018년 3월 현재 실적
18가정방문상담-횟수1,0009498808346404
19가정방문상담-인원125126110114806
20집단상담-횟수15015210010015011
21집단상담-인원2,0002,4031,3001,8092,200192
22놀이치료-횟수15015515015415015
23놀이치료-인원10131012106
24e-클린홍보-횟수10101016100
25e-클린홍보-인원2,5002,9742,5007,0937,0000
26예산(국비)-천원89,640<NA>65,900<NA>49,480<NA>
27예산(도비)-천원40,000<NA>40,000<NA>40,000<NA>