Overview

Dataset statistics

Number of variables4
Number of observations29
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 KiB
Average record size in memory36.6 B

Variable types

Text4

Dataset

Description한국임업진흥원의 시험분석조사인증 통계입니다. 2020-01-01~2020-12-31 까지의 시험 분석 의뢰 개수, 시험 개수, 시료 개수 정보를 제공합니다.
Author한국임업진흥원
URLhttps://www.data.go.kr/data/15093642/fileData.do

Alerts

의뢰분류 has unique valuesUnique

Reproduction

Analysis started2024-04-17 13:32:17.447869
Analysis finished2024-04-17 13:32:17.747500
Duration0.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

의뢰분류
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2024-04-17T22:32:17.872567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length18
Mean length14.724138
Min length4

Characters and Unicode

Total characters427
Distinct characters85
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

Unique29 ?
Unique (%)100.0%

Sample

1st rowKS제품시험
2nd row목재등급평가사양성교육
3rd row시험분석-규격품질검사
4th row시험분석-규격품질검사-A형
5th row시험분석-규격품질검사-A형-이의신청
ValueCountFrequency (%)
분석 2
 
5.6%
ks제품시험 1
 
2.8%
시험분석-신기술지정(현장평가,종합평가 1
 
2.8%
청정숲푸드-지정 1
 
2.8%
특별관리임산물-결과증명서 1
 
2.8%
재발급 1
 
2.8%
특별관리임산물-사본발급 1
 
2.8%
특별관리임산물-생산적합성-종묘 1
 
2.8%
특별관리임산물-생산적합성-종자 1
 
2.8%
특별관리임산물-생산적합성-종자-재검사 1
 
2.8%
Other values (25) 25
69.4%
2024-04-17T22:32:18.192590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 42
 
9.8%
분 22
 
5.2%
석 22
 
5.2%
시 20
 
4.7%
험 20
 
4.7%
사 18
 
4.2%
산 17
 
4.0%
관 11
 
2.6%
리 11
 
2.6%
물 11
 
2.6%
Other values (75) 233
54.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 363
85.0%
Dash Punctuation 42
 
9.8%
Space Separator 7
 
1.6%
Uppercase Letter 6
 
1.4%
Close Punctuation 3
 
0.7%
Open Punctuation 3
 
0.7%
Math Symbol 2
 
0.5%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
분 22
 
6.1%
석 22
 
6.1%
시 20
 
5.5%
험 20
 
5.5%
사 18
 
5.0%
산 17
 
4.7%
관 11
 
3.0%
리 11
 
3.0%
물 11
 
3.0%
임 10
 
2.8%
Other values (64) 201
55.4%
Uppercase Letter
ValueCountFrequency (%)
A 2
33.3%
B 2
33.3%
K 1
16.7%
S 1
16.7%
Math Symbol
ValueCountFrequency (%)
< 1
50.0%
> 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%
Space Separator
ValueCountFrequency (%)
7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 363
85.0%
Common 58
 
13.6%
Latin 6
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
분 22
 
6.1%
석 22
 
6.1%
시 20
 
5.5%
험 20
 
5.5%
사 18
 
5.0%
산 17
 
4.7%
관 11
 
3.0%
리 11
 
3.0%
물 11
 
3.0%
임 10
 
2.8%
Other values (64) 201
55.4%
Common
ValueCountFrequency (%)
- 42
72.4%
7
 
12.1%
) 3
 
5.2%
( 3
 
5.2%
< 1
 
1.7%
, 1
 
1.7%
> 1
 
1.7%
Latin
ValueCountFrequency (%)
A 2
33.3%
B 2
33.3%
K 1
16.7%
S 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 363
85.0%
ASCII 64
 
15.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 42
65.6%
7
 
10.9%
) 3
 
4.7%
( 3
 
4.7%
A 2
 
3.1%
B 2
 
3.1%
< 1
 
1.6%
K 1
 
1.6%
S 1
 
1.6%
, 1
 
1.6%
Hangul
ValueCountFrequency (%)
분 22
 
6.1%
석 22
 
6.1%
시 20
 
5.5%
험 20
 
5.5%
사 18
 
5.0%
산 17
 
4.7%
관 11
 
3.0%
리 11
 
3.0%
물 11
 
3.0%
임 10
 
2.8%
Other values (64) 201
55.4%

의뢰 개수
Text

Distinct26
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Memory size364.0 B
2024-04-17T22:32:18.340909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.2413793
Min length1

Characters and Unicode

Total characters65
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

Unique23 ?
Unique (%)79.3%

Sample

1st row2
2nd row143
3rd row574
4th row74
5th row20
ValueCountFrequency (%)
4 2
 
6.9%
3 2
 
6.9%
20 2
 
6.9%
2 1
 
3.4%
218 1
 
3.4%
161 1
 
3.4%
40 1
 
3.4%
496 1
 
3.4%
259 1
 
3.4%
29 1
 
3.4%
Other values (16) 16
55.2%
2024-04-17T22:32:18.820017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 10
15.4%
1 10
15.4%
4 9
13.8%
5 7
10.8%
3 6
9.2%
6 6
9.2%
0 4
 
6.2%
7 4
 
6.2%
9 4
 
6.2%
8 3
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63
96.9%
Other Punctuation 2
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 10
15.9%
1 10
15.9%
4 9
14.3%
5 7
11.1%
3 6
9.5%
6 6
9.5%
0 4
 
6.3%
7 4
 
6.3%
9 4
 
6.3%
8 3
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 65
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 10
15.4%
1 10
15.4%
4 9
13.8%
5 7
10.8%
3 6
9.2%
6 6
9.2%
0 4
 
6.2%
7 4
 
6.2%
9 4
 
6.2%
8 3
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 10
15.4%
1 10
15.4%
4 9
13.8%
5 7
10.8%
3 6
9.2%
6 6
9.2%
0 4
 
6.2%
7 4
 
6.2%
9 4
 
6.2%
8 3
 
4.6%

시험 개수
Text

Distinct27
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Memory size364.0 B
2024-04-17T22:32:18.973850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.9655172
Min length1

Characters and Unicode

Total characters86
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

Unique25 ?
Unique (%)86.2%

Sample

1st row8
2nd row143
3rd row4,240
4th row576
5th row50
ValueCountFrequency (%)
4 2
 
6.9%
40 2
 
6.9%
8 1
 
3.4%
3 1
 
3.4%
218 1
 
3.4%
2,255 1
 
3.4%
3,975 1
 
3.4%
161 1
 
3.4%
2,093 1
 
3.4%
232 1
 
3.4%
Other values (17) 17
58.6%
2024-04-17T22:32:19.220189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 13
15.1%
1 10
11.6%
3 10
11.6%
5 10
11.6%
4 9
10.5%
, 8
9.3%
0 7
8.1%
7 6
7.0%
8 5
 
5.8%
6 4
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 78
90.7%
Other Punctuation 8
 
9.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 13
16.7%
1 10
12.8%
3 10
12.8%
5 10
12.8%
4 9
11.5%
0 7
9.0%
7 6
7.7%
8 5
 
6.4%
6 4
 
5.1%
9 4
 
5.1%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 86
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 13
15.1%
1 10
11.6%
3 10
11.6%
5 10
11.6%
4 9
10.5%
, 8
9.3%
0 7
8.1%
7 6
7.0%
8 5
 
5.8%
6 4
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 13
15.1%
1 10
11.6%
3 10
11.6%
5 10
11.6%
4 9
10.5%
, 8
9.3%
0 7
8.1%
7 6
7.0%
8 5
 
5.8%
6 4
 
4.7%

시료 개수
Text

Distinct26
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Memory size364.0 B
2024-04-17T22:32:19.372339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.6206897
Min length1

Characters and Unicode

Total characters76
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

Unique23 ?
Unique (%)79.3%

Sample

1st row2
2nd row143
3rd row574
4th row74
5th row20
ValueCountFrequency (%)
4 2
 
6.9%
3 2
 
6.9%
20 2
 
6.9%
2 1
 
3.4%
218 1
 
3.4%
161 1
 
3.4%
46 1
 
3.4%
496 1
 
3.4%
259 1
 
3.4%
29 1
 
3.4%
Other values (16) 16
55.2%
2024-04-17T22:32:19.630524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 12
15.8%
1 11
14.5%
2 9
11.8%
3 8
10.5%
5 8
10.5%
0 6
7.9%
9 6
7.9%
7 5
6.6%
, 5
6.6%
6 4
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71
93.4%
Other Punctuation 5
 
6.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 12
16.9%
1 11
15.5%
2 9
12.7%
3 8
11.3%
5 8
11.3%
0 6
8.5%
9 6
8.5%
7 5
7.0%
6 4
 
5.6%
8 2
 
2.8%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 76
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 12
15.8%
1 11
14.5%
2 9
11.8%
3 8
10.5%
5 8
10.5%
0 6
7.9%
9 6
7.9%
7 5
6.6%
, 5
6.6%
6 4
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 76
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 12
15.8%
1 11
14.5%
2 9
11.8%
3 8
10.5%
5 8
10.5%
0 6
7.9%
9 6
7.9%
7 5
6.6%
, 5
6.6%
6 4
 
5.3%

Correlations

2024-04-17T22:32:19.715547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
의뢰분류의뢰 개수시험 개수시료 개수
의뢰분류1.0001.0001.0001.000
의뢰 개수1.0001.0000.9901.000
시험 개수1.0000.9901.0000.990
시료 개수1.0001.0000.9901.000

Missing values

2024-04-17T22:32:17.652699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T22:32:17.722260image/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

의뢰분류의뢰 개수시험 개수시료 개수
0KS제품시험282
1목재등급평가사양성교육143143143
2시험분석-규격품질검사5744,240574
3시험분석-규격품질검사-A형7457674
4시험분석-규격품질검사-A형-이의신청205020
5시험분석-규격품질검사-B형4512,137451
6시험분석-규격품질검사-B형-이의신청525752
7시험분석-사본발급545454
8시험분석-산림토양조사111
9시험분석-시험분석조사2,1165,7822,395
의뢰분류의뢰 개수시험 개수시료 개수
19특별관리임산물-사본발급713713713
20특별관리임산물-생산적합성-종묘2923229
21특별관리임산물-생산적합성-종자2592,093259
22특별관리임산물-생산적합성-종자-재검사2016120
23특별관리임산물-생산적합성-토양4963,975496
24특별관리임산물-유통관리 검사404046
25특별관리임산물-품질검사1612,255161
26특별관리임산물-품질검사-국내산-이의신청3403
27특별관리임산물-합격증 추가발급218218218
28<합계>6,20128,20018,450