Overview

Dataset statistics

Number of variables1
Number of observations1727
Missing cells1716
Missing cells (%)99.4%
Duplicate rows3
Duplicate rows (%)0.2%
Total size in memory13.6 KiB
Average record size in memory8.1 B

Variable types

Text1

Dataset

Description지리적표시관리 인증, 심사 등의 업무 관리(등록번호, 등록명칭, 등록일자, 대상지역, 생산계획량, 구성현황 등)
Author국립농산물품질관리원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220204000000001691

Alerts

Dataset has 3 (0.2%) duplicate rowsDuplicates
ࡱ has 1716 (99.4%) missing valuesMissing

Reproduction

Analysis started2024-03-23 07:27:13.599076
Analysis finished2024-03-23 07:27:13.864470
Duration0.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ࡱ
Text

MISSING 

Distinct9
Distinct (%)81.8%
Missing1716
Missing (%)99.4%
Memory size13.6 KiB
2024-03-23T07:27:13.933367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length2
Min length1

Characters and Unicode

Total characters22
Distinct characters12
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)63.6%

Sample

1st row
2nd row@”
3rd row)̱
4th row0
5th roẁ0
ValueCountFrequency (%)
 2
18.2%
 2
18.2%
 1
9.1%
@” 1
9.1%
̱ 1
9.1%
0 1
9.1%
̀0 1
9.1%
 1
9.1%
1
9.1%
2024-03-23T07:27:14.771134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
 6
27.3%
4
18.2%
 2
 
9.1%
0 2
 
9.1%
 1
 
4.5%
@ 1
 
4.5%
” 1
 
4.5%
) 1
 
4.5%
̱ 1
 
4.5%
̀ 1
 
4.5%
Other values (2) 2
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Control 10
45.5%
Space Separator 4
 
18.2%
Decimal Number 2
 
9.1%
Other Punctuation 2
 
9.1%
Nonspacing Mark 2
 
9.1%
Close Punctuation 1
 
4.5%
Open Punctuation 1
 
4.5%

Most frequent character per category

Control
ValueCountFrequency (%)
 6
60.0%
 2
 
20.0%
 1
 
10.0%
” 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
@ 1
50.0%
. 1
50.0%
Nonspacing Mark
ValueCountFrequency (%)
̱ 1
50.0%
̀ 1
50.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Decimal Number
ValueCountFrequency (%)
0 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20
90.9%
Inherited 2
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
 6
30.0%
4
20.0%
 2
 
10.0%
0 2
 
10.0%
 1
 
5.0%
@ 1
 
5.0%
” 1
 
5.0%
) 1
 
5.0%
( 1
 
5.0%
. 1
 
5.0%
Inherited
ValueCountFrequency (%)
̱ 1
50.0%
̀ 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19
86.4%
Diacriticals 2
 
9.1%
None 1
 
4.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
 6
31.6%
4
21.1%
 2
 
10.5%
0 2
 
10.5%
 1
 
5.3%
@ 1
 
5.3%
) 1
 
5.3%
( 1
 
5.3%
. 1
 
5.3%
None
ValueCountFrequency (%)
” 1
100.0%
Diacriticals
ValueCountFrequency (%)
̱ 1
50.0%
̀ 1
50.0%

Missing values

2024-03-23T07:27:13.707767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T07:27:13.825516image/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

ࡱ
0<NA>
1<NA>
2<NA>
3<NA>
4<NA>
5<NA>
6<NA>
7<NA>
8<NA>
9<NA>
ࡱ
1717<NA>
1718<NA>
1719<NA>
1720<NA>
1721<NA>
1722<NA>
1723<NA>
1724<NA>
1725<NA>
1726<NA>

Duplicate rows

Most frequently occurring

ࡱ# duplicates
2<NA>1716
02
12