Overview

Dataset statistics

Number of variables5
Number of observations72
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory41.8 B

Variable types

Text4
Categorical1

Dataset

Description2015년 제·개정된 농축수산물 표준코드의 등급코드와 동일한 의미를 가지는 2013년 농축수산물 표준코드의 등급코드를 나타낸 정보
Author농림수산식품교육문화정보원
URLhttps://www.data.go.kr/data/15045729/fileData.do

Alerts

업데이트일자 has constant value ""Constant
구등급코드 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:57:07.995989
Analysis finished2023-12-12 20:57:08.364732
Duration0.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

등급코드
Text

Distinct68
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size708.0 B
2023-12-13T05:57:08.512430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters144
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)88.9%

Sample

1st row11
2nd row12
3rd row13
4th row14
5th row15
ValueCountFrequency (%)
8z 2
 
2.8%
7z 2
 
2.8%
3z 2
 
2.8%
1z 2
 
2.8%
83 1
 
1.4%
84 1
 
1.4%
4k 1
 
1.4%
4z 1
 
1.4%
4o 1
 
1.4%
4m 1
 
1.4%
Other values (58) 58
80.6%
2023-12-13T05:57:08.822980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 29
20.1%
1 21
14.6%
7 19
13.2%
3 16
11.1%
8 10
 
6.9%
Z 9
 
6.2%
2 5
 
3.5%
9 4
 
2.8%
5 4
 
2.8%
6 4
 
2.8%
Other values (15) 23
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 112
77.8%
Uppercase Letter 32
 
22.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Z 9
28.1%
C 3
 
9.4%
D 3
 
9.4%
B 3
 
9.4%
A 3
 
9.4%
Y 1
 
3.1%
K 1
 
3.1%
O 1
 
3.1%
M 1
 
3.1%
L 1
 
3.1%
Other values (6) 6
18.8%
Decimal Number
ValueCountFrequency (%)
4 29
25.9%
1 21
18.8%
7 19
17.0%
3 16
14.3%
8 10
 
8.9%
2 5
 
4.5%
9 4
 
3.6%
5 4
 
3.6%
6 4
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common 112
77.8%
Latin 32
 
22.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
Z 9
28.1%
C 3
 
9.4%
D 3
 
9.4%
B 3
 
9.4%
A 3
 
9.4%
Y 1
 
3.1%
K 1
 
3.1%
O 1
 
3.1%
M 1
 
3.1%
L 1
 
3.1%
Other values (6) 6
18.8%
Common
ValueCountFrequency (%)
4 29
25.9%
1 21
18.8%
7 19
17.0%
3 16
14.3%
8 10
 
8.9%
2 5
 
4.5%
9 4
 
3.6%
5 4
 
3.6%
6 4
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 29
20.1%
1 21
14.6%
7 19
13.2%
3 16
11.1%
8 10
 
6.9%
Z 9
 
6.2%
2 5
 
3.5%
9 4
 
2.8%
5 4
 
2.8%
6 4
 
2.8%
Other values (15) 23
16.0%

등급명
Text

Distinct40
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Memory size708.0 B
2023-12-13T05:57:09.009781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length5
Mean length2.5138889
Min length1

Characters and Unicode

Total characters181
Distinct characters43
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

Unique28 ?
Unique (%)38.9%

Sample

1st row특
2nd row상
3rd row보통
4th row4등
5th row5등
ValueCountFrequency (%)
무등급 9
 
12.5%
등외 5
 
6.9%
양식산 4
 
5.6%
상 4
 
5.6%
자연산 4
 
5.6%
특 4
 
5.6%
보통 4
 
5.6%
6등 2
 
2.8%
7등 2
 
2.8%
4등 2
 
2.8%
Other values (30) 32
44.4%
2023-12-13T05:57:09.294617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
등 24
 
13.3%
무 11
 
6.1%
1 11
 
6.1%
산 11
 
6.1%
+ 10
 
5.5%
급 9
 
5.0%
외 5
 
2.8%
기 5
 
2.8%
C 5
 
2.8%
A 5
 
2.8%
Other values (33) 85
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 121
66.9%
Decimal Number 29
 
16.0%
Uppercase Letter 19
 
10.5%
Math Symbol 10
 
5.5%
Close Punctuation 1
 
0.6%
Open Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
등 24
19.8%
무 11
 
9.1%
산 11
 
9.1%
급 9
 
7.4%
외 5
 
4.1%
기 5
 
4.1%
상 4
 
3.3%
보 4
 
3.3%
식 4
 
3.3%
자 4
 
3.3%
Other values (18) 40
33.1%
Decimal Number
ValueCountFrequency (%)
1 11
37.9%
3 4
 
13.8%
2 4
 
13.8%
5 2
 
6.9%
7 2
 
6.9%
8 2
 
6.9%
6 2
 
6.9%
4 2
 
6.9%
Uppercase Letter
ValueCountFrequency (%)
C 5
26.3%
A 5
26.3%
B 5
26.3%
D 4
21.1%
Math Symbol
ValueCountFrequency (%)
+ 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 121
66.9%
Common 41
 
22.7%
Latin 19
 
10.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
등 24
19.8%
무 11
 
9.1%
산 11
 
9.1%
급 9
 
7.4%
외 5
 
4.1%
기 5
 
4.1%
상 4
 
3.3%
보 4
 
3.3%
식 4
 
3.3%
자 4
 
3.3%
Other values (18) 40
33.1%
Common
ValueCountFrequency (%)
1 11
26.8%
+ 10
24.4%
3 4
 
9.8%
2 4
 
9.8%
5 2
 
4.9%
7 2
 
4.9%
8 2
 
4.9%
6 2
 
4.9%
4 2
 
4.9%
) 1
 
2.4%
Latin
ValueCountFrequency (%)
C 5
26.3%
A 5
26.3%
B 5
26.3%
D 4
21.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 121
66.9%
ASCII 60
33.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
등 24
19.8%
무 11
 
9.1%
산 11
 
9.1%
급 9
 
7.4%
외 5
 
4.1%
기 5
 
4.1%
상 4
 
3.3%
보 4
 
3.3%
식 4
 
3.3%
자 4
 
3.3%
Other values (18) 40
33.1%
ASCII
ValueCountFrequency (%)
1 11
18.3%
+ 10
16.7%
C 5
8.3%
A 5
8.3%
B 5
8.3%
3 4
 
6.7%
2 4
 
6.7%
D 4
 
6.7%
5 2
 
3.3%
7 2
 
3.3%
Other values (5) 8
13.3%

구등급코드
Text

UNIQUE 

Distinct72
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
2023-12-13T05:57:09.526898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters144
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)100.0%

Sample

1st row11
2nd row12
3rd row13
4th row14
5th row15
ValueCountFrequency (%)
11 1
 
1.4%
12 1
 
1.4%
72 1
 
1.4%
71 1
 
1.4%
4z 1
 
1.4%
4o 1
 
1.4%
4m 1
 
1.4%
4l 1
 
1.4%
73 1
 
1.4%
4k 1
 
1.4%
Other values (62) 62
86.1%
2023-12-13T05:57:09.888371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 29
20.1%
1 21
14.6%
7 19
13.2%
3 16
11.1%
2 11
 
7.6%
Z 5
 
3.5%
9 4
 
2.8%
0 4
 
2.8%
8 4
 
2.8%
6 4
 
2.8%
Other values (16) 27
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 116
80.6%
Uppercase Letter 28
 
19.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Z 5
17.9%
C 3
10.7%
D 3
10.7%
A 3
10.7%
B 3
10.7%
H 1
 
3.6%
M 1
 
3.6%
L 1
 
3.6%
K 1
 
3.6%
I 1
 
3.6%
Other values (6) 6
21.4%
Decimal Number
ValueCountFrequency (%)
4 29
25.0%
1 21
18.1%
7 19
16.4%
3 16
13.8%
2 11
 
9.5%
9 4
 
3.4%
0 4
 
3.4%
8 4
 
3.4%
6 4
 
3.4%
5 4
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 116
80.6%
Latin 28
 
19.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
Z 5
17.9%
C 3
10.7%
D 3
10.7%
A 3
10.7%
B 3
10.7%
H 1
 
3.6%
M 1
 
3.6%
L 1
 
3.6%
K 1
 
3.6%
I 1
 
3.6%
Other values (6) 6
21.4%
Common
ValueCountFrequency (%)
4 29
25.0%
1 21
18.1%
7 19
16.4%
3 16
13.8%
2 11
 
9.5%
9 4
 
3.4%
0 4
 
3.4%
8 4
 
3.4%
6 4
 
3.4%
5 4
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 29
20.1%
1 21
14.6%
7 19
13.2%
3 16
11.1%
2 11
 
7.6%
Z 5
 
3.5%
9 4
 
2.8%
0 4
 
2.8%
8 4
 
2.8%
6 4
 
2.8%
Other values (16) 27
18.8%

구등급명
Text

Distinct41
Distinct (%)56.9%
Missing0
Missing (%)0.0%
Memory size708.0 B
2023-12-13T05:57:10.084411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length7.5
Mean length2.4583333
Min length1

Characters and Unicode

Total characters177
Distinct characters45
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

Unique28 ?
Unique (%)38.9%

Sample

1st row특
2nd row상
3rd row보통
4th row4등
5th row5등
ValueCountFrequency (%)
등외 5
 
6.9%
무등급 5
 
6.9%
특 4
 
5.6%
상 4
 
5.6%
없음 4
 
5.6%
자연산 4
 
5.6%
양식산 4
 
5.6%
보통 4
 
5.6%
8등 2
 
2.8%
5등 2
 
2.8%
Other values (31) 34
47.2%
2023-12-13T05:57:10.396772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
등 20
 
11.3%
산 11
 
6.2%
1 11
 
6.2%
+ 10
 
5.6%
무 7
 
4.0%
기 5
 
2.8%
A 5
 
2.8%
C 5
 
2.8%
외 5
 
2.8%
B 5
 
2.8%
Other values (35) 93
52.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 117
66.1%
Decimal Number 29
 
16.4%
Uppercase Letter 19
 
10.7%
Math Symbol 10
 
5.6%
Open Punctuation 1
 
0.6%
Close Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
등 20
17.1%
산 11
 
9.4%
무 7
 
6.0%
기 5
 
4.3%
외 5
 
4.3%
급 5
 
4.3%
자 4
 
3.4%
양 4
 
3.4%
특 4
 
3.4%
상 4
 
3.4%
Other values (20) 48
41.0%
Decimal Number
ValueCountFrequency (%)
1 11
37.9%
2 4
 
13.8%
3 4
 
13.8%
7 2
 
6.9%
4 2
 
6.9%
8 2
 
6.9%
6 2
 
6.9%
5 2
 
6.9%
Uppercase Letter
ValueCountFrequency (%)
A 5
26.3%
C 5
26.3%
B 5
26.3%
D 4
21.1%
Math Symbol
ValueCountFrequency (%)
+ 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 117
66.1%
Common 41
 
23.2%
Latin 19
 
10.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
등 20
17.1%
산 11
 
9.4%
무 7
 
6.0%
기 5
 
4.3%
외 5
 
4.3%
급 5
 
4.3%
자 4
 
3.4%
양 4
 
3.4%
특 4
 
3.4%
상 4
 
3.4%
Other values (20) 48
41.0%
Common
ValueCountFrequency (%)
1 11
26.8%
+ 10
24.4%
2 4
 
9.8%
3 4
 
9.8%
7 2
 
4.9%
4 2
 
4.9%
8 2
 
4.9%
6 2
 
4.9%
5 2
 
4.9%
( 1
 
2.4%
Latin
ValueCountFrequency (%)
A 5
26.3%
C 5
26.3%
B 5
26.3%
D 4
21.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 117
66.1%
ASCII 60
33.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
등 20
17.1%
산 11
 
9.4%
무 7
 
6.0%
기 5
 
4.3%
외 5
 
4.3%
급 5
 
4.3%
자 4
 
3.4%
양 4
 
3.4%
특 4
 
3.4%
상 4
 
3.4%
Other values (20) 48
41.0%
ASCII
ValueCountFrequency (%)
1 11
18.3%
+ 10
16.7%
A 5
8.3%
C 5
8.3%
B 5
8.3%
2 4
 
6.7%
3 4
 
6.7%
D 4
 
6.7%
7 2
 
3.3%
4 2
 
3.3%
Other values (5) 8
13.3%

업데이트일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size708.0 B
2015-12-15
72 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015-12-15
2nd row2015-12-15
3rd row2015-12-15
4th row2015-12-15
5th row2015-12-15

Common Values

ValueCountFrequency (%)
2015-12-15 72
100.0%

Length

2023-12-13T05:57:10.518730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:57:10.614765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015-12-15 72
100.0%

Correlations

2023-12-13T05:57:10.673069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등급코드등급명구등급코드구등급명
등급코드1.0001.0001.0000.996
등급명1.0001.0001.0001.000
구등급코드1.0001.0001.0001.000
구등급명0.9961.0001.0001.000

Missing values

2023-12-13T05:57:08.259380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:57:08.334886image/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

등급코드등급명구등급코드구등급명업데이트일자
011특11특2015-12-15
112상12상2015-12-15
213보통13보통2015-12-15
3144등144등2015-12-15
4155등155등2015-12-15
5166등166등2015-12-15
6177등177등2015-12-15
7188등188등2015-12-15
819등외19등외2015-12-15
91A유기농산물1A유기농산물2015-12-15
등급코드등급명구등급코드구등급명업데이트일자
627C보통7C보통2015-12-15
637D하7D하2015-12-15
647Z무등급70없음2015-12-15
657Z무등급7Z무등급2015-12-15
6681특21특2015-12-15
6782상22상2015-12-15
6883보통23보통2015-12-15
6984등외24등외2015-12-15
708Z무등급20없음2015-12-15
718Z무등급2Z무등급2015-12-15