Overview

Dataset statistics

Number of variables5
Number of observations554
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.3 KiB
Average record size in memory41.2 B

Variable types

Text4
Categorical1

Dataset

Description2015년 제·개정된 농축수산물 표준코드의 산지코드와 동일한 의미를 가지는 2013년 농축수산물 표준코드의 산지코드를 나타낸 정보
Author농림수산식품교육문화정보원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220210000000001769

Alerts

UPDT_DE has constant value ""Constant
STD_MTC_CODE has unique valuesUnique

Reproduction

Analysis started2023-12-11 03:28:46.643330
Analysis finished2023-12-11 03:28:47.711553
Duration1.07 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

STD_MTC_NEW_CODE
Text

Distinct535
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2023-12-11T12:28:48.038595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters2770
Distinct characters36
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

Unique516 ?
Unique (%)93.1%

Sample

1st row01000
2nd row01300
3rd row01800
4th row02200
5th row02500
ValueCountFrequency (%)
800nz 2
 
0.4%
800om 2
 
0.4%
800id 2
 
0.4%
800ru 2
 
0.4%
800ar 2
 
0.4%
800fk 2
 
0.4%
28700 2
 
0.4%
800es 2
 
0.4%
800us 2
 
0.4%
800ye 2
 
0.4%
Other values (525) 534
96.4%
2023-12-11T12:28:48.661954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1175
42.4%
8 348
 
12.6%
1 138
 
5.0%
5 111
 
4.0%
2 106
 
3.8%
3 102
 
3.7%
4 86
 
3.1%
9 63
 
2.3%
7 53
 
1.9%
6 51
 
1.8%
Other values (26) 537
19.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2233
80.6%
Uppercase Letter 537
 
19.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 42
 
7.8%
S 32
 
6.0%
T 32
 
6.0%
A 30
 
5.6%
G 30
 
5.6%
N 29
 
5.4%
C 29
 
5.4%
B 25
 
4.7%
K 23
 
4.3%
R 23
 
4.3%
Other values (16) 242
45.1%
Decimal Number
ValueCountFrequency (%)
0 1175
52.6%
8 348
 
15.6%
1 138
 
6.2%
5 111
 
5.0%
2 106
 
4.7%
3 102
 
4.6%
4 86
 
3.9%
9 63
 
2.8%
7 53
 
2.4%
6 51
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2233
80.6%
Latin 537
 
19.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 42
 
7.8%
S 32
 
6.0%
T 32
 
6.0%
A 30
 
5.6%
G 30
 
5.6%
N 29
 
5.4%
C 29
 
5.4%
B 25
 
4.7%
K 23
 
4.3%
R 23
 
4.3%
Other values (16) 242
45.1%
Common
ValueCountFrequency (%)
0 1175
52.6%
8 348
 
15.6%
1 138
 
6.2%
5 111
 
5.0%
2 106
 
4.7%
3 102
 
4.6%
4 86
 
3.9%
9 63
 
2.8%
7 53
 
2.4%
6 51
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2770
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1175
42.4%
8 348
 
12.6%
1 138
 
5.0%
5 111
 
4.0%
2 106
 
3.8%
3 102
 
3.7%
4 86
 
3.1%
9 63
 
2.3%
7 53
 
1.9%
6 51
 
1.8%
Other values (26) 537
19.4%

STD_MTC_NEW_NM
Text

Distinct535
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2023-12-11T12:28:49.110180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length5.9386282
Min length1

Characters and Unicode

Total characters3290
Distinct characters299
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique516 ?
Unique (%)93.1%

Sample

1st row서울특별시 강북구
2nd row서울특별시 도봉구
3rd row서울특별시 노원구
4th row서울특별시 중랑구
5th row서울특별시 동대문구
ValueCountFrequency (%)
경기도 33
 
4.0%
서울특별시 26
 
3.2%
경상북도 24
 
2.9%
전라남도 23
 
2.8%
경상남도 20
 
2.4%
강원도 19
 
2.3%
부산광역시 17
 
2.1%
충청남도 16
 
2.0%
전라북도 15
 
1.8%
충청북도 13
 
1.6%
Other values (521) 613
74.8%
2023-12-11T12:28:49.677530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
265
 
8.1%
도 210
 
6.4%
시 178
 
5.4%
군 99
 
3.0%
남 89
 
2.7%
구 83
 
2.5%
경 80
 
2.4%
아 72
 
2.2%
북 67
 
2.0%
광 67
 
2.0%
Other values (289) 2080
63.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3018
91.7%
Space Separator 265
 
8.1%
Other Punctuation 3
 
0.1%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 210
 
7.0%
시 178
 
5.9%
군 99
 
3.3%
남 89
 
2.9%
구 83
 
2.8%
경 80
 
2.7%
아 72
 
2.4%
북 67
 
2.2%
광 67
 
2.2%
라 65
 
2.2%
Other values (285) 2008
66.5%
Space Separator
ValueCountFrequency (%)
265
100.0%
Other Punctuation
ValueCountFrequency (%)
· 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3018
91.7%
Common 272
 
8.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 210
 
7.0%
시 178
 
5.9%
군 99
 
3.3%
남 89
 
2.9%
구 83
 
2.8%
경 80
 
2.7%
아 72
 
2.4%
북 67
 
2.2%
광 67
 
2.2%
라 65
 
2.2%
Other values (285) 2008
66.5%
Common
ValueCountFrequency (%)
265
97.4%
· 3
 
1.1%
) 2
 
0.7%
( 2
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3018
91.7%
ASCII 269
 
8.2%
None 3
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
265
98.5%
) 2
 
0.7%
( 2
 
0.7%
Hangul
ValueCountFrequency (%)
도 210
 
7.0%
시 178
 
5.9%
군 99
 
3.3%
남 89
 
2.9%
구 83
 
2.8%
경 80
 
2.7%
아 72
 
2.4%
북 67
 
2.2%
광 67
 
2.2%
라 65
 
2.2%
Other values (285) 2008
66.5%
None
ValueCountFrequency (%)
· 3
100.0%

STD_MTC_CODE
Text

UNIQUE 

Distinct554
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2023-12-11T12:28:50.076663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.5397112
Min length5

Characters and Unicode

Total characters3069
Distinct characters36
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

Unique554 ?
Unique (%)100.0%

Sample

1st row142000
2nd row132000
3rd row139000
4th row131000
5th row130000
ValueCountFrequency (%)
142000 1
 
0.2%
800mc 1
 
0.2%
800lb 1
 
0.2%
800lc 1
 
0.2%
800mh 1
 
0.2%
800mg 1
 
0.2%
800me 1
 
0.2%
800md 1
 
0.2%
800ma 1
 
0.2%
800li 1
 
0.2%
Other values (544) 544
98.2%
2023-12-11T12:28:50.616991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1455
47.4%
8 336
 
10.9%
1 172
 
5.6%
5 108
 
3.5%
3 107
 
3.5%
6 105
 
3.4%
4 82
 
2.7%
7 78
 
2.5%
2 67
 
2.2%
9 52
 
1.7%
Other values (26) 507
 
16.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2562
83.5%
Uppercase Letter 507
 
16.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 40
 
7.9%
T 32
 
6.3%
G 30
 
5.9%
A 29
 
5.7%
S 29
 
5.7%
C 27
 
5.3%
N 26
 
5.1%
B 24
 
4.7%
P 21
 
4.1%
K 21
 
4.1%
Other values (16) 228
45.0%
Decimal Number
ValueCountFrequency (%)
0 1455
56.8%
8 336
 
13.1%
1 172
 
6.7%
5 108
 
4.2%
3 107
 
4.2%
6 105
 
4.1%
4 82
 
3.2%
7 78
 
3.0%
2 67
 
2.6%
9 52
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2562
83.5%
Latin 507
 
16.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 40
 
7.9%
T 32
 
6.3%
G 30
 
5.9%
A 29
 
5.7%
S 29
 
5.7%
C 27
 
5.3%
N 26
 
5.1%
B 24
 
4.7%
P 21
 
4.1%
K 21
 
4.1%
Other values (16) 228
45.0%
Common
ValueCountFrequency (%)
0 1455
56.8%
8 336
 
13.1%
1 172
 
6.7%
5 108
 
4.2%
3 107
 
4.2%
6 105
 
4.1%
4 82
 
3.2%
7 78
 
3.0%
2 67
 
2.6%
9 52
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3069
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1455
47.4%
8 336
 
10.9%
1 172
 
5.6%
5 108
 
3.5%
3 107
 
3.5%
6 105
 
3.4%
4 82
 
2.7%
7 78
 
2.5%
2 67
 
2.2%
9 52
 
1.7%
Other values (26) 507
 
16.5%

STD_MTC_NM
Text

Distinct538
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2023-12-11T12:28:51.060632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length5.9386282
Min length1

Characters and Unicode

Total characters3290
Distinct characters306
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique522 ?
Unique (%)94.2%

Sample

1st row서울특별시 강북구
2nd row서울특별시 도봉구
3rd row서울특별시 노원구
4th row서울특별시 중랑구
5th row서울특별시 동대문구
ValueCountFrequency (%)
경기도 33
 
3.9%
서울특별시 26
 
3.1%
경상북도 24
 
2.8%
전라남도 23
 
2.7%
경상남도 20
 
2.4%
강원도 19
 
2.2%
부산광역시 17
 
2.0%
충청남도 16
 
1.9%
전라북도 15
 
1.8%
군도 14
 
1.6%
Other values (529) 642
75.6%
2023-12-11T12:28:51.743451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
295
 
9.0%
도 210
 
6.4%
시 174
 
5.3%
군 106
 
3.2%
남 89
 
2.7%
구 85
 
2.6%
경 80
 
2.4%
아 75
 
2.3%
광 67
 
2.0%
북 67
 
2.0%
Other values (296) 2042
62.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2989
90.9%
Space Separator 295
 
9.0%
Other Punctuation 3
 
0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 210
 
7.0%
시 174
 
5.8%
군 106
 
3.5%
남 89
 
3.0%
구 85
 
2.8%
경 80
 
2.7%
아 75
 
2.5%
광 67
 
2.2%
북 67
 
2.2%
라 66
 
2.2%
Other values (291) 1970
65.9%
Space Separator
ValueCountFrequency (%)
295
100.0%
Other Punctuation
ValueCountFrequency (%)
& 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2989
90.9%
Common 301
 
9.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 210
 
7.0%
시 174
 
5.8%
군 106
 
3.5%
남 89
 
3.0%
구 85
 
2.8%
경 80
 
2.7%
아 75
 
2.5%
광 67
 
2.2%
북 67
 
2.2%
라 66
 
2.2%
Other values (291) 1970
65.9%
Common
ValueCountFrequency (%)
295
98.0%
& 3
 
1.0%
( 1
 
0.3%
) 1
 
0.3%
- 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2989
90.9%
ASCII 301
 
9.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
295
98.0%
& 3
 
1.0%
( 1
 
0.3%
) 1
 
0.3%
- 1
 
0.3%
Hangul
ValueCountFrequency (%)
도 210
 
7.0%
시 174
 
5.8%
군 106
 
3.5%
남 89
 
3.0%
구 85
 
2.8%
경 80
 
2.7%
아 75
 
2.5%
광 67
 
2.2%
북 67
 
2.2%
라 66
 
2.2%
Other values (291) 1970
65.9%

UPDT_DE
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
20220127
554 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20220127
2nd row20220127
3rd row20220127
4th row20220127
5th row20220127

Common Values

ValueCountFrequency (%)
20220127 554
100.0%

Length

2023-12-11T12:28:51.940417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:28:52.061661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20220127 554
100.0%

Missing values

2023-12-11T12:28:47.563422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:28:47.668919image/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

STD_MTC_NEW_CODESTD_MTC_NEW_NMSTD_MTC_CODESTD_MTC_NMUPDT_DE
001000서울특별시 강북구142000서울특별시 강북구20220127
101300서울특별시 도봉구132000서울특별시 도봉구20220127
201800서울특별시 노원구139000서울특별시 노원구20220127
302200서울특별시 중랑구131000서울특별시 중랑구20220127
402500서울특별시 동대문구130000서울특별시 동대문구20220127
502800서울특별시 성북구136000서울특별시 성북구20220127
603100서울특별시 종로구110000서울특별시 종로구20220127
703400서울특별시 은평구122000서울특별시 은평구20220127
803600서울특별시 서대문구120000서울특별시 서대문구20220127
903900서울특별시 마포구121000서울특별시 마포구20220127
STD_MTC_NEW_CODESTD_MTC_NEW_NMSTD_MTC_CODESTD_MTC_NMUPDT_DE
54490800세종특별자치시339000세종시 세종시20220127
54591000경기도910000경기도20220127
54692000강원도920000강원도20220127
54793000충청남도930000충청남도20220127
54894000충청북도940000충청북도20220127
54995000전라남도950000전라남도20220127
55096000전라북도960000전라북도20220127
55197000경상남도970000경상남도20220127
55298000경상북도980000경상북도20220127
55399000제주특별자치도990000제주도20220127