Overview

Dataset statistics

Number of variables5
Number of observations554
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.8 KiB
Average record size in memory40.2 B

Variable types

Text4
DateTime1

Dataset

Description2015년 제·개정된 농축수산물 표준코드의 산지코드와 동일한 의미를 가지는 2013년 농축수산물 표준코드의 산지코드를 나타낸 정보
Author농림수산식품교육문화정보원
URLhttps://www.data.go.kr/data/15045731/fileData.do

Alerts

업데이트일자 has constant value ""Constant
구산지코드 has unique valuesUnique

Reproduction

Analysis started2023-12-12 08:42:11.851623
Analysis finished2023-12-12 08:42:12.475039
Duration0.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

산지코드
Text

Distinct536
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2023-12-12T17:42:12.783048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.9548736
Min length4

Characters and Unicode

Total characters2745
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

Unique518 ?
Unique (%)93.5%

Sample

1st row1000
2nd row1300
3rd row1800
4th row2200
5th row2500
ValueCountFrequency (%)
800nz 2
 
0.4%
800us 2
 
0.4%
800es 2
 
0.4%
28700 2
 
0.4%
800mx 2
 
0.4%
800fk 2
 
0.4%
800ye 2
 
0.4%
800id 2
 
0.4%
800ar 2
 
0.4%
800br 2
 
0.4%
Other values (526) 534
96.4%
2023-12-12T17:42:13.300604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1150
41.9%
8 349
 
12.7%
1 137
 
5.0%
5 111
 
4.0%
2 106
 
3.9%
3 102
 
3.7%
4 86
 
3.1%
9 62
 
2.3%
7 52
 
1.9%
6 51
 
1.9%
Other values (26) 539
19.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2206
80.4%
Uppercase Letter 539
 
19.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 42
 
7.8%
T 32
 
5.9%
S 32
 
5.9%
A 30
 
5.6%
G 30
 
5.6%
C 29
 
5.4%
N 29
 
5.4%
B 25
 
4.6%
R 24
 
4.5%
K 23
 
4.3%
Other values (16) 243
45.1%
Decimal Number
ValueCountFrequency (%)
0 1150
52.1%
8 349
 
15.8%
1 137
 
6.2%
5 111
 
5.0%
2 106
 
4.8%
3 102
 
4.6%
4 86
 
3.9%
9 62
 
2.8%
7 52
 
2.4%
6 51
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2206
80.4%
Latin 539
 
19.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 42
 
7.8%
T 32
 
5.9%
S 32
 
5.9%
A 30
 
5.6%
G 30
 
5.6%
C 29
 
5.4%
N 29
 
5.4%
B 25
 
4.6%
R 24
 
4.5%
K 23
 
4.3%
Other values (16) 243
45.1%
Common
ValueCountFrequency (%)
0 1150
52.1%
8 349
 
15.8%
1 137
 
6.2%
5 111
 
5.0%
2 106
 
4.8%
3 102
 
4.6%
4 86
 
3.9%
9 62
 
2.8%
7 52
 
2.4%
6 51
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2745
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1150
41.9%
8 349
 
12.7%
1 137
 
5.0%
5 111
 
4.0%
2 106
 
3.9%
3 102
 
3.7%
4 86
 
3.1%
9 62
 
2.3%
7 52
 
1.9%
6 51
 
1.9%
Other values (26) 539
19.6%

산지명
Text

Distinct536
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2023-12-12T17:42:13.742603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length5.933213
Min length1

Characters and Unicode

Total characters3287
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

Unique518 ?
Unique (%)93.5%

Sample

1st row서울특별시 강북구
2nd row서울특별시 도봉구
3rd row서울특별시 노원구
4th row서울특별시 중랑구
5th row서울특별시 동대문구
ValueCountFrequency (%)
경기도 32
 
3.9%
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 (522) 613
74.9%
2023-12-12T17:42:14.388420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
264
 
8.0%
도 209
 
6.4%
시 177
 
5.4%
군 99
 
3.0%
남 89
 
2.7%
구 83
 
2.5%
경 79
 
2.4%
아 72
 
2.2%
북 67
 
2.0%
광 67
 
2.0%
Other values (289) 2081
63.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3016
91.8%
Space Separator 264
 
8.0%
Other Punctuation 3
 
0.1%
Open Punctuation 2
 
0.1%
Close Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 209
 
6.9%
시 177
 
5.9%
군 99
 
3.3%
남 89
 
3.0%
구 83
 
2.8%
경 79
 
2.6%
아 72
 
2.4%
북 67
 
2.2%
광 67
 
2.2%
라 65
 
2.2%
Other values (285) 2009
66.6%
Space Separator
ValueCountFrequency (%)
264
100.0%
Other Punctuation
ValueCountFrequency (%)
· 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3016
91.8%
Common 271
 
8.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 209
 
6.9%
시 177
 
5.9%
군 99
 
3.3%
남 89
 
3.0%
구 83
 
2.8%
경 79
 
2.6%
아 72
 
2.4%
북 67
 
2.2%
광 67
 
2.2%
라 65
 
2.2%
Other values (285) 2009
66.6%
Common
ValueCountFrequency (%)
264
97.4%
· 3
 
1.1%
( 2
 
0.7%
) 2
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3016
91.8%
ASCII 268
 
8.2%
None 3
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
264
98.5%
( 2
 
0.7%
) 2
 
0.7%
Hangul
ValueCountFrequency (%)
도 209
 
6.9%
시 177
 
5.9%
군 99
 
3.3%
남 89
 
3.0%
구 83
 
2.8%
경 79
 
2.6%
아 72
 
2.4%
북 67
 
2.2%
광 67
 
2.2%
라 65
 
2.2%
Other values (285) 2009
66.6%
None
ValueCountFrequency (%)
· 3
100.0%

구산지코드
Text

UNIQUE 

Distinct554
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2023-12-12T17:42:14.825226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.5379061
Min length5

Characters and Unicode

Total characters3068
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%
800md 1
 
0.2%
800lc 1
 
0.2%
800li 1
 
0.2%
800mi 1
 
0.2%
800mh 1
 
0.2%
800mg 1
 
0.2%
800me 1
 
0.2%
800mc 1
 
0.2%
800lk 1
 
0.2%
Other values (544) 544
98.2%
2023-12-12T17:42:15.357471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1454
47.4%
8 337
 
11.0%
1 171
 
5.6%
3 107
 
3.5%
5 107
 
3.5%
6 105
 
3.4%
4 81
 
2.6%
7 78
 
2.5%
2 67
 
2.2%
9 52
 
1.7%
Other values (26) 509
 
16.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2559
83.4%
Uppercase Letter 509
 
16.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 40
 
7.9%
T 32
 
6.3%
G 30
 
5.9%
S 29
 
5.7%
A 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) 230
45.2%
Decimal Number
ValueCountFrequency (%)
0 1454
56.8%
8 337
 
13.2%
1 171
 
6.7%
3 107
 
4.2%
5 107
 
4.2%
6 105
 
4.1%
4 81
 
3.2%
7 78
 
3.0%
2 67
 
2.6%
9 52
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2559
83.4%
Latin 509
 
16.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 40
 
7.9%
T 32
 
6.3%
G 30
 
5.9%
S 29
 
5.7%
A 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) 230
45.2%
Common
ValueCountFrequency (%)
0 1454
56.8%
8 337
 
13.2%
1 171
 
6.7%
3 107
 
4.2%
5 107
 
4.2%
6 105
 
4.1%
4 81
 
3.2%
7 78
 
3.0%
2 67
 
2.6%
9 52
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3068
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1454
47.4%
8 337
 
11.0%
1 171
 
5.6%
3 107
 
3.5%
5 107
 
3.5%
6 105
 
3.4%
4 81
 
2.6%
7 78
 
2.5%
2 67
 
2.2%
9 52
 
1.7%
Other values (26) 509
 
16.6%

구산지명
Text

Distinct539
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2023-12-12T17:42:15.758402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length5.9512635
Min length1

Characters and Unicode

Total characters3297
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

Unique524 ?
Unique (%)94.6%

Sample

1st row서울특별시 강북구
2nd row서울특별시 도봉구
3rd row서울특별시 노원구
4th row서울특별시 중랑구
5th row서울특별시 동대문구
ValueCountFrequency (%)
경기도 32
 
3.8%
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 (532) 645
75.8%
2023-12-12T17:42:16.415409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
297
 
9.0%
도 211
 
6.4%
시 173
 
5.2%
군 106
 
3.2%
남 89
 
2.7%
구 85
 
2.6%
경 79
 
2.4%
아 75
 
2.3%
광 67
 
2.0%
북 67
 
2.0%
Other values (296) 2048
62.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2994
90.8%
Space Separator 297
 
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 211
 
7.0%
시 173
 
5.8%
군 106
 
3.5%
남 89
 
3.0%
구 85
 
2.8%
경 79
 
2.6%
아 75
 
2.5%
광 67
 
2.2%
북 67
 
2.2%
라 66
 
2.2%
Other values (291) 1976
66.0%
Space Separator
ValueCountFrequency (%)
297
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 2994
90.8%
Common 303
 
9.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 211
 
7.0%
시 173
 
5.8%
군 106
 
3.5%
남 89
 
3.0%
구 85
 
2.8%
경 79
 
2.6%
아 75
 
2.5%
광 67
 
2.2%
북 67
 
2.2%
라 66
 
2.2%
Other values (291) 1976
66.0%
Common
ValueCountFrequency (%)
297
98.0%
& 3
 
1.0%
( 1
 
0.3%
) 1
 
0.3%
- 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2994
90.8%
ASCII 303
 
9.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
297
98.0%
& 3
 
1.0%
( 1
 
0.3%
) 1
 
0.3%
- 1
 
0.3%
Hangul
ValueCountFrequency (%)
도 211
 
7.0%
시 173
 
5.8%
군 106
 
3.5%
남 89
 
3.0%
구 85
 
2.8%
경 79
 
2.6%
아 75
 
2.5%
광 67
 
2.2%
북 67
 
2.2%
라 66
 
2.2%
Other values (291) 1976
66.0%

업데이트일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
Minimum2015-12-15 00:00:00
Maximum2015-12-15 00:00:00
2023-12-12T17:42:16.555485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:42:16.663794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Missing values

2023-12-12T17:42:12.306160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:42:12.424329image/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

산지코드산지명구산지코드구산지명업데이트일자
01000서울특별시 강북구142000서울특별시 강북구2015-12-15
11300서울특별시 도봉구132000서울특별시 도봉구2015-12-15
21800서울특별시 노원구139000서울특별시 노원구2015-12-15
32200서울특별시 중랑구131000서울특별시 중랑구2015-12-15
42500서울특별시 동대문구130000서울특별시 동대문구2015-12-15
52800서울특별시 성북구136000서울특별시 성북구2015-12-15
63100서울특별시 종로구110000서울특별시 종로구2015-12-15
73400서울특별시 은평구122000서울특별시 은평구2015-12-15
83600서울특별시 서대문구120000서울특별시 서대문구2015-12-15
93900서울특별시 마포구121000서울특별시 마포구2015-12-15
산지코드산지명구산지코드구산지명업데이트일자
54490800세종특별자치시339000세종시 세종시2015-12-15
54591000경기도910000경기도2015-12-15
54692000강원도920000강원도2015-12-15
54793000충청남도930000충청남도2015-12-15
54894000충청북도940000충청북도2015-12-15
54995000전라남도950000전라남도2015-12-15
55096000전라북도960000전라북도2015-12-15
55197000경상남도970000경상남도2015-12-15
55298000경상북도980000경상북도2015-12-15
55399000제주특별자치도990000제주도2015-12-15