Overview

Dataset statistics

Number of variables2
Number of observations1583
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.9 KiB
Average record size in memory16.1 B

Variable types

Text2

Dataset

Description농수산물도매시장 수산물 시장도매인 시스템에서 추출한 품종별 원산지 목록(거래물량 많은 순)에 관한 데이터로 품종 및 원산지 정보를 제공합니다.
Author대구광역시
URLhttps://www.data.go.kr/data/15086039/fileData.do

Reproduction

Analysis started2023-12-12 00:44:28.527862
Analysis finished2023-12-12 00:44:28.801551
Duration0.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품종
Text

Distinct570
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
2023-12-12T09:44:29.029139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length5.701832
Min length2

Characters and Unicode

Total characters9026
Distinct characters221
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique248 ?
Unique (%)15.7%

Sample

1st row가자미(가공)
2nd row가자미(가공)
3rd row가자미(가공)
4th row가자미(가공)
5th row가자미(일반)
ValueCountFrequency (%)
냉동 649
21.1%
신선 389
 
12.7%
활 282
 
9.2%
건 166
 
5.4%
기타 51
 
1.7%
오징어 41
 
1.3%
게 34
 
1.1%
갈치 30
 
1.0%
고등어 29
 
0.9%
새우 28
 
0.9%
Other values (345) 1370
44.6%
2023-12-12T09:44:29.518973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1486
 
16.5%
동 663
 
7.3%
냉 649
 
7.2%
신 389
 
4.3%
선 389
 
4.3%
어 369
 
4.1%
기 295
 
3.3%
활 282
 
3.1%
타 205
 
2.3%
) 202
 
2.2%
Other values (211) 4097
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7136
79.1%
Space Separator 1486
 
16.5%
Close Punctuation 202
 
2.2%
Open Punctuation 202
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 663
 
9.3%
냉 649
 
9.1%
신 389
 
5.5%
선 389
 
5.5%
어 369
 
5.2%
기 295
 
4.1%
활 282
 
4.0%
타 205
 
2.9%
가 172
 
2.4%
치 172
 
2.4%
Other values (208) 3551
49.8%
Space Separator
ValueCountFrequency (%)
1486
100.0%
Close Punctuation
ValueCountFrequency (%)
) 202
100.0%
Open Punctuation
ValueCountFrequency (%)
( 202
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7136
79.1%
Common 1890
 
20.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 663
 
9.3%
냉 649
 
9.1%
신 389
 
5.5%
선 389
 
5.5%
어 369
 
5.2%
기 295
 
4.1%
활 282
 
4.0%
타 205
 
2.9%
가 172
 
2.4%
치 172
 
2.4%
Other values (208) 3551
49.8%
Common
ValueCountFrequency (%)
1486
78.6%
) 202
 
10.7%
( 202
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7136
79.1%
ASCII 1890
 
20.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1486
78.6%
) 202
 
10.7%
( 202
 
10.7%
Hangul
ValueCountFrequency (%)
동 663
 
9.3%
냉 649
 
9.1%
신 389
 
5.5%
선 389
 
5.5%
어 369
 
5.2%
기 295
 
4.1%
활 282
 
4.0%
타 205
 
2.9%
가 172
 
2.4%
치 172
 
2.4%
Other values (208) 3551
49.8%

원산지
Text

Distinct88
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
2023-12-12T09:44:29.740031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length2
Mean length3.0233733
Min length2

Characters and Unicode

Total characters4786
Distinct characters134
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)1.4%

Sample

1st row기니
2nd row세네갈
3rd row원양산
4th row한국
5th row기니
ValueCountFrequency (%)
한국 442
24.8%
중국 183
 
10.3%
러시아 78
 
4.4%
연방 78
 
4.4%
원양산 59
 
3.3%
베트남 56
 
3.1%
수입산 53
 
3.0%
일본 53
 
3.0%
동해산 49
 
2.8%
남해안 49
 
2.8%
Other values (88) 679
38.2%
2023-12-12T09:44:30.166589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
국 687
 
14.4%
한 442
 
9.2%
235
 
4.9%
시 191
 
4.0%
중 185
 
3.9%
남 181
 
3.8%
아 179
 
3.7%
산 173
 
3.6%
해 113
 
2.4%
수 84
 
1.8%
Other values (124) 2316
48.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4551
95.1%
Space Separator 235
 
4.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 687
 
15.1%
한 442
 
9.7%
시 191
 
4.2%
중 185
 
4.1%
남 181
 
4.0%
아 179
 
3.9%
산 173
 
3.8%
해 113
 
2.5%
수 84
 
1.8%
연 79
 
1.7%
Other values (123) 2237
49.2%
Space Separator
ValueCountFrequency (%)
235
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4551
95.1%
Common 235
 
4.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 687
 
15.1%
한 442
 
9.7%
시 191
 
4.2%
중 185
 
4.1%
남 181
 
4.0%
아 179
 
3.9%
산 173
 
3.8%
해 113
 
2.5%
수 84
 
1.8%
연 79
 
1.7%
Other values (123) 2237
49.2%
Common
ValueCountFrequency (%)
235
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4551
95.1%
ASCII 235
 
4.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
국 687
 
15.1%
한 442
 
9.7%
시 191
 
4.2%
중 185
 
4.1%
남 181
 
4.0%
아 179
 
3.9%
산 173
 
3.8%
해 113
 
2.5%
수 84
 
1.8%
연 79
 
1.7%
Other values (123) 2237
49.2%
ASCII
ValueCountFrequency (%)
235
100.0%

Missing values

2023-12-12T09:44:28.717669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:44:28.775275image/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

품종원산지
0가자미(가공)기니
1가자미(가공)세네갈
2가자미(가공)원양산
3가자미(가공)한국
4가자미(일반)기니
5가자미(일반)세네갈
6가자미(일반)스페인
7갈치속젓한국
8갈치젓한국
9건 가문어페루
품종원산지
1573활 해삼일본
1574활 해삼중국
1575활 해삼한국
1576활 홍삼치한국
1577활 홍어아르헨티나
1578활 홍합중국
1579활 홍합한국
1580활 홍해삼한국
1581활 황석어한국
1582황석어젓한국