Overview

Dataset statistics

Number of variables3
Number of observations40
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory28.3 B

Variable types

Numeric1
Categorical1
Text1

Dataset

Description농림수산식품교육문화정보원 농업온 시스템의 품목 정보에 대한 데이터로서품목 구분코드, 품목 대분류, 품목 중분류의 항목을 포함하고 있습니다.
Author농림수산식품교육문화정보원
URLhttps://www.data.go.kr/data/15122565/fileData.do

Alerts

품목 구분 코드 is highly overall correlated with 품목 대분류High correlation
품목 대분류 is highly overall correlated with 품목 구분 코드High correlation
품목 구분 코드 has unique valuesUnique
품목 중분류 has unique valuesUnique

Reproduction

Analysis started2023-12-12 21:56:31.673041
Analysis finished2023-12-12 21:56:32.071662
Duration0.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목 구분 코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean925.55
Minimum101
Maximum2402
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T06:56:32.159189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile202.9
Q1604.75
median802.5
Q31127.5
95-th percentile2044.75
Maximum2402
Range2301
Interquartile range (IQR)522.75

Descriptive statistics

Standard deviation552.81345
Coefficient of variation (CV)0.59728103
Kurtosis1.3771809
Mean925.55
Median Absolute Deviation (MAD)202
Skewness1.2489636
Sum37022
Variance305602.72
MonotonicityNot monotonic
2023-12-13T06:56:32.366719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1929 1
 
2.5%
101 1
 
2.5%
203 1
 
2.5%
301 1
 
2.5%
401 1
 
2.5%
501 1
 
2.5%
1001 1
 
2.5%
1005 1
 
2.5%
1008 1
 
2.5%
1101 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
101 1
2.5%
201 1
2.5%
203 1
2.5%
301 1
2.5%
401 1
2.5%
501 1
2.5%
601 1
2.5%
602 1
2.5%
603 1
2.5%
604 1
2.5%
ValueCountFrequency (%)
2402 1
2.5%
2401 1
2.5%
2026 1
2.5%
1933 1
2.5%
1929 1
2.5%
1326 1
2.5%
1209 1
2.5%
1205 1
2.5%
1202 1
2.5%
1201 1
2.5%

품목 대분류
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
과실류
14 
채소류
10 
과채류
7 
식량작물
6 
화훼류
3 

Length

Max length4
Median length3
Mean length3.15
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
과실류 14
35.0%
채소류 10
25.0%
과채류 7
17.5%
식량작물 6
15.0%
화훼류 3
 
7.5%

Length

2023-12-13T06:56:32.531080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:56:32.662611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
과실류 14
35.0%
채소류 10
25.0%
과채류 7
17.5%
식량작물 6
15.0%
화훼류 3
 
7.5%

품목 중분류
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-13T06:56:32.878017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.125
Min length1

Characters and Unicode

Total characters85
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)100.0%

Sample

1st row산수유
2nd row복분자
3rd row사과
4th row배
5th row포도
ValueCountFrequency (%)
산수유 1
 
2.5%
복분자 1
 
2.5%
시금치 1
 
2.5%
보리 1
 
2.5%
밀 1
 
2.5%
콩 1
 
2.5%
옥수수 1
 
2.5%
감자 1
 
2.5%
배추 1
 
2.5%
상추 1
 
2.5%
Other values (30) 30
75.0%
2023-12-13T06:56:33.207002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 4
 
4.7%
추 4
 
4.7%
감 3
 
3.5%
자 3
 
3.5%
파 3
 
3.5%
리 3
 
3.5%
배 2
 
2.4%
복 2
 
2.4%
참 2
 
2.4%
마 2
 
2.4%
Other values (54) 57
67.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 85
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 4
 
4.7%
추 4
 
4.7%
감 3
 
3.5%
자 3
 
3.5%
파 3
 
3.5%
리 3
 
3.5%
배 2
 
2.4%
복 2
 
2.4%
참 2
 
2.4%
마 2
 
2.4%
Other values (54) 57
67.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 85
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 4
 
4.7%
추 4
 
4.7%
감 3
 
3.5%
자 3
 
3.5%
파 3
 
3.5%
리 3
 
3.5%
배 2
 
2.4%
복 2
 
2.4%
참 2
 
2.4%
마 2
 
2.4%
Other values (54) 57
67.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 85
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 4
 
4.7%
추 4
 
4.7%
감 3
 
3.5%
자 3
 
3.5%
파 3
 
3.5%
리 3
 
3.5%
배 2
 
2.4%
복 2
 
2.4%
참 2
 
2.4%
마 2
 
2.4%
Other values (54) 57
67.1%

Interactions

2023-12-13T06:56:31.803807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:56:33.306491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품목 구분 코드품목 대분류품목 중분류
품목 구분 코드1.0000.9571.000
품목 대분류0.9571.0001.000
품목 중분류1.0001.0001.000
2023-12-13T06:56:33.408283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품목 구분 코드품목 대분류
품목 구분 코드1.0000.862
품목 대분류0.8621.000

Missing values

2023-12-13T06:56:31.947777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:56:32.040958image/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

품목 구분 코드품목 대분류품목 중분류
01929과실류산수유
11933과실류복분자
2601과실류사과
3602과실류배
4603과실류포도
5604과실류복숭아
6605과실류감
7608과실류자두
8610과실류살구
9611과실류참다래
품목 구분 코드품목 대분류품목 중분류
301101채소류무
311103채소류당근
321201채소류양파
331202채소류파
341205채소류고추
351209채소류마늘
361326채소류파프리카
372026화훼류장미
382401화훼류거베라
392402화훼류국화