Overview

Dataset statistics

Number of variables6
Number of observations302
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.5 KiB
Average record size in memory52.4 B

Variable types

Numeric3
Categorical2
Text1

Dataset

Description국립농산물품질관리원에서 관리하는 농산물 방사능 검사현황 정보(년도, 구분, 품목, 검사건수, 적합건수, 부적합건수)
Author국립농산물품질관리원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220204000000001682

Alerts

구분 has constant value ""Constant
부적합건수 has constant value ""Constant
검사건수 is highly overall correlated with 적합건수High correlation
적합건수 is highly overall correlated with 검사건수High correlation

Reproduction

Analysis started2024-03-23 07:53:05.832197
Analysis finished2024-03-23 07:53:09.092888
Duration3.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

Distinct6
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.4073
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T07:53:09.187637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7396146
Coefficient of variation (CV)0.00086144813
Kurtosis-1.3487837
Mean2019.4073
Median Absolute Deviation (MAD)2
Skewness0.089957575
Sum609861
Variance3.026259
MonotonicityDecreasing
2024-03-23T07:53:09.547844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2018 63
20.9%
2021 53
17.5%
2017 53
17.5%
2022 48
15.9%
2019 43
14.2%
2020 42
13.9%
ValueCountFrequency (%)
2017 53
17.5%
2018 63
20.9%
2019 43
14.2%
2020 42
13.9%
2021 53
17.5%
2022 48
15.9%
ValueCountFrequency (%)
2022 48
15.9%
2021 53
17.5%
2020 42
13.9%
2019 43
14.2%
2018 63
20.9%
2017 53
17.5%

구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
국내산
302 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
국내산 302
100.0%

Length

2024-03-23T07:53:09.930979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T07:53:10.209290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국내산 302
100.0%

품목
Text

Distinct86
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2024-03-23T07:53:10.629913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length2.9635762
Min length1

Characters and Unicode

Total characters895
Distinct characters130
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

Unique25 ?
Unique (%)8.3%

Sample

1st row가지
2nd row감귤
3rd row감자
4th row고구마
5th row고구마순(고구마줄기)
ValueCountFrequency (%)
가지 6
 
2.0%
떫은감 6
 
2.0%
홍고추(붉은고추 6
 
2.0%
풋고추 6
 
2.0%
시금치 6
 
2.0%
양배추 6
 
2.0%
양파 6
 
2.0%
얼갈이배추 6
 
2.0%
열무 6
 
2.0%
상추 6
 
2.0%
Other values (76) 242
80.1%
2024-03-23T07:53:11.589705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
추 59
 
6.6%
고 39
 
4.4%
마 31
 
3.5%
배 28
 
3.1%
리 26
 
2.9%
감 24
 
2.7%
파 22
 
2.5%
토 22
 
2.5%
) 20
 
2.2%
( 20
 
2.2%
Other values (120) 604
67.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 854
95.4%
Close Punctuation 20
 
2.2%
Open Punctuation 20
 
2.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
추 59
 
6.9%
고 39
 
4.6%
마 31
 
3.6%
배 28
 
3.3%
리 26
 
3.0%
감 24
 
2.8%
파 22
 
2.6%
토 22
 
2.6%
양 19
 
2.2%
대 18
 
2.1%
Other values (117) 566
66.3%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 854
95.4%
Common 41
 
4.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
추 59
 
6.9%
고 39
 
4.6%
마 31
 
3.6%
배 28
 
3.3%
리 26
 
3.0%
감 24
 
2.8%
파 22
 
2.6%
토 22
 
2.6%
양 19
 
2.2%
대 18
 
2.1%
Other values (117) 566
66.3%
Common
ValueCountFrequency (%)
) 20
48.8%
( 20
48.8%
, 1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 854
95.4%
ASCII 41
 
4.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
추 59
 
6.9%
고 39
 
4.6%
마 31
 
3.6%
배 28
 
3.3%
리 26
 
3.0%
감 24
 
2.8%
파 22
 
2.6%
토 22
 
2.6%
양 19
 
2.2%
대 18
 
2.1%
Other values (117) 566
66.3%
ASCII
ValueCountFrequency (%)
) 20
48.8%
( 20
48.8%
, 1
 
2.4%

검사건수
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1225166
Minimum1
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T07:53:12.067427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q312
95-th percentile32
Maximum82
Range81
Interquartile range (IQR)10

Descriptive statistics

Standard deviation12.000895
Coefficient of variation (CV)1.3155246
Kurtosis9.7779327
Mean9.1225166
Median Absolute Deviation (MAD)3
Skewness2.7066166
Sum2755
Variance144.02148
MonotonicityNot monotonic
2024-03-23T07:53:12.478223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1 74
24.5%
2 33
 
10.9%
3 30
 
9.9%
4 23
 
7.6%
5 12
 
4.0%
7 12
 
4.0%
6 9
 
3.0%
8 9
 
3.0%
9 8
 
2.6%
16 8
 
2.6%
Other values (31) 84
27.8%
ValueCountFrequency (%)
1 74
24.5%
2 33
10.9%
3 30
9.9%
4 23
 
7.6%
5 12
 
4.0%
6 9
 
3.0%
7 12
 
4.0%
8 9
 
3.0%
9 8
 
2.6%
10 5
 
1.7%
ValueCountFrequency (%)
82 1
 
0.3%
75 1
 
0.3%
71 1
 
0.3%
49 1
 
0.3%
44 3
1.0%
42 2
0.7%
41 1
 
0.3%
40 1
 
0.3%
39 1
 
0.3%
37 1
 
0.3%

적합건수
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1225166
Minimum1
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T07:53:12.739019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q312
95-th percentile32
Maximum82
Range81
Interquartile range (IQR)10

Descriptive statistics

Standard deviation12.000895
Coefficient of variation (CV)1.3155246
Kurtosis9.7779327
Mean9.1225166
Median Absolute Deviation (MAD)3
Skewness2.7066166
Sum2755
Variance144.02148
MonotonicityNot monotonic
2024-03-23T07:53:12.996274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1 74
24.5%
2 33
 
10.9%
3 30
 
9.9%
4 23
 
7.6%
5 12
 
4.0%
7 12
 
4.0%
6 9
 
3.0%
8 9
 
3.0%
9 8
 
2.6%
16 8
 
2.6%
Other values (31) 84
27.8%
ValueCountFrequency (%)
1 74
24.5%
2 33
10.9%
3 30
9.9%
4 23
 
7.6%
5 12
 
4.0%
6 9
 
3.0%
7 12
 
4.0%
8 9
 
3.0%
9 8
 
2.6%
10 5
 
1.7%
ValueCountFrequency (%)
82 1
 
0.3%
75 1
 
0.3%
71 1
 
0.3%
49 1
 
0.3%
44 3
1.0%
42 2
0.7%
41 1
 
0.3%
40 1
 
0.3%
39 1
 
0.3%
37 1
 
0.3%

부적합건수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
0
302 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 302
100.0%

Length

2024-03-23T07:53:13.400785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T07:53:13.743326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 302
100.0%

Interactions

2024-03-23T07:53:07.749083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:53:06.215708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:53:06.962909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:53:07.917581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:53:06.438087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:53:07.229744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:53:08.100558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:53:06.700108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:53:07.478023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T07:53:13.918436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도품목검사건수적합건수
년도1.0000.0000.0000.000
품목0.0001.0000.2920.292
검사건수0.0000.2921.0001.000
적합건수0.0000.2921.0001.000
2024-03-23T07:53:14.160869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도검사건수적합건수
년도1.000-0.027-0.027
검사건수-0.0271.0001.000
적합건수-0.0271.0001.000

Missing values

2024-03-23T07:53:08.503366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T07:53:09.021652image/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

년도구분품목검사건수적합건수부적합건수
02022국내산가지440
12022국내산감귤110
22022국내산감자25250
32022국내산고구마32320
42022국내산고구마순(고구마줄기)220
52022국내산고사리110
62022국내산단감990
72022국내산대추330
82022국내산대파49490
92022국내산두릅110
년도구분품목검사건수적합건수부적합건수
2922017국내산유채110
2932017국내산쪽파12120
2942017국내산취나물330
2952017국내산토마토440
2962017국내산포도14140
2972017국내산표고버섯18180
2982017국내산풋고추44440
2992017국내산호박44440
3002017국내산호박잎220
3012017국내산홍고추(붉은고추)16160