Overview

Dataset statistics

Number of variables6
Number of observations350
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.9 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 07:53:15.343006
Analysis finished2024-03-23 07:53:18.406797
Duration3.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

Distinct7
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.9
Minimum2017
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-03-23T07:53:18.718532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2020
Q32022
95-th percentile2023
Maximum2023
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0351496
Coefficient of variation (CV)0.0010075497
Kurtosis-1.3196089
Mean2019.9
Median Absolute Deviation (MAD)2
Skewness0.06461542
Sum706965
Variance4.1418338
MonotonicityDecreasing
2024-03-23T07:53:19.073323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2018 63
18.0%
2021 53
15.1%
2017 53
15.1%
2023 48
13.7%
2022 48
13.7%
2019 43
12.3%
2020 42
12.0%
ValueCountFrequency (%)
2017 53
15.1%
2018 63
18.0%
2019 43
12.3%
2020 42
12.0%
2021 53
15.1%
2022 48
13.7%
2023 48
13.7%
ValueCountFrequency (%)
2023 48
13.7%
2022 48
13.7%
2021 53
15.1%
2020 42
12.0%
2019 43
12.3%
2018 63
18.0%
2017 53
15.1%

구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
국내산
350 

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 350
100.0%

Length

2024-03-23T07:53:19.468737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T07:53:19.828487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국내산 350
100.0%

품목
Text

Distinct92
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2024-03-23T07:53:20.204043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length2.9657143
Min length1

Characters and Unicode

Total characters1038
Distinct characters139
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

Unique29 ?
Unique (%)8.3%

Sample

1st row가지
2nd row감귤
3rd row감자
4th row고구마
5th row고사리
ValueCountFrequency (%)
가지 7
 
2.0%
풋고추 7
 
2.0%
감귤 7
 
2.0%
포도 7
 
2.0%
사과 7
 
2.0%
상추 7
 
2.0%
부추 7
 
2.0%
양배추 7
 
2.0%
양파 7
 
2.0%
얼갈이배추 7
 
2.0%
Other values (82) 280
80.0%
2024-03-23T07:53:20.832407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
추 69
 
6.6%
고 44
 
4.2%
마 36
 
3.5%
배 32
 
3.1%
리 30
 
2.9%
감 28
 
2.7%
토 26
 
2.5%
파 26
 
2.5%
) 23
 
2.2%
( 23
 
2.2%
Other values (129) 701
67.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 990
95.4%
Close Punctuation 23
 
2.2%
Open Punctuation 23
 
2.2%
Other Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
추 69
 
7.0%
고 44
 
4.4%
마 36
 
3.6%
배 32
 
3.2%
리 30
 
3.0%
감 28
 
2.8%
토 26
 
2.6%
파 26
 
2.6%
양 22
 
2.2%
대 20
 
2.0%
Other values (126) 657
66.4%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 990
95.4%
Common 48
 
4.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
추 69
 
7.0%
고 44
 
4.4%
마 36
 
3.6%
배 32
 
3.2%
리 30
 
3.0%
감 28
 
2.8%
토 26
 
2.6%
파 26
 
2.6%
양 22
 
2.2%
대 20
 
2.0%
Other values (126) 657
66.4%
Common
ValueCountFrequency (%)
) 23
47.9%
( 23
47.9%
, 2
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 990
95.4%
ASCII 48
 
4.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
추 69
 
7.0%
고 44
 
4.4%
마 36
 
3.6%
배 32
 
3.2%
리 30
 
3.0%
감 28
 
2.8%
토 26
 
2.6%
파 26
 
2.6%
양 22
 
2.2%
대 20
 
2.0%
Other values (126) 657
66.4%
ASCII
ValueCountFrequency (%)
) 23
47.9%
( 23
47.9%
, 2
 
4.2%

검사건수
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9
Minimum1
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-03-23T07:53:21.139771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q312
95-th percentile31.55
Maximum82
Range81
Interquartile range (IQR)10

Descriptive statistics

Standard deviation11.564135
Coefficient of variation (CV)1.2993411
Kurtosis10.107658
Mean8.9
Median Absolute Deviation (MAD)3
Skewness2.716868
Sum3115
Variance133.72923
MonotonicityNot monotonic
2024-03-23T07:53:21.403628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1 85
24.3%
2 38
 
10.9%
3 34
 
9.7%
4 31
 
8.9%
5 15
 
4.3%
7 12
 
3.4%
9 10
 
2.9%
8 10
 
2.9%
16 9
 
2.6%
11 9
 
2.6%
Other values (31) 97
27.7%
ValueCountFrequency (%)
1 85
24.3%
2 38
10.9%
3 34
 
9.7%
4 31
 
8.9%
5 15
 
4.3%
6 9
 
2.6%
7 12
 
3.4%
8 10
 
2.9%
9 10
 
2.9%
10 6
 
1.7%
ValueCountFrequency (%)
82 1
 
0.3%
75 1
 
0.3%
71 1
 
0.3%
49 1
 
0.3%
44 3
0.9%
42 2
0.6%
41 1
 
0.3%
40 1
 
0.3%
39 1
 
0.3%
37 1
 
0.3%

적합건수
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9
Minimum1
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-03-23T07:53:21.671809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q312
95-th percentile31.55
Maximum82
Range81
Interquartile range (IQR)10

Descriptive statistics

Standard deviation11.564135
Coefficient of variation (CV)1.2993411
Kurtosis10.107658
Mean8.9
Median Absolute Deviation (MAD)3
Skewness2.716868
Sum3115
Variance133.72923
MonotonicityNot monotonic
2024-03-23T07:53:22.106645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1 85
24.3%
2 38
 
10.9%
3 34
 
9.7%
4 31
 
8.9%
5 15
 
4.3%
7 12
 
3.4%
9 10
 
2.9%
8 10
 
2.9%
16 9
 
2.6%
11 9
 
2.6%
Other values (31) 97
27.7%
ValueCountFrequency (%)
1 85
24.3%
2 38
10.9%
3 34
 
9.7%
4 31
 
8.9%
5 15
 
4.3%
6 9
 
2.6%
7 12
 
3.4%
8 10
 
2.9%
9 10
 
2.9%
10 6
 
1.7%
ValueCountFrequency (%)
82 1
 
0.3%
75 1
 
0.3%
71 1
 
0.3%
49 1
 
0.3%
44 3
0.9%
42 2
0.6%
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 size2.9 KiB
0
350 

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 (%)
0 350
100.0%

Length

2024-03-23T07:53:22.402118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T07:53:22.592963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 350
100.0%

Interactions

2024-03-23T07:53:17.236776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:53:15.742707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:53:16.502058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:53:17.442869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:53:15.991704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:53:16.767151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:53:17.668633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:53:16.232151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:53:16.976722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T07:53:22.779843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도품목검사건수적합건수
년도1.0000.0000.0000.000
품목0.0001.0000.3830.383
검사건수0.0000.3831.0001.000
적합건수0.0000.3831.0001.000
2024-03-23T07:53:23.048803image/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

2024-03-23T07:53:17.982758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T07:53:18.178436image/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
02023국내산가지110
12023국내산감귤440
22023국내산감자22220
32023국내산고구마28280
42023국내산고사리440
52023국내산곤드레나물110
62023국내산단감550
72023국내산대추110
82023국내산대파36360
92023국내산두릅110
년도구분품목검사건수적합건수부적합건수
3402017국내산유채110
3412017국내산쪽파12120
3422017국내산취나물330
3432017국내산토마토440
3442017국내산포도14140
3452017국내산표고버섯18180
3462017국내산풋고추44440
3472017국내산호박44440
3482017국내산호박잎220
3492017국내산홍고추(붉은고추)16160