Overview

Dataset statistics

Number of variables6
Number of observations620
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.6 KiB
Average record size in memory52.2 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 07:52:35.392488
Analysis finished2024-03-23 07:52:38.525281
Duration3.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

Distinct11
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.0806
Minimum2010
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-03-23T07:52:38.724767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2011
Q12013
median2015
Q32017
95-th percentile2020
Maximum2020
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6233596
Coefficient of variation (CV)0.0013018633
Kurtosis-0.95723314
Mean2015.0806
Median Absolute Deviation (MAD)2
Skewness0.19579527
Sum1249350
Variance6.8820157
MonotonicityDecreasing
2024-03-23T07:52:39.083012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2015 90
14.5%
2014 81
13.1%
2013 76
12.3%
2012 73
11.8%
2018 63
10.2%
2016 58
9.4%
2017 53
8.5%
2019 43
6.9%
2011 42
6.8%
2020 35
 
5.6%
ValueCountFrequency (%)
2010 6
 
1.0%
2011 42
6.8%
2012 73
11.8%
2013 76
12.3%
2014 81
13.1%
2015 90
14.5%
2016 58
9.4%
2017 53
8.5%
2018 63
10.2%
2019 43
6.9%
ValueCountFrequency (%)
2020 35
 
5.6%
2019 43
6.9%
2018 63
10.2%
2017 53
8.5%
2016 58
9.4%
2015 90
14.5%
2014 81
13.1%
2013 76
12.3%
2012 73
11.8%
2011 42
6.8%

구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
국내산
620 

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 620
100.0%

Length

2024-03-23T07:52:39.298131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T07:52:39.466872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국내산 620
100.0%

품목
Text

Distinct128
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
2024-03-23T07:52:39.860636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length3.0806452
Min length1

Characters and Unicode

Total characters1910
Distinct characters170
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

Unique28 ?
Unique (%)4.5%

Sample

1st row가지
2nd row감자
3rd row고구마순(고구마줄기)
4th row고사리
5th row근대
ValueCountFrequency (%)
풋고추 11
 
1.8%
대파 11
 
1.8%
가지 10
 
1.6%
얼갈이배추 10
 
1.6%
오이 10
 
1.6%
쪽파 10
 
1.6%
상추 10
 
1.6%
홍고추(붉은고추 10
 
1.6%
열무 10
 
1.6%
호박 10
 
1.6%
Other values (118) 518
83.5%
2024-03-23T07:52:41.410068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
추 118
 
6.2%
고 83
 
4.3%
리 66
 
3.5%
( 56
 
2.9%
) 56
 
2.9%
배 52
 
2.7%
마 50
 
2.6%
파 42
 
2.2%
무 41
 
2.1%
나 41
 
2.1%
Other values (160) 1305
68.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1795
94.0%
Open Punctuation 56
 
2.9%
Close Punctuation 56
 
2.9%
Other Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
추 118
 
6.6%
고 83
 
4.6%
리 66
 
3.7%
배 52
 
2.9%
마 50
 
2.8%
파 42
 
2.3%
무 41
 
2.3%
나 41
 
2.3%
양 40
 
2.2%
감 35
 
1.9%
Other values (157) 1227
68.4%
Open Punctuation
ValueCountFrequency (%)
( 56
100.0%
Close Punctuation
ValueCountFrequency (%)
) 56
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1795
94.0%
Common 115
 
6.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
추 118
 
6.6%
고 83
 
4.6%
리 66
 
3.7%
배 52
 
2.9%
마 50
 
2.8%
파 42
 
2.3%
무 41
 
2.3%
나 41
 
2.3%
양 40
 
2.2%
감 35
 
1.9%
Other values (157) 1227
68.4%
Common
ValueCountFrequency (%)
( 56
48.7%
) 56
48.7%
, 3
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1795
94.0%
ASCII 115
 
6.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
추 118
 
6.6%
고 83
 
4.6%
리 66
 
3.7%
배 52
 
2.9%
마 50
 
2.8%
파 42
 
2.3%
무 41
 
2.3%
나 41
 
2.3%
양 40
 
2.2%
감 35
 
1.9%
Other values (157) 1227
68.4%
ASCII
ValueCountFrequency (%)
( 56
48.7%
) 56
48.7%
, 3
 
2.6%

검사건수
Real number (ℝ)

HIGH CORRELATION 

Distinct85
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.582258
Minimum1
Maximum238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-03-23T07:52:41.946798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q316
95-th percentile66.05
Maximum238
Range237
Interquartile range (IQR)15

Descriptive statistics

Standard deviation29.619417
Coefficient of variation (CV)1.9008424
Kurtosis20.088721
Mean15.582258
Median Absolute Deviation (MAD)3
Skewness4.0059786
Sum9661
Variance877.30986
MonotonicityNot monotonic
2024-03-23T07:52:42.485212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 166
26.8%
2 72
 
11.6%
3 56
 
9.0%
4 34
 
5.5%
5 22
 
3.5%
6 19
 
3.1%
8 16
 
2.6%
7 14
 
2.3%
16 13
 
2.1%
9 12
 
1.9%
Other values (75) 196
31.6%
ValueCountFrequency (%)
1 166
26.8%
2 72
11.6%
3 56
 
9.0%
4 34
 
5.5%
5 22
 
3.5%
6 19
 
3.1%
7 14
 
2.3%
8 16
 
2.6%
9 12
 
1.9%
10 9
 
1.5%
ValueCountFrequency (%)
238 1
0.2%
231 1
0.2%
198 1
0.2%
196 1
0.2%
195 1
0.2%
169 1
0.2%
162 1
0.2%
160 1
0.2%
158 1
0.2%
152 1
0.2%

적합건수
Real number (ℝ)

HIGH CORRELATION 

Distinct85
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.582258
Minimum1
Maximum238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-03-23T07:52:42.975526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q316
95-th percentile66.05
Maximum238
Range237
Interquartile range (IQR)15

Descriptive statistics

Standard deviation29.619417
Coefficient of variation (CV)1.9008424
Kurtosis20.088721
Mean15.582258
Median Absolute Deviation (MAD)3
Skewness4.0059786
Sum9661
Variance877.30986
MonotonicityNot monotonic
2024-03-23T07:52:43.538915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 166
26.8%
2 72
 
11.6%
3 56
 
9.0%
4 34
 
5.5%
5 22
 
3.5%
6 19
 
3.1%
8 16
 
2.6%
7 14
 
2.3%
16 13
 
2.1%
9 12
 
1.9%
Other values (75) 196
31.6%
ValueCountFrequency (%)
1 166
26.8%
2 72
11.6%
3 56
 
9.0%
4 34
 
5.5%
5 22
 
3.5%
6 19
 
3.1%
7 14
 
2.3%
8 16
 
2.6%
9 12
 
1.9%
10 9
 
1.5%
ValueCountFrequency (%)
238 1
0.2%
231 1
0.2%
198 1
0.2%
196 1
0.2%
195 1
0.2%
169 1
0.2%
162 1
0.2%
160 1
0.2%
158 1
0.2%
152 1
0.2%

부적합건수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0
620 

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 (%)
0 620
100.0%

Length

2024-03-23T07:52:44.054684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T07:52:44.222757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 620
100.0%

Interactions

2024-03-23T07:52:37.196458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:35.733553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:36.496142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:37.450099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:35.994348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:36.713456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:37.681314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:36.251545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:36.958950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T07:52:44.325433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도검사건수적합건수
년도1.0000.0000.000
검사건수0.0001.0001.000
적합건수0.0001.0001.000
2024-03-23T07:52:44.521701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도검사건수적합건수
년도1.000-0.077-0.077
검사건수-0.0771.0001.000
적합건수-0.0771.0001.000

Missing values

2024-03-23T07:52:38.041790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T07:52:38.389053image/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
02020국내산가지110
12020국내산감자24240
22020국내산고구마순(고구마줄기)110
32020국내산고사리110
42020국내산근대110
52020국내산대파26260
62020국내산들깻잎220
72020국내산마늘12120
82020국내산매실110
92020국내산머위대110
년도구분품목검사건수적합건수부적합건수
6102011국내산포도220
6112011국내산풋고추28280
6122011국내산호박880
6132011국내산홍고추(붉은고추)10100
6142010국내산당근110
6152010국내산대파110
6162010국내산배330
6172010국내산벼50500
6182010국내산콩440
6192010국내산풋고추110