Overview

Dataset statistics

Number of variables8
Number of observations108
Missing cells86
Missing cells (%)10.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 KiB
Average record size in memory70.2 B

Variable types

Numeric5
Categorical3

Dataset

Description- 제주도 친환경 농축산물의 농산물별 출하 건수, 출하량 등의 출하 정보를 제공합니다. - 데이터 제공처: KOSIS 국가통계포털
Author제주특별자치도 미래성장과
URLhttps://www.jejudatahub.net/data/view/data/897

Alerts

기준 연도 is highly overall correlated with 인증기관High correlation
출하 건수 is highly overall correlated with 농가 수(가구) and 1 other fieldsHigh correlation
농가 수(가구) is highly overall correlated with 출하 건수 and 2 other fieldsHigh correlation
면적(헥타르) is highly overall correlated with 농가 수(가구) and 3 other fieldsHigh correlation
출하량(톤) is highly overall correlated with 출하 건수 and 2 other fieldsHigh correlation
농축산물 구분 is highly overall correlated with 면적(헥타르)High correlation
인증기관 is highly overall correlated with 기준 연도 and 1 other fieldsHigh correlation
출하 건수 has 12 (11.1%) missing valuesMissing
농가 수(가구) has 12 (11.1%) missing valuesMissing
면적(헥타르) has 49 (45.4%) missing valuesMissing
출하량(톤) has 13 (12.0%) missing valuesMissing

Reproduction

Analysis started2023-12-11 19:41:09.430834
Analysis finished2023-12-11 19:41:13.837122
Duration4.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준 연도
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.0093
Minimum2011
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T04:41:13.922633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011
Q12012
median2013
Q32016
95-th percentile2019
Maximum2019
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6280136
Coefficient of variation (CV)0.0013048667
Kurtosis-1.0257541
Mean2014.0093
Median Absolute Deviation (MAD)2
Skewness0.52487561
Sum217513
Variance6.9064555
MonotonicityDecreasing
2023-12-12T04:41:14.549681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2012 22
20.4%
2011 22
20.4%
2014 11
10.2%
2013 11
10.2%
2015 10
9.3%
2019 8
 
7.4%
2018 8
 
7.4%
2017 8
 
7.4%
2016 8
 
7.4%
ValueCountFrequency (%)
2011 22
20.4%
2012 22
20.4%
2013 11
10.2%
2014 11
10.2%
2015 10
9.3%
2016 8
 
7.4%
2017 8
 
7.4%
2018 8
 
7.4%
2019 8
 
7.4%
ValueCountFrequency (%)
2019 8
 
7.4%
2018 8
 
7.4%
2017 8
 
7.4%
2016 8
 
7.4%
2015 10
9.3%
2014 11
10.2%
2013 11
10.2%
2012 22
20.4%
2011 22
20.4%

시군구
Categorical

Distinct2
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size996.0 B
제주시
57 
서귀포시
51 

Length

Max length4
Median length3
Mean length3.4722222
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제주시
2nd row제주시
3rd row제주시
4th row제주시
5th row서귀포시

Common Values

ValueCountFrequency (%)
제주시 57
52.8%
서귀포시 51
47.2%

Length

2023-12-12T04:41:14.754805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T04:41:14.914090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주시 57
52.8%
서귀포시 51
47.2%

농축산물 구분
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size996.0 B
유기농산물
22 
무농약농산물
22 
유기축산물
22 
무항상제축산물
22 
저농약농산물
14 

Length

Max length7
Median length6
Mean length5.6296296
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row유기농산물
2nd row무농약농산물
3rd row유기축산물
4th row무항상제축산물
5th row유기농산물

Common Values

ValueCountFrequency (%)
유기농산물 22
20.4%
무농약농산물 22
20.4%
유기축산물 22
20.4%
무항상제축산물 22
20.4%
저농약농산물 14
13.0%
전환기 6
 
5.6%

Length

2023-12-12T04:41:15.130549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T04:41:15.334443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유기농산물 22
20.4%
무농약농산물 22
20.4%
유기축산물 22
20.4%
무항상제축산물 22
20.4%
저농약농산물 14
13.0%
전환기 6
 
5.6%

인증기관
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size996.0 B
총계
64 
국립농산물품질관리원제주지원
22 
민간기관
22 

Length

Max length14
Median length2
Mean length4.8518519
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
총계 64
59.3%
국립농산물품질관리원제주지원 22
 
20.4%
민간기관 22
 
20.4%

Length

2023-12-12T04:41:15.539524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T04:41:15.727420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
총계 64
59.3%
국립농산물품질관리원제주지원 22
 
20.4%
민간기관 22
 
20.4%

출하 건수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct60
Distinct (%)62.5%
Missing12
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean103.83333
Minimum1
Maximum449
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T04:41:15.930700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q118.5
median61
Q3112.5
95-th percentile414.25
Maximum449
Range448
Interquartile range (IQR)94

Descriptive statistics

Standard deviation128.2132
Coefficient of variation (CV)1.2347981
Kurtosis0.96522432
Mean103.83333
Median Absolute Deviation (MAD)46.5
Skewness1.4874865
Sum9968
Variance16438.625
MonotonicityNot monotonic
2023-12-12T04:41:16.148874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 18
 
16.7%
24 4
 
3.7%
23 3
 
2.8%
61 3
 
2.8%
93 3
 
2.8%
1 2
 
1.9%
67 2
 
1.9%
12 2
 
1.9%
28 2
 
1.9%
75 2
 
1.9%
Other values (50) 55
50.9%
(Missing) 12
 
11.1%
ValueCountFrequency (%)
1 2
 
1.9%
2 18
16.7%
11 1
 
0.9%
12 2
 
1.9%
17 1
 
0.9%
19 2
 
1.9%
22 1
 
0.9%
23 3
 
2.8%
24 4
 
3.7%
26 1
 
0.9%
ValueCountFrequency (%)
449 1
0.9%
436 1
0.9%
428 1
0.9%
423 1
0.9%
415 1
0.9%
414 1
0.9%
411 1
0.9%
340 1
0.9%
332 1
0.9%
325 1
0.9%

농가 수(가구)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct67
Distinct (%)69.8%
Missing12
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean121.51042
Minimum1
Maximum547
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T04:41:16.379407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q121.25
median66.5
Q3166.25
95-th percentile461.5
Maximum547
Range546
Interquartile range (IQR)145

Descriptive statistics

Standard deviation143.04221
Coefficient of variation (CV)1.1772012
Kurtosis0.97634074
Mean121.51042
Median Absolute Deviation (MAD)61
Skewness1.4085311
Sum11665
Variance20461.074
MonotonicityNot monotonic
2023-12-12T04:41:16.645278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 18
 
16.7%
27 3
 
2.8%
23 3
 
2.8%
67 2
 
1.9%
1 2
 
1.9%
98 2
 
1.9%
24 2
 
1.9%
63 2
 
1.9%
121 2
 
1.9%
50 2
 
1.9%
Other values (57) 58
53.7%
(Missing) 12
 
11.1%
ValueCountFrequency (%)
1 2
 
1.9%
2 18
16.7%
12 2
 
1.9%
14 1
 
0.9%
19 1
 
0.9%
22 1
 
0.9%
23 3
 
2.8%
24 2
 
1.9%
26 1
 
0.9%
27 3
 
2.8%
ValueCountFrequency (%)
547 1
0.9%
499 1
0.9%
484 1
0.9%
472 1
0.9%
466 1
0.9%
460 1
0.9%
454 1
0.9%
381 1
0.9%
361 1
0.9%
347 1
0.9%

면적(헥타르)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct58
Distinct (%)98.3%
Missing49
Missing (%)45.4%
Infinite0
Infinite (%)0.0%
Mean377.27119
Minimum11
Maximum842
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T04:41:16.891618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile17.6
Q177.5
median415
Q3567.5
95-th percentile817.2
Maximum842
Range831
Interquartile range (IQR)490

Descriptive statistics

Standard deviation272.24272
Coefficient of variation (CV)0.72161015
Kurtosis-1.3133096
Mean377.27119
Median Absolute Deviation (MAD)234
Skewness0.024772961
Sum22259
Variance74116.098
MonotonicityNot monotonic
2023-12-12T04:41:17.139915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 2
 
1.9%
43 1
 
0.9%
490 1
 
0.9%
66 1
 
0.9%
61 1
 
0.9%
573 1
 
0.9%
165 1
 
0.9%
415 1
 
0.9%
21 1
 
0.9%
44 1
 
0.9%
Other values (48) 48
44.4%
(Missing) 49
45.4%
ValueCountFrequency (%)
11 2
1.9%
14 1
0.9%
18 1
0.9%
21 1
0.9%
27 1
0.9%
28 1
0.9%
34 1
0.9%
43 1
0.9%
44 1
0.9%
45 1
0.9%
ValueCountFrequency (%)
842 1
0.9%
832 1
0.9%
819 1
0.9%
817 1
0.9%
805 1
0.9%
788 1
0.9%
778 1
0.9%
686 1
0.9%
673 1
0.9%
644 1
0.9%

출하량(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct95
Distinct (%)100.0%
Missing13
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean3376.2
Minimum16
Maximum23197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T04:41:17.385105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile61.1
Q11013
median1976
Q34761
95-th percentile9015.9
Maximum23197
Range23181
Interquartile range (IQR)3748

Descriptive statistics

Standard deviation3828.2394
Coefficient of variation (CV)1.13389
Kurtosis11.561107
Mean3376.2
Median Absolute Deviation (MAD)1580
Skewness2.8513746
Sum320739
Variance14655417
MonotonicityNot monotonic
2023-12-12T04:41:17.648095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5542 1
 
0.9%
3396 1
 
0.9%
6118 1
 
0.9%
3504 1
 
0.9%
176 1
 
0.9%
478 1
 
0.9%
2840 1
 
0.9%
3013 1
 
0.9%
2591 1
 
0.9%
1261 1
 
0.9%
Other values (85) 85
78.7%
(Missing) 13
 
12.0%
ValueCountFrequency (%)
16 1
0.9%
17 1
0.9%
18 1
0.9%
36 1
0.9%
59 1
0.9%
62 1
0.9%
80 1
0.9%
154 1
0.9%
176 1
0.9%
260 1
0.9%
ValueCountFrequency (%)
23197 1
0.9%
21675 1
0.9%
9875 1
0.9%
9707 1
0.9%
9543 1
0.9%
8790 1
0.9%
8695 1
0.9%
8616 1
0.9%
7911 1
0.9%
7672 1
0.9%

Interactions

2023-12-12T04:41:12.573085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:09.927863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:10.660887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:11.267521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:11.919755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:12.746221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:10.088008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:10.799676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:11.423574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:12.078172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:12.877916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:10.230712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:10.925408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:11.544767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:12.203371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:13.009827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:10.392181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:11.040787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:11.673050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:12.318170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:13.146240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:10.530214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:11.154009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:11.797709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:41:12.440016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:41:17.831392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준 연도시군구농축산물 구분인증기관출하 건수농가 수(가구)면적(헥타르)출하량(톤)
기준 연도1.0000.0000.0000.7660.0000.0000.0000.265
시군구0.0001.0000.1130.0000.3000.3460.3760.432
농축산물 구분0.0000.1131.0000.0000.6760.6980.7370.520
인증기관0.7660.0000.0001.0000.3070.0000.8540.261
출하 건수0.0000.3000.6760.3071.0000.9740.7790.574
농가 수(가구)0.0000.3460.6980.0000.9741.0000.8290.598
면적(헥타르)0.0000.3760.7370.8540.7790.8291.0000.526
출하량(톤)0.2650.4320.5200.2610.5740.5980.5261.000
2023-12-12T04:41:18.041145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인증기관농축산물 구분시군구
인증기관1.0000.0000.000
농축산물 구분0.0001.0000.077
시군구0.0000.0771.000
2023-12-12T04:41:18.230356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준 연도출하 건수농가 수(가구)면적(헥타르)출하량(톤)시군구농축산물 구분인증기관
기준 연도1.0000.1040.0650.4490.3500.0000.0000.658
출하 건수0.1041.0000.9850.4620.5840.2870.4060.134
농가 수(가구)0.0650.9851.0000.5410.5700.3320.4270.000
면적(헥타르)0.4490.4620.5411.0000.6770.3500.5490.537
출하량(톤)0.3500.5840.5700.6771.0000.3040.2090.107
시군구0.0000.2870.3320.3500.3041.0000.0770.000
농축산물 구분0.0000.4060.4270.5490.2090.0771.0000.000
인증기관0.6580.1340.0000.5370.1070.0000.0001.000

Missing values

2023-12-12T04:41:13.327854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:41:13.545399image/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.
2023-12-12T04:41:13.738239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

기준 연도시군구농축산물 구분인증기관출하 건수농가 수(가구)면적(헥타르)출하량(톤)
02019제주시유기농산물총계1882395365283
12019제주시무농약농산물총계4364664695542
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