Overview

Dataset statistics

Number of variables7
Number of observations51
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory65.6 B

Variable types

Numeric7

Dataset

Description1971~2021년까지 농림축수산물 및 임산물 수출입 현황 등 백만불 단위의 교역 정보에 대한 통계자료입니다. .
Author산림청
URLhttps://www.data.go.kr/data/15069528/fileData.do

Alerts

연도(년) is highly overall correlated with 수출 국가전체(백만불) and 4 other fieldsHigh correlation
수출 국가전체(백만불) is highly overall correlated with 연도(년) and 4 other fieldsHigh correlation
수출 농림축수산물(백만불) is highly overall correlated with 연도(년) and 4 other fieldsHigh correlation
수입 국가전체(백만불) is highly overall correlated with 연도(년) and 4 other fieldsHigh correlation
수입 농림축수산물(백만불) is highly overall correlated with 연도(년) and 4 other fieldsHigh correlation
수입 임산물(백만불) is highly overall correlated with 연도(년) and 4 other fieldsHigh correlation
연도(년) has unique valuesUnique
수출 국가전체(백만불) has unique valuesUnique
수출 농림축수산물(백만불) has unique valuesUnique
수출 임산물(백만불) has unique valuesUnique
수입 농림축수산물(백만불) has unique valuesUnique
수입 임산물(백만불) has unique valuesUnique

Reproduction

Analysis started2023-12-12 19:11:55.983200
Analysis finished2023-12-12 19:12:01.657755
Duration5.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도(년)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1996
Minimum1971
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-13T04:12:01.752911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1971
5-th percentile1973.5
Q11983.5
median1996
Q32008.5
95-th percentile2018.5
Maximum2021
Range50
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.866069
Coefficient of variation (CV)0.0074479302
Kurtosis-1.2
Mean1996
Median Absolute Deviation (MAD)13
Skewness0
Sum101796
Variance221
MonotonicityStrictly increasing
2023-12-13T04:12:01.935004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1971 1
 
2.0%
1972 1
 
2.0%
1999 1
 
2.0%
2000 1
 
2.0%
2001 1
 
2.0%
2002 1
 
2.0%
2003 1
 
2.0%
2004 1
 
2.0%
2005 1
 
2.0%
2006 1
 
2.0%
Other values (41) 41
80.4%
ValueCountFrequency (%)
1971 1
2.0%
1972 1
2.0%
1973 1
2.0%
1974 1
2.0%
1975 1
2.0%
1976 1
2.0%
1977 1
2.0%
1978 1
2.0%
1979 1
2.0%
1980 1
2.0%
ValueCountFrequency (%)
2021 1
2.0%
2020 1
2.0%
2019 1
2.0%
2018 1
2.0%
2017 1
2.0%
2016 1
2.0%
2015 1
2.0%
2014 1
2.0%
2013 1
2.0%
2012 1
2.0%

수출 국가전체(백만불)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211218.55
Minimum1068
Maximum644400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-13T04:12:02.137321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1068
5-th percentile3842.5
Q126845
median129715
Q3396748
95-th percentile573179.5
Maximum644400
Range643332
Interquartile range (IQR)369903

Descriptive statistics

Standard deviation216083.44
Coefficient of variation (CV)1.0230325
Kurtosis-1.0441021
Mean211218.55
Median Absolute Deviation (MAD)117004
Skewness0.7522313
Sum10772146
Variance4.6692054 × 1010
MonotonicityNot monotonic
2023-12-13T04:12:02.333302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1068 1
 
2.0%
1624 1
 
2.0%
143685 1
 
2.0%
172268 1
 
2.0%
150439 1
 
2.0%
162471 1
 
2.0%
193817 1
 
2.0%
253845 1
 
2.0%
284419 1
 
2.0%
325465 1
 
2.0%
Other values (41) 41
80.4%
ValueCountFrequency (%)
1068 1
2.0%
1624 1
2.0%
3225 1
2.0%
4460 1
2.0%
5081 1
2.0%
7715 1
2.0%
10047 1
2.0%
12711 1
2.0%
15056 1
2.0%
17505 1
2.0%
ValueCountFrequency (%)
644400 1
2.0%
604860 1
2.0%
573694 1
2.0%
572665 1
2.0%
559649 1
2.0%
556514 1
2.0%
547870 1
2.0%
542333 1
2.0%
526757 1
2.0%
512498 1
2.0%

수출 농림축수산물(백만불)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3924.4902
Minimum285
Maximum11374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-13T04:12:02.512738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum285
5-th percentile681.5
Q11962
median3012
Q34653
95-th percentile9420.5
Maximum11374
Range11089
Interquartile range (IQR)2691

Descriptive statistics

Standard deviation2872.1901
Coefficient of variation (CV)0.73186323
Kurtosis0.01865948
Mean3924.4902
Median Absolute Deviation (MAD)1248
Skewness1.0699197
Sum200149
Variance8249475.8
MonotonicityNot monotonic
2023-12-13T04:12:02.656532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
285 1
 
2.0%
376 1
 
2.0%
3198 1
 
2.0%
3012 1
 
2.0%
2852 1
 
2.0%
2801 1
 
2.0%
2991 1
 
2.0%
3365 1
 
2.0%
3416 1
 
2.0%
3395 1
 
2.0%
Other values (41) 41
80.4%
ValueCountFrequency (%)
285 1
2.0%
376 1
2.0%
680 1
2.0%
683 1
2.0%
937 1
2.0%
1181 1
2.0%
1543 1
2.0%
1584 1
2.0%
1592 1
2.0%
1620 1
2.0%
ValueCountFrequency (%)
11374 1
2.0%
9868 1
2.0%
9541 1
2.0%
9300 1
2.0%
9153 1
2.0%
8593 1
2.0%
8250 1
2.0%
8028 1
2.0%
8006 1
2.0%
7876 1
2.0%

수출 임산물(백만불)
Real number (ℝ)

UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean366.90196
Minimum114
Maximum738
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-13T04:12:02.796611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum114
5-th percentile136.5
Q1238
median353
Q3483
95-th percentile637.5
Maximum738
Range624
Interquartile range (IQR)245

Descriptive statistics

Standard deviation167.54346
Coefficient of variation (CV)0.45664367
Kurtosis-0.89034627
Mean366.90196
Median Absolute Deviation (MAD)122
Skewness0.28261808
Sum18712
Variance28070.81
MonotonicityNot monotonic
2023-12-13T04:12:02.957132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151 1
 
2.0%
197 1
 
2.0%
298 1
 
2.0%
282 1
 
2.0%
231 1
 
2.0%
180 1
 
2.0%
188 1
 
2.0%
176 1
 
2.0%
162 1
 
2.0%
135 1
 
2.0%
Other values (41) 41
80.4%
ValueCountFrequency (%)
114 1
2.0%
124 1
2.0%
135 1
2.0%
138 1
2.0%
141 1
2.0%
151 1
2.0%
155 1
2.0%
162 1
2.0%
176 1
2.0%
180 1
2.0%
ValueCountFrequency (%)
738 1
2.0%
657 1
2.0%
644 1
2.0%
631 1
2.0%
629 1
2.0%
610 1
2.0%
592 1
2.0%
574 1
2.0%
549 1
2.0%
542 1
2.0%

수입 국가전체(백만불)
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195355.55
Minimum2394
Maximum615093
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-13T04:12:03.096429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2394
5-th percentile5546
Q128411.5
median119752
Q3381519.5
95-th percentile524945
Maximum615093
Range612699
Interquartile range (IQR)353108

Descriptive statistics

Standard deviation195346.66
Coefficient of variation (CV)0.99995449
Kurtosis-0.99093823
Mean195355.55
Median Absolute Deviation (MAD)104711
Skewness0.75641049
Sum9963133
Variance3.8160317 × 1010
MonotonicityNot monotonic
2023-12-13T04:12:03.228965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141098 2
 
3.9%
2394 1
 
2.0%
425212 1
 
2.0%
119752 1
 
2.0%
152126 1
 
2.0%
178827 1
 
2.0%
224463 1
 
2.0%
261238 1
 
2.0%
309383 1
 
2.0%
356846 1
 
2.0%
Other values (40) 40
78.4%
ValueCountFrequency (%)
2394 1
2.0%
2522 1
2.0%
4240 1
2.0%
6852 1
2.0%
7274 1
2.0%
8774 1
2.0%
10811 1
2.0%
14972 1
2.0%
20339 1
2.0%
22292 1
2.0%
ValueCountFrequency (%)
615093 1
2.0%
535202 1
2.0%
525515 1
2.0%
524375 1
2.0%
519584 1
2.0%
515561 1
2.0%
503259 1
2.0%
478478 1
2.0%
467633 1
2.0%
436499 1
2.0%

수입 농림축수산물(백만불)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13971.686
Minimum531
Maximum48053
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-13T04:12:03.380303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum531
5-th percentile1065.5
Q12890
median8564
Q322220
95-th percentile38715
Maximum48053
Range47522
Interquartile range (IQR)19330

Descriptive statistics

Standard deviation13675.936
Coefficient of variation (CV)0.97883218
Kurtosis-0.40755816
Mean13971.686
Median Absolute Deviation (MAD)6033
Skewness0.97773156
Sum712556
Variance1.8703123 × 108
MonotonicityNot monotonic
2023-12-13T04:12:03.531980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
598 1
 
2.0%
531 1
 
2.0%
8564 1
 
2.0%
9819 1
 
2.0%
10079 1
 
2.0%
11472 1
 
2.0%
12185 1
 
2.0%
13484 1
 
2.0%
14276 1
 
2.0%
16101 1
 
2.0%
Other values (41) 41
80.4%
ValueCountFrequency (%)
531 1
2.0%
598 1
2.0%
939 1
2.0%
1192 1
2.0%
1242 1
2.0%
1294 1
2.0%
1443 1
2.0%
1842 1
2.0%
2503 1
2.0%
2531 1
2.0%
ValueCountFrequency (%)
48053 1
2.0%
41422 1
2.0%
39875 1
2.0%
37555 1
2.0%
36140 1
2.0%
34776 1
2.0%
34462 1
2.0%
34304 1
2.0%
34193 1
2.0%
33422 1
2.0%

수입 임산물(백만불)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2452.4706
Minimum133
Maximum7822
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-13T04:12:03.684748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum133
5-th percentile278
Q1716
median1994
Q33384
95-th percentile6761
Maximum7822
Range7689
Interquartile range (IQR)2668

Descriptive statistics

Standard deviation2032.1376
Coefficient of variation (CV)0.82860836
Kurtosis0.48562667
Mean2452.4706
Median Absolute Deviation (MAD)1295
Skewness1.0944349
Sum125076
Variance4129583.4
MonotonicityNot monotonic
2023-12-13T04:12:03.829662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
162 1
 
2.0%
133 1
 
2.0%
1521 1
 
2.0%
1744 1
 
2.0%
1799 1
 
2.0%
2157 1
 
2.0%
2173 1
 
2.0%
2327 1
 
2.0%
2463 1
 
2.0%
2882 1
 
2.0%
Other values (41) 41
80.4%
ValueCountFrequency (%)
133 1
2.0%
162 1
2.0%
271 1
2.0%
285 1
2.0%
316 1
2.0%
418 1
2.0%
540 1
2.0%
624 1
2.0%
629 1
2.0%
671 1
2.0%
ValueCountFrequency (%)
7822 1
2.0%
7289 1
2.0%
7010 1
2.0%
6512 1
2.0%
6404 1
2.0%
5792 1
2.0%
4945 1
2.0%
4620 1
2.0%
4317 1
2.0%
3847 1
2.0%

Interactions

2023-12-13T04:12:00.499627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:56.166110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:56.680818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:57.313657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:57.970327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:58.667150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:59.720030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:12:00.623685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:56.233351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:56.757570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:57.389931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:58.082905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:58.778869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:59.815970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:12:00.731558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:56.306827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:56.848552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:57.519835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:58.170979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:58.871534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:59.910851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:12:00.848280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:56.378529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:56.933325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:57.596048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:58.257917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:58.976646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:12:00.012290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:12:00.996749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:56.453437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:57.024752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:57.683946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:58.374334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:59.094190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:12:00.147261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:12:01.129895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:56.533430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:57.117011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:57.788218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:58.480276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:59.193404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:12:00.281172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:12:01.278480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:56.601374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:57.210822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:57.870428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:58.564982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:59.609091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:12:00.383673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:12:03.932450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도(년)수출 국가전체(백만불)수출 농림축수산물(백만불)수출 임산물(백만불)수입 국가전체(백만불)수입 농림축수산물(백만불)수입 임산물(백만불)
연도(년)1.0000.9210.9480.7990.8330.8990.898
수출 국가전체(백만불)0.9211.0000.9110.5760.9870.9540.909
수출 농림축수산물(백만불)0.9480.9111.0000.0810.9240.9780.931
수출 임산물(백만불)0.7990.5760.0811.0000.5690.6560.631
수입 국가전체(백만불)0.8330.9870.9240.5691.0000.9640.852
수입 농림축수산물(백만불)0.8990.9540.9780.6560.9641.0000.931
수입 임산물(백만불)0.8980.9090.9310.6310.8520.9311.000
2023-12-13T04:12:04.049196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도(년)수출 국가전체(백만불)수출 농림축수산물(백만불)수출 임산물(백만불)수입 국가전체(백만불)수입 농림축수산물(백만불)수입 임산물(백만불)
연도(년)1.0000.9910.953-0.2190.9850.9850.951
수출 국가전체(백만불)0.9911.0000.947-0.2220.9950.9810.948
수출 농림축수산물(백만불)0.9530.9471.000-0.0600.9500.9650.958
수출 임산물(백만불)-0.219-0.222-0.0601.000-0.220-0.153-0.042
수입 국가전체(백만불)0.9850.9950.950-0.2201.0000.9830.957
수입 농림축수산물(백만불)0.9850.9810.965-0.1530.9831.0000.977
수입 임산물(백만불)0.9510.9480.958-0.0420.9570.9771.000

Missing values

2023-12-13T04:12:01.458181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:12:01.599439image/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

연도(년)수출 국가전체(백만불)수출 농림축수산물(백만불)수출 임산물(백만불)수입 국가전체(백만불)수입 농림축수산물(백만불)수입 임산물(백만불)
0197110682851512394598162
1197216243761972522531133
2197332256803524240939285
31974446068327068521242316
41975508193729472741294271
519767715118146087741192418
61977100471592549108111443540
71978127111764657149721842671
819791505619947382033929161047
91980175051930629222923164912
연도(년)수출 국가전체(백만불)수출 농림축수산물(백만불)수출 임산물(백만불)수입 국가전체(백만불)수입 농림축수산물(백만불)수입 임산물(백만불)
4120125478708006310519584334223847
4220135596497876410515561341934317
4320145726658250367525515361404945
4420155267578028272436499347764620
4520164954268593420406193344626404
4620175736949153434478478375557010
4720186048609300521535202414227822
4820195423339541407503259343046512
4920205124989868380467633398755792
50202164440011374451615093480537289