Overview

Dataset statistics

Number of variables8
Number of observations449
Missing cells412
Missing cells (%)11.5%
Duplicate rows1
Duplicate rows (%)0.2%
Total size in memory30.4 KiB
Average record size in memory69.3 B

Variable types

Text1
Categorical2
Numeric5

Dataset

Description국가별 농산물 교역현황 정보 제공 제공 항목 : 국가별 교역현황(전체 수출입 실적, 농림축산물 교역 동향, 농림축산물 수출입 상위 10개 품목)
Author농림축산식품부
URLhttps://www.data.go.kr/data/15075400/fileData.do

Alerts

Dataset has 1 (0.2%) duplicate rowsDuplicates
구분1 is highly overall correlated with 구분2High correlation
구분2 is highly overall correlated with 구분1High correlation
2016(단위-천불) is highly overall correlated with 2017(단위-천불) and 3 other fieldsHigh correlation
2017(단위-천불) is highly overall correlated with 2016(단위-천불) and 3 other fieldsHigh correlation
2018(단위-천불) is highly overall correlated with 2016(단위-천불) and 3 other fieldsHigh correlation
2019(단위-천불) is highly overall correlated with 2016(단위-천불) and 3 other fieldsHigh correlation
2020(단위-천불) is highly overall correlated with 2016(단위-천불) and 3 other fieldsHigh correlation
국가 has 385 (85.7%) missing valuesMissing
2016(단위-천불) has 5 (1.1%) missing valuesMissing
2018(단위-천불) has 5 (1.1%) missing valuesMissing
2020(단위-천불) has 10 (2.2%) missing valuesMissing
2016(단위-천불) has 20 (4.5%) zerosZeros
2017(단위-천불) has 19 (4.2%) zerosZeros
2018(단위-천불) has 19 (4.2%) zerosZeros
2019(단위-천불) has 19 (4.2%) zerosZeros
2020(단위-천불) has 15 (3.3%) zerosZeros

Reproduction

Analysis started2023-12-12 21:05:29.419215
Analysis finished2023-12-12 21:05:32.744941
Duration3.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

국가
Text

MISSING 

Distinct64
Distinct (%)100.0%
Missing385
Missing (%)85.7%
Memory size3.6 KiB
2023-12-13T06:05:32.927300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.4375
Min length2

Characters and Unicode

Total characters220
Distinct characters101
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)100.0%

Sample

1st row대세계
2nd row칠레
3rd row싱가포르
4th rowEFTA
5th row아세안
ValueCountFrequency (%)
그리스 1
 
1.6%
중국 1
 
1.6%
덴마크 1
 
1.6%
독일 1
 
1.6%
라트비아 1
 
1.6%
루마니아 1
 
1.6%
룩셈부르크 1
 
1.6%
리투아니아 1
 
1.6%
몰타 1
 
1.6%
벨기에 1
 
1.6%
Other values (54) 54
84.4%
2023-12-13T06:05:33.293441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 19
 
8.6%
스 13
 
5.9%
리 8
 
3.6%
이 6
 
2.7%
드 6
 
2.7%
라 6
 
2.7%
르 5
 
2.3%
니 5
 
2.3%
란 4
 
1.8%
로 4
 
1.8%
Other values (91) 144
65.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 214
97.3%
Uppercase Letter 6
 
2.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 19
 
8.9%
스 13
 
6.1%
리 8
 
3.7%
이 6
 
2.8%
드 6
 
2.8%
라 6
 
2.8%
르 5
 
2.3%
니 5
 
2.3%
란 4
 
1.9%
로 4
 
1.9%
Other values (86) 138
64.5%
Uppercase Letter
ValueCountFrequency (%)
E 2
33.3%
F 1
16.7%
T 1
16.7%
A 1
16.7%
U 1
16.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 214
97.3%
Latin 6
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 19
 
8.9%
스 13
 
6.1%
리 8
 
3.7%
이 6
 
2.8%
드 6
 
2.8%
라 6
 
2.8%
르 5
 
2.3%
니 5
 
2.3%
란 4
 
1.9%
로 4
 
1.9%
Other values (86) 138
64.5%
Latin
ValueCountFrequency (%)
E 2
33.3%
F 1
16.7%
T 1
16.7%
A 1
16.7%
U 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 214
97.3%
ASCII 6
 
2.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 19
 
8.9%
스 13
 
6.1%
리 8
 
3.7%
이 6
 
2.8%
드 6
 
2.8%
라 6
 
2.8%
르 5
 
2.3%
니 5
 
2.3%
란 4
 
1.9%
로 4
 
1.9%
Other values (86) 138
64.5%
ASCII
ValueCountFrequency (%)
E 2
33.3%
F 1
16.7%
T 1
16.7%
A 1
16.7%
U 1
16.7%

구분1
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
<NA>
217 
수출
38 
수입
38 
무역수지
37 
(B)
 
20
Other values (8)
99 

Length

Max length11
Median length4
Mean length3.8240535
Min length2

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row수 출
2nd row(B)
3rd row<NA>
4th row수 입
5th row(A)

Common Values

ValueCountFrequency (%)
<NA> 217
48.3%
수출 38
 
8.5%
수입 38
 
8.5%
무역수지 37
 
8.2%
(B) 20
 
4.5%
(A) 20
 
4.5%
수 출 19
 
4.2%
수 입 19
 
4.2%
무역수지(B-A) 18
 
4.0%
무역수지(A-B) 8
 
1.8%
Other values (3) 15
 
3.3%

Length

2023-12-13T06:05:33.429128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 217
44.5%
수출 38
 
7.8%
수입 38
 
7.8%
무역수지 38
 
7.8%
수 38
 
7.8%
b 20
 
4.1%
a 20
 
4.1%
출 19
 
3.9%
입 19
 
3.9%
무역수지(b-a 18
 
3.7%
Other values (4) 23
 
4.7%

구분2
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
농산물
128 
축산물
128 
임산물
128 
<NA>
65 

Length

Max length4
Median length3
Mean length3.1447661
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row농산물
2nd row축산물
3rd row임산물
4th row농산물
5th row축산물

Common Values

ValueCountFrequency (%)
농산물 128
28.5%
축산물 128
28.5%
임산물 128
28.5%
<NA> 65
14.5%

Length

2023-12-13T06:05:33.570779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:05:33.734137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농산물 128
28.5%
축산물 128
28.5%
임산물 128
28.5%
na 65
14.5%

2016(단위-천불)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct402
Distinct (%)90.5%
Missing5
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean29157.099
Minimum-6174435
Maximum4349092
Zeros20
Zeros (%)4.5%
Negative55
Negative (%)12.2%
Memory size4.1 KiB
2023-12-13T06:05:33.913600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6174435
5-th percentile-112398.65
Q132.75
median1715
Q322634.5
95-th percentile407597.7
Maximum4349092
Range10523527
Interquartile range (IQR)22601.75

Descriptive statistics

Standard deviation548036.04
Coefficient of variation (CV)18.795973
Kurtosis54.687137
Mean29157.099
Median Absolute Deviation (MAD)4107.5
Skewness-2.9322676
Sum12945752
Variance3.003435 × 1011
MonotonicityNot monotonic
2023-12-13T06:05:34.427506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20
 
4.5%
1 7
 
1.6%
82 3
 
0.7%
2 3
 
0.7%
32 3
 
0.7%
5 2
 
0.4%
6 2
 
0.4%
22 2
 
0.4%
10 2
 
0.4%
205 2
 
0.4%
Other values (392) 398
88.6%
(Missing) 5
 
1.1%
ValueCountFrequency (%)
-6174435 1
0.2%
-3328146 1
0.2%
-3177442 1
0.2%
-2785475 1
0.2%
-2436816 1
0.2%
-936248 1
0.2%
-829396 1
0.2%
-813547 1
0.2%
-714595 1
0.2%
-669191 1
0.2%
ValueCountFrequency (%)
4349092 1
0.2%
2532213 1
0.2%
2326481 1
0.2%
1824301 1
0.2%
1765724 1
0.2%
1713129 1
0.2%
1463632 1
0.2%
1399036 1
0.2%
1285291 1
0.2%
1267774 1
0.2%

2017(단위-천불)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct402
Distinct (%)90.3%
Missing4
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean30735.072
Minimum-7083184
Maximum4886992
Zeros19
Zeros (%)4.2%
Negative56
Negative (%)12.5%
Memory size4.1 KiB
2023-12-13T06:05:34.595651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-7083184
5-th percentile-137500
Q129
median1854
Q325838
95-th percentile465911.8
Maximum4886992
Range11970176
Interquartile range (IQR)25809

Descriptive statistics

Standard deviation616513.22
Coefficient of variation (CV)20.058948
Kurtosis57.240929
Mean30735.072
Median Absolute Deviation (MAD)4012
Skewness-3.1852854
Sum13677107
Variance3.8008855 × 1011
MonotonicityNot monotonic
2023-12-13T06:05:34.773943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
4.2%
2 8
 
1.8%
1 6
 
1.3%
29 3
 
0.7%
5 3
 
0.7%
3 2
 
0.4%
67 2
 
0.4%
70 2
 
0.4%
11 2
 
0.4%
34 2
 
0.4%
Other values (392) 396
88.2%
(Missing) 4
 
0.9%
ValueCountFrequency (%)
-7083184 1
0.2%
-3769729 1
0.2%
-3468623 1
0.2%
-3325474 1
0.2%
-2647270 1
0.2%
-1019256 1
0.2%
-934850 1
0.2%
-885532 1
0.2%
-750644 1
0.2%
-723565 1
0.2%
ValueCountFrequency (%)
4886992 1
0.2%
2553990 1
0.2%
2543796 1
0.2%
2181332 1
0.2%
1989530 1
0.2%
1848553 1
0.2%
1713063 1
0.2%
1695478 1
0.2%
1477319 1
0.2%
1299408 1
0.2%

2018(단위-천불)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct396
Distinct (%)89.2%
Missing5
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean32080.457
Minimum-8571033
Maximum5848961
Zeros19
Zeros (%)4.2%
Negative57
Negative (%)12.7%
Memory size4.1 KiB
2023-12-13T06:05:34.950854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-8571033
5-th percentile-161593.25
Q118
median1894
Q325000.75
95-th percentile528891.5
Maximum5848961
Range14419994
Interquartile range (IQR)24982.75

Descriptive statistics

Standard deviation709358.85
Coefficient of variation (CV)22.111869
Kurtosis66.364185
Mean32080.457
Median Absolute Deviation (MAD)4344
Skewness-3.45925
Sum14243723
Variance5.0318998 × 1011
MonotonicityNot monotonic
2023-12-13T06:05:35.126952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
4.2%
6 4
 
0.9%
4 4
 
0.9%
1 4
 
0.9%
7 4
 
0.9%
18 2
 
0.4%
2194 2
 
0.4%
131 2
 
0.4%
54 2
 
0.4%
10 2
 
0.4%
Other values (386) 399
88.9%
(Missing) 5
 
1.1%
ValueCountFrequency (%)
-8571033 1
0.2%
-4166036 1
0.2%
-4108721 1
0.2%
-3492274 1
0.2%
-2403610 1
0.2%
-1193841 1
0.2%
-1000678 1
0.2%
-979084 1
0.2%
-957862 1
0.2%
-906769 1
0.2%
ValueCountFrequency (%)
5848961 1
0.2%
2898309 1
0.2%
2735670 1
0.2%
2689385 1
0.2%
2360057 1
0.2%
2254850 1
0.2%
1778851 1
0.2%
1708339 1
0.2%
1397349 1
0.2%
1234109 1
0.2%

2019(단위-천불)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct404
Distinct (%)90.6%
Missing3
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean33117.166
Minimum-8079144
Maximum5312557
Zeros19
Zeros (%)4.2%
Negative57
Negative (%)12.7%
Memory size4.1 KiB
2023-12-13T06:05:35.286072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-8079144
5-th percentile-123618.75
Q138.5
median1951
Q323689.5
95-th percentile466574.25
Maximum5312557
Range13391701
Interquartile range (IQR)23651

Descriptive statistics

Standard deviation667977.77
Coefficient of variation (CV)20.170137
Kurtosis65.446133
Mean33117.166
Median Absolute Deviation (MAD)4065.5
Skewness-3.5025742
Sum14770256
Variance4.4619431 × 1011
MonotonicityNot monotonic
2023-12-13T06:05:35.479850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
4.2%
1 8
 
1.8%
4 4
 
0.9%
1401 3
 
0.7%
26 3
 
0.7%
2 2
 
0.4%
98 2
 
0.4%
87 2
 
0.4%
579 2
 
0.4%
19 2
 
0.4%
Other values (394) 399
88.9%
(Missing) 3
 
0.7%
ValueCountFrequency (%)
-8079144 1
0.2%
-4203764 1
0.2%
-3500055 1
0.2%
-3304801 1
0.2%
-2321523 1
0.2%
-1085037 1
0.2%
-901200 1
0.2%
-890763 1
0.2%
-881326 1
0.2%
-811490 1
0.2%
ValueCountFrequency (%)
5312557 1
0.2%
2946022 1
0.2%
2721773 1
0.2%
2673413 1
0.2%
2309020 1
0.2%
1971395 1
0.2%
1735742 1
0.2%
1488650 1
0.2%
1417437 1
0.2%
1331653 1
0.2%

2020(단위-천불)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct402
Distinct (%)91.6%
Missing10
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean36340.182
Minimum-7654069
Maximum5401868
Zeros15
Zeros (%)3.3%
Negative56
Negative (%)12.5%
Memory size4.1 KiB
2023-12-13T06:05:35.658419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-7654069
5-th percentile-180666.8
Q140.5
median2349
Q326785
95-th percentile478507.4
Maximum5401868
Range13055937
Interquartile range (IQR)26744.5

Descriptive statistics

Standard deviation661649.42
Coefficient of variation (CV)18.207102
Kurtosis59.802533
Mean36340.182
Median Absolute Deviation (MAD)5278
Skewness-2.947823
Sum15953340
Variance4.3777996 × 1011
MonotonicityNot monotonic
2023-12-13T06:05:35.824036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
3.3%
1 6
 
1.3%
5 3
 
0.7%
26 3
 
0.7%
3 3
 
0.7%
2 3
 
0.7%
10 2
 
0.4%
393 2
 
0.4%
25 2
 
0.4%
28 2
 
0.4%
Other values (392) 398
88.6%
(Missing) 10
 
2.2%
ValueCountFrequency (%)
-7654069 1
0.2%
-4271660 1
0.2%
-3402940 1
0.2%
-3231527 1
0.2%
-2283532 1
0.2%
-1018559 1
0.2%
-968321 1
0.2%
-869695 1
0.2%
-818663 1
0.2%
-735882 1
0.2%
ValueCountFrequency (%)
5401868 1
0.2%
2908184 1
0.2%
2853410 1
0.2%
2684476 1
0.2%
2370585 1
0.2%
1837079 1
0.2%
1669735 1
0.2%
1412316 1
0.2%
1388903 1
0.2%
1254577 1
0.2%

Interactions

2023-12-13T06:05:31.894387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:29.818234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:30.402426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:30.953262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:31.442859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:32.017190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:29.925746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:30.510447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:31.037623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:31.530073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:32.101595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:30.045312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:30.621992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:31.152998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:31.613368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:32.186969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:30.160701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:30.726761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:31.254871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:31.701379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:32.289364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:30.286029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:30.839106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:31.338598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:05:31.793941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:05:35.927871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국가구분1구분22016(단위-천불)2017(단위-천불)2018(단위-천불)2019(단위-천불)2020(단위-천불)
국가1.0001.000NaN1.0001.0001.0001.0001.000
구분11.0001.0001.0000.6440.5500.7160.6980.634
구분2NaN1.0001.0000.1520.2030.1570.0000.159
2016(단위-천불)1.0000.6440.1521.0000.9640.9390.9730.967
2017(단위-천불)1.0000.5500.2030.9641.0000.9300.9390.924
2018(단위-천불)1.0000.7160.1570.9390.9301.0000.9480.939
2019(단위-천불)1.0000.6980.0000.9730.9390.9481.0000.983
2020(단위-천불)1.0000.6340.1590.9670.9240.9390.9831.000
2023-12-13T06:05:36.071397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분1구분2
구분11.0000.982
구분20.9821.000
2023-12-13T06:05:36.163016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2016(단위-천불)2017(단위-천불)2018(단위-천불)2019(단위-천불)2020(단위-천불)구분1구분2
2016(단위-천불)1.0000.9840.9740.9600.9480.3130.114
2017(단위-천불)0.9841.0000.9830.9690.9530.2810.155
2018(단위-천불)0.9740.9831.0000.9830.9660.3280.148
2019(단위-천불)0.9600.9690.9831.0000.9650.3550.000
2020(단위-천불)0.9480.9530.9660.9651.0000.3050.120
구분10.3130.2810.3280.3550.3051.0000.982
구분20.1140.1550.1480.0000.1200.9821.000

Missing values

2023-12-13T06:05:32.432879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:05:32.549956image/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.
2023-12-13T06:05:32.667166image/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

국가구분1구분22016(단위-천불)2017(단위-천불)2018(단위-천불)2019(단위-천불)2020(단위-천불)
0대세계수 출농산물55816047600561486675
1<NA>(B)축산물458341418461496
2<NA><NA>임산물425439524419393
3<NA>수 입농산물1766618593199031987620669
4<NA>(A)축산물58076603752277867627
5<NA><NA>임산물62007097787766435983
6<NA>무역수지(B-A)<NA>-23209-25466-28355-27277-26715
7칠레수 출농산물854910859123461475614998
8<NA>(B)축산물5444393828
9<NA><NA>임산물293389419531228
국가구분1구분22016(단위-천불)2017(단위-천불)2018(단위-천불)2019(단위-천불)2020(단위-천불)
439<NA>(A)축산물010684
440<NA><NA>임산물405575427579654
441<NA>무역수지(B-A)<NA>-14029-22169-18135-14821-59
442파나마수출농산물267433709160191066346
443<NA><NA>축산물1162<NA>
444<NA><NA>임산물1304894733764361
445<NA>수입농산물19802285190222002550
446<NA><NA>축산물0200<NA>
447<NA><NA>임산물210827872
448<NA>무역수지<NA>197819787989173944085

Duplicate rows

Most frequently occurring

국가구분1구분22016(단위-천불)2017(단위-천불)2018(단위-천불)2019(단위-천불)2020(단위-천불)# duplicates
0<NA>(B)축산물<NA><NA><NA><NA><NA>2