Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory673.8 KiB
Average record size in memory69.0 B

Variable types

DateTime1
Numeric5
Text1

Dataset

Description2023년 월별, 품목별(HS4단위), 국가별 수출입 실적입니다. 월별 수치에 대한 확정치는 다음연도에 확정되는 연도별 업데이트 자료로서 중량은 톤(ton), 금액은 미화로 천불(US1,000$)입니다.
Author관세청
URLhttps://www.data.go.kr/data/15116432/fileData.do

Alerts

수출중량 is highly overall correlated with 수출액High correlation
수입중량 is highly overall correlated with 수입액High correlation
수출액 is highly overall correlated with 수출중량High correlation
수입액 is highly overall correlated with 수입중량High correlation
수출중량 is highly skewed (γ1 = 38.28023826)Skewed
수입중량 is highly skewed (γ1 = 31.15611183)Skewed
수출액 is highly skewed (γ1 = 34.88091233)Skewed
수입액 is highly skewed (γ1 = 30.59711154)Skewed
수출중량 has 5627 (56.3%) zerosZeros
수입중량 has 6061 (60.6%) zerosZeros
수출액 has 4522 (45.2%) zerosZeros
수입액 has 4903 (49.0%) zerosZeros

Reproduction

Analysis started2024-04-06 08:31:34.303833
Analysis finished2024-04-06 08:31:42.748551
Duration8.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

월별
Date

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2023-01-01 00:00:00
Maximum2023-12-01 00:00:00
2024-04-06T17:31:42.941749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:43.122014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

세번4단위
Real number (ℝ)

Distinct355
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2035.602
Minimum101
Maximum3003
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:31:43.498888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile401
Q11506
median2204
Q32834
95-th percentile2934
Maximum3003
Range2902
Interquartile range (IQR)1328

Descriptive statistics

Standard deviation862.99805
Coefficient of variation (CV)0.42395225
Kurtosis-0.85205035
Mean2035.602
Median Absolute Deviation (MAD)643
Skewness-0.65958021
Sum20356020
Variance744765.64
MonotonicityNot monotonic
2024-04-06T17:31:43.939948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3002 174
 
1.7%
2710 139
 
1.4%
1902 133
 
1.3%
2202 120
 
1.2%
2103 111
 
1.1%
2106 107
 
1.1%
1905 106
 
1.1%
2101 105
 
1.1%
2008 103
 
1.0%
1901 96
 
1.0%
Other values (345) 8806
88.1%
ValueCountFrequency (%)
101 2
 
< 0.1%
102 2
 
< 0.1%
104 1
 
< 0.1%
105 5
 
0.1%
106 50
0.5%
201 10
 
0.1%
202 14
 
0.1%
203 18
 
0.2%
204 8
 
0.1%
206 28
0.3%
ValueCountFrequency (%)
3003 17
 
0.2%
3002 174
1.7%
3001 51
 
0.5%
2942 26
 
0.3%
2941 32
 
0.3%
2940 24
 
0.2%
2939 32
 
0.3%
2938 28
 
0.3%
2937 41
 
0.4%
2936 37
 
0.4%

국가
Text

Distinct195
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-06T17:31:44.783872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length3.442
Min length1

Characters and Unicode

Total characters34420
Distinct characters185
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)0.2%

Sample

1st row노르웨이
2nd row이탈리아
3rd row미얀마
4th row스웨덴
5th row독일
ValueCountFrequency (%)
미국 353
 
3.4%
중국 345
 
3.3%
태국 289
 
2.8%
일본 279
 
2.7%
독일 276
 
2.6%
베트남 270
 
2.6%
싱가포르 265
 
2.5%
홍콩 257
 
2.4%
대만 248
 
2.4%
말레이시아 246
 
2.3%
Other values (200) 7662
73.0%
2024-04-06T17:31:45.848667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 2297
 
6.7%
국 1413
 
4.1%
스 1336
 
3.9%
리 1236
 
3.6%
이 926
 
2.7%
르 888
 
2.6%
시 863
 
2.5%
트 675
 
2.0%
라 642
 
1.9%
미 636
 
1.8%
Other values (175) 23508
68.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 33927
98.6%
Space Separator 490
 
1.4%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 2297
 
6.8%
국 1413
 
4.2%
스 1336
 
3.9%
리 1236
 
3.6%
이 926
 
2.7%
르 888
 
2.6%
시 863
 
2.5%
트 675
 
2.0%
라 642
 
1.9%
미 636
 
1.9%
Other values (173) 23015
67.8%
Space Separator
ValueCountFrequency (%)
490
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 33927
98.6%
Common 493
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 2297
 
6.8%
국 1413
 
4.2%
스 1336
 
3.9%
리 1236
 
3.6%
이 926
 
2.7%
르 888
 
2.6%
시 863
 
2.5%
트 675
 
2.0%
라 642
 
1.9%
미 636
 
1.9%
Other values (173) 23015
67.8%
Common
ValueCountFrequency (%)
490
99.4%
- 3
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 33927
98.6%
ASCII 493
 
1.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 2297
 
6.8%
국 1413
 
4.2%
스 1336
 
3.9%
리 1236
 
3.6%
이 926
 
2.7%
르 888
 
2.6%
시 863
 
2.5%
트 675
 
2.0%
라 642
 
1.9%
미 636
 
1.9%
Other values (173) 23015
67.8%
ASCII
ValueCountFrequency (%)
490
99.4%
- 3
 
0.6%

수출중량
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct863
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1081.1664
Minimum0
Maximum1219133
Zeros5627
Zeros (%)56.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:31:46.149991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q316
95-th percentile538.15
Maximum1219133
Range1219133
Interquartile range (IQR)16

Descriptive statistics

Standard deviation20604.373
Coefficient of variation (CV)19.057541
Kurtosis1800.3232
Mean1081.1664
Median Absolute Deviation (MAD)0
Skewness38.280238
Sum10811664
Variance4.2454018 × 108
MonotonicityNot monotonic
2024-04-06T17:31:46.526153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5627
56.3%
1 560
 
5.6%
2 260
 
2.6%
3 156
 
1.6%
4 115
 
1.1%
20 109
 
1.1%
5 109
 
1.1%
6 91
 
0.9%
7 83
 
0.8%
9 81
 
0.8%
Other values (853) 2809
28.1%
ValueCountFrequency (%)
0 5627
56.3%
1 560
 
5.6%
2 260
 
2.6%
3 156
 
1.6%
4 115
 
1.1%
5 109
 
1.1%
6 91
 
0.9%
7 83
 
0.8%
8 79
 
0.8%
9 81
 
0.8%
ValueCountFrequency (%)
1219133 1
< 0.1%
886773 1
< 0.1%
637153 1
< 0.1%
559877 1
< 0.1%
524157 1
< 0.1%
452979 1
< 0.1%
431083 1
< 0.1%
248574 1
< 0.1%
235865 1
< 0.1%
231896 1
< 0.1%

수입중량
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1103
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3946.6962
Minimum0
Maximum3086314
Zeros6061
Zeros (%)60.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:31:46.822030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile1249.05
Maximum3086314
Range3086314
Interquartile range (IQR)20

Descriptive statistics

Standard deviation67273.252
Coefficient of variation (CV)17.04546
Kurtosis1171.8827
Mean3946.6962
Median Absolute Deviation (MAD)0
Skewness31.156112
Sum39466962
Variance4.5256905 × 109
MonotonicityNot monotonic
2024-04-06T17:31:47.161173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6061
60.6%
1 335
 
3.4%
2 164
 
1.6%
3 119
 
1.2%
4 102
 
1.0%
10 85
 
0.9%
5 79
 
0.8%
20 69
 
0.7%
6 61
 
0.6%
8 59
 
0.6%
Other values (1093) 2866
28.7%
ValueCountFrequency (%)
0 6061
60.6%
1 335
 
3.4%
2 164
 
1.6%
3 119
 
1.2%
4 102
 
1.0%
5 79
 
0.8%
6 61
 
0.6%
7 50
 
0.5%
8 59
 
0.6%
9 46
 
0.5%
ValueCountFrequency (%)
3086314 1
< 0.1%
3084192 1
< 0.1%
2241654 1
< 0.1%
1821505 1
< 0.1%
1645154 1
< 0.1%
1504649 1
< 0.1%
1447610 1
< 0.1%
1238702 1
< 0.1%
1070128 1
< 0.1%
941538 1
< 0.1%

수출액
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1252
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1210.1313
Minimum0
Maximum1158683
Zeros4522
Zeros (%)45.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:31:47.522017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q362
95-th percentile1365.7
Maximum1158683
Range1158683
Interquartile range (IQR)62

Descriptive statistics

Standard deviation19772.178
Coefficient of variation (CV)16.33887
Kurtosis1561.2652
Mean1210.1313
Median Absolute Deviation (MAD)2
Skewness34.880912
Sum12101313
Variance3.9093904 × 108
MonotonicityNot monotonic
2024-04-06T17:31:47.868030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4522
45.2%
1 441
 
4.4%
2 273
 
2.7%
3 184
 
1.8%
4 137
 
1.4%
5 108
 
1.1%
9 87
 
0.9%
8 84
 
0.8%
6 84
 
0.8%
7 82
 
0.8%
Other values (1242) 3998
40.0%
ValueCountFrequency (%)
0 4522
45.2%
1 441
 
4.4%
2 273
 
2.7%
3 184
 
1.8%
4 137
 
1.4%
5 108
 
1.1%
6 84
 
0.8%
7 82
 
0.8%
8 84
 
0.8%
9 87
 
0.9%
ValueCountFrequency (%)
1158683 1
< 0.1%
690521 1
< 0.1%
552970 1
< 0.1%
528571 1
< 0.1%
490521 1
< 0.1%
482867 1
< 0.1%
398953 1
< 0.1%
381420 1
< 0.1%
338690 1
< 0.1%
331491 1
< 0.1%

수입액
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1642
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2334.5901
Minimum0
Maximum1651734
Zeros4903
Zeros (%)49.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:31:48.144827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q3100
95-th percentile3181
Maximum1651734
Range1651734
Interquartile range (IQR)100

Descriptive statistics

Standard deviation32386.701
Coefficient of variation (CV)13.872543
Kurtosis1149.8596
Mean2334.5901
Median Absolute Deviation (MAD)1
Skewness30.597112
Sum23345901
Variance1.0488984 × 109
MonotonicityNot monotonic
2024-04-06T17:31:48.448362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4903
49.0%
1 350
 
3.5%
2 158
 
1.6%
3 120
 
1.2%
4 96
 
1.0%
6 81
 
0.8%
5 76
 
0.8%
7 70
 
0.7%
9 63
 
0.6%
11 53
 
0.5%
Other values (1632) 4030
40.3%
ValueCountFrequency (%)
0 4903
49.0%
1 350
 
3.5%
2 158
 
1.6%
3 120
 
1.2%
4 96
 
1.0%
5 76
 
0.8%
6 81
 
0.8%
7 70
 
0.7%
8 46
 
0.5%
9 63
 
0.6%
ValueCountFrequency (%)
1651734 1
< 0.1%
1119979 1
< 0.1%
999122 1
< 0.1%
974834 1
< 0.1%
962630 1
< 0.1%
754685 1
< 0.1%
648505 1
< 0.1%
608395 1
< 0.1%
574962 1
< 0.1%
558028 1
< 0.1%

Interactions

2024-04-06T17:31:41.178466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:35.987547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:37.152451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:38.375616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:40.116428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:41.413495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:36.197091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:37.345915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:38.577555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:40.295371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:41.618434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:36.449383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:37.551730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:38.807894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:40.539973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:41.828750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:36.656121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:37.816773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:39.033120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:40.755476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:42.086253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:36.921009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:38.089653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:39.268059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:31:40.936473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:31:48.647502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월별세번4단위수출중량수입중량수출액수입액
월별1.0000.0000.0260.0000.0290.025
세번4단위0.0001.0000.0410.0880.0600.081
수출중량0.0260.0411.0000.1170.9370.000
수입중량0.0000.0880.1171.0000.0760.951
수출액0.0290.0600.9370.0761.0000.135
수입액0.0250.0810.0000.9510.1351.000
2024-04-06T17:31:48.870503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세번4단위수출중량수입중량수출액수입액
세번4단위1.0000.226-0.0990.260-0.043
수출중량0.2261.000-0.0030.896-0.059
수입중량-0.099-0.0031.000-0.0380.897
수출액0.2600.896-0.0381.000-0.068
수입액-0.043-0.0590.897-0.0681.000

Missing values

2024-04-06T17:31:42.355429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:31:42.646139image/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

월별세번4단위국가수출중량수입중량수출액수입액
403152023-072005노르웨이70300
257882023-111507이탈리아0000
420832023-062008미얀마12467470
124982023-09802스웨덴0000
730982023-112826독일0006
317912023-091701카자흐스탄3030
930032023-082931홍콩101876
180232023-071003미국488840
904542023-062925호주10100
571562023-042501키리바티700100
월별세번4단위국가수출중량수입중량수출액수입액
778242023-012844스위스00560
238262023-121213미국0110029
641942023-052709호주02341790163916
443152023-032101홍콩6714368776
736342023-092827파키스탄30250
181942023-121005러시아 연방017000620
13412023-02206핀란드023035
124462023-06802멕시코0000
504082023-022204브라질0000
851792023-032915네덜란드069301743