Overview

Dataset statistics

Number of variables6
Number of observations884
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.9 KiB
Average record size in memory53.1 B

Variable types

Numeric5
Text1

Dataset

Description텅스텐, 몰리브덴, 망간, 주석, 황철석, 니켈, 코발트, 크롬, 티타늄, 지르코늄, 알루미늄, 백금, 탄탈륨, 바나듐, 니오븀, 인상흑연, 토상흑연, 납석, 장석, 고령토류, 석면, 석회석류, 규석, 규사, 규조토, 형석, 인광석, 규회석, 운모, 홍주석, 남정석, 중정석, 마그네사이트, 석고, 불석, 수정, 명반석, 붕소, 금강석, 무연탄, 유연탄, 동, 연, 아연, 철, 금, 은, 하석, 주사, 비소, 활석, 황 등 주요 광종별 2005~2021년 기간 중 국내 수출입 현황자료 제공
URLhttps://www.data.go.kr/data/3075984/fileData.do

Alerts

수출중량(톤) is highly overall correlated with 수출금액(천불) and 1 other fieldsHigh correlation
수출금액(천불) is highly overall correlated with 수출중량(톤) and 1 other fieldsHigh correlation
수입중량(톤) is highly overall correlated with 수출중량(톤) and 1 other fieldsHigh correlation
수입금액(천불) is highly overall correlated with 수출금액(천불) and 1 other fieldsHigh correlation
수출금액(천불) is highly skewed (γ1 = 20.15586219)Skewed
수출중량(톤) has 215 (24.3%) zerosZeros
수출금액(천불) has 190 (21.5%) zerosZeros
수입중량(톤) has 126 (14.3%) zerosZeros
수입금액(천불) has 77 (8.7%) zerosZeros

Reproduction

Analysis started2023-12-12 08:04:44.734680
Analysis finished2023-12-12 08:04:49.334608
Duration4.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기간
Real number (ℝ)

Distinct17
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013
Minimum2005
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-12-12T17:04:49.435325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2005
Q12009
median2013
Q32017
95-th percentile2021
Maximum2021
Range16
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.9017528
Coefficient of variation (CV)0.0024350486
Kurtosis-1.2083776
Mean2013
Median Absolute Deviation (MAD)4
Skewness0
Sum1779492
Variance24.02718
MonotonicityNot monotonic
2023-12-12T17:04:49.615849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2005 52
 
5.9%
2006 52
 
5.9%
2021 52
 
5.9%
2020 52
 
5.9%
2019 52
 
5.9%
2018 52
 
5.9%
2017 52
 
5.9%
2016 52
 
5.9%
2015 52
 
5.9%
2014 52
 
5.9%
Other values (7) 364
41.2%
ValueCountFrequency (%)
2005 52
5.9%
2006 52
5.9%
2007 52
5.9%
2008 52
5.9%
2009 52
5.9%
2010 52
5.9%
2011 52
5.9%
2012 52
5.9%
2013 52
5.9%
2014 52
5.9%
ValueCountFrequency (%)
2021 52
5.9%
2020 52
5.9%
2019 52
5.9%
2018 52
5.9%
2017 52
5.9%
2016 52
5.9%
2015 52
5.9%
2014 52
5.9%
2013 52
5.9%
2012 52
5.9%

품목명
Text

Distinct52
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
2023-12-12T17:04:49.930254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.9807692
Min length1

Characters and Unicode

Total characters2635
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row텅스텐광
2nd row텅스텐광
3rd row텅스텐광
4th row텅스텐광
5th row텅스텐광
ValueCountFrequency (%)
텅스텐광 17
 
1.9%
몰리브덴광 17
 
1.9%
금강석 17
 
1.9%
운모 17
 
1.9%
홍주석 17
 
1.9%
남정석 17
 
1.9%
중정석 17
 
1.9%
마그네사이트 17
 
1.9%
석고 17
 
1.9%
불석 17
 
1.9%
Other values (42) 714
80.8%
2023-12-12T17:04:50.490449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
광 391
 
14.8%
석 340
 
12.9%
연 102
 
3.9%
규 68
 
2.6%
금 51
 
1.9%
늄 51
 
1.9%
탄 51
 
1.9%
토 51
 
1.9%
사 51
 
1.9%
주 51
 
1.9%
Other values (70) 1428
54.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2635
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 391
 
14.8%
석 340
 
12.9%
연 102
 
3.9%
규 68
 
2.6%
금 51
 
1.9%
늄 51
 
1.9%
탄 51
 
1.9%
토 51
 
1.9%
사 51
 
1.9%
주 51
 
1.9%
Other values (70) 1428
54.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2635
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 391
 
14.8%
석 340
 
12.9%
연 102
 
3.9%
규 68
 
2.6%
금 51
 
1.9%
늄 51
 
1.9%
탄 51
 
1.9%
토 51
 
1.9%
사 51
 
1.9%
주 51
 
1.9%
Other values (70) 1428
54.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2635
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
광 391
 
14.8%
석 340
 
12.9%
연 102
 
3.9%
규 68
 
2.6%
금 51
 
1.9%
늄 51
 
1.9%
탄 51
 
1.9%
토 51
 
1.9%
사 51
 
1.9%
주 51
 
1.9%
Other values (70) 1428
54.2%

수출중량(톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct546
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37982.385
Minimum0
Maximum1371973
Zeros215
Zeros (%)24.3%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-12-12T17:04:50.674394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0475
median206
Q37655.75
95-th percentile184261.85
Maximum1371973
Range1371973
Interquartile range (IQR)7655.7025

Descriptive statistics

Standard deviation142217.2
Coefficient of variation (CV)3.7442935
Kurtosis47.420794
Mean37982.385
Median Absolute Deviation (MAD)206
Skewness6.3809409
Sum33576429
Variance2.0225732 × 1010
MonotonicityNot monotonic
2023-12-12T17:04:51.156086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 215
 
24.3%
1.0 17
 
1.9%
20.0 6
 
0.7%
43.0 5
 
0.6%
1000.0 5
 
0.6%
22.0 5
 
0.6%
2.0 5
 
0.6%
1500.0 5
 
0.6%
63.0 5
 
0.6%
39.0 4
 
0.5%
Other values (536) 612
69.2%
ValueCountFrequency (%)
0.0 215
24.3%
0.008 1
 
0.1%
0.018 1
 
0.1%
0.03 1
 
0.1%
0.033 1
 
0.1%
0.037 1
 
0.1%
0.046 1
 
0.1%
0.048 1
 
0.1%
0.05 2
 
0.2%
0.053 1
 
0.1%
ValueCountFrequency (%)
1371973.0 1
0.1%
1339932.0 1
0.1%
1318405.0 1
0.1%
1313668.0 1
0.1%
1254500.0 1
0.1%
1205526.0 1
0.1%
978819.0 1
0.1%
852193.0 1
0.1%
763011.0 1
0.1%
728883.0 1
0.1%

수출금액(천불)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct544
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7071.5588
Minimum0
Maximum1185720
Zeros190
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-12-12T17:04:51.315078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median201.5
Q33104.5
95-th percentile20695.05
Maximum1185720
Range1185720
Interquartile range (IQR)3099.5

Descriptive statistics

Standard deviation46087.065
Coefficient of variation (CV)6.5172427
Kurtosis492.15718
Mean7071.5588
Median Absolute Deviation (MAD)201.5
Skewness20.155862
Sum6251258
Variance2.1240176 × 109
MonotonicityNot monotonic
2023-12-12T17:04:51.471938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 190
 
21.5%
1 17
 
1.9%
5 6
 
0.7%
9 6
 
0.7%
8 5
 
0.6%
100 5
 
0.6%
2 5
 
0.6%
4 5
 
0.6%
7 4
 
0.5%
13 4
 
0.5%
Other values (534) 637
72.1%
ValueCountFrequency (%)
0 190
21.5%
1 17
 
1.9%
2 5
 
0.6%
3 3
 
0.3%
4 5
 
0.6%
5 6
 
0.7%
6 1
 
0.1%
7 4
 
0.5%
8 5
 
0.6%
9 6
 
0.7%
ValueCountFrequency (%)
1185720 1
0.1%
388022 1
0.1%
260669 1
0.1%
195597 1
0.1%
186997 1
0.1%
138661 1
0.1%
137728 1
0.1%
125225 1
0.1%
118990 1
0.1%
118578 1
0.1%

수입중량(톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct723
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2468251.9
Minimum0
Maximum1.32667 × 108
Zeros126
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-12-12T17:04:51.624264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1121.5
median19125
Q3230325
95-th percentile2152946.3
Maximum1.32667 × 108
Range1.32667 × 108
Interquartile range (IQR)230203.5

Descriptive statistics

Standard deviation15108237
Coefficient of variation (CV)6.1210272
Kurtosis52.434811
Mean2468251.9
Median Absolute Deviation (MAD)19125
Skewness7.2721031
Sum2.1819347 × 109
Variance2.2825883 × 1014
MonotonicityNot monotonic
2023-12-12T17:04:51.795631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 126
 
14.3%
1.0 12
 
1.4%
3.0 6
 
0.7%
2.0 4
 
0.5%
0.002 3
 
0.3%
4.0 3
 
0.3%
7.0 3
 
0.3%
5.0 3
 
0.3%
76.0 3
 
0.3%
368.0 2
 
0.2%
Other values (713) 719
81.3%
ValueCountFrequency (%)
0.0 126
14.3%
0.001 1
 
0.1%
0.002 3
 
0.3%
0.019 1
 
0.1%
0.242 1
 
0.1%
0.269 1
 
0.1%
0.305 1
 
0.1%
0.322 1
 
0.1%
0.33 1
 
0.1%
0.381 1
 
0.1%
ValueCountFrequency (%)
132667000.0 1
0.1%
131520000.0 1
0.1%
131464000.0 1
0.1%
119322000.0 1
0.1%
118468000.0 1
0.1%
117873000.0 1
0.1%
117106000.0 1
0.1%
116222000.0 1
0.1%
116118000.0 1
0.1%
115373000.0 1
0.1%

수입금액(천불)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct742
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499536.75
Minimum0
Maximum16052361
Zeros77
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-12-12T17:04:51.942369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1273
median10882
Q362753.75
95-th percentile3294481.1
Maximum16052361
Range16052361
Interquartile range (IQR)62480.75

Descriptive statistics

Standard deviation1852646.3
Coefficient of variation (CV)3.7087288
Kurtosis30.442097
Mean499536.75
Median Absolute Deviation (MAD)10882
Skewness5.2806958
Sum4.4159048 × 108
Variance3.4322983 × 1012
MonotonicityNot monotonic
2023-12-12T17:04:52.119528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 77
 
8.7%
1 12
 
1.4%
2 10
 
1.1%
3 6
 
0.7%
7 3
 
0.3%
21 3
 
0.3%
57 3
 
0.3%
12 3
 
0.3%
10 3
 
0.3%
171 2
 
0.2%
Other values (732) 762
86.2%
ValueCountFrequency (%)
0 77
8.7%
1 12
 
1.4%
2 10
 
1.1%
3 6
 
0.7%
5 2
 
0.2%
6 2
 
0.2%
7 3
 
0.3%
8 1
 
0.1%
9 2
 
0.2%
10 3
 
0.3%
ValueCountFrequency (%)
16052361 1
0.1%
14668105 1
0.1%
14221222 1
0.1%
13485475 1
0.1%
13480829 1
0.1%
13062449 1
0.1%
12074936 1
0.1%
11623986 1
0.1%
11425189 1
0.1%
11380768 1
0.1%

Interactions

2023-12-12T17:04:48.187131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:45.118528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:45.968170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:46.628717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:47.369235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:48.353745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:45.324910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:46.114801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:46.776615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:47.618971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:48.528332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:45.500569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:46.266386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:46.914111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:47.778454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:48.697466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:45.671776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:46.405937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:47.038470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:47.922294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:48.862581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:45.816368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:46.518796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:47.176897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:48.057272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:04:52.248430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기간품목명수출중량(톤)수출금액(천불)수입중량(톤)수입금액(천불)
기간1.0000.0000.2310.0000.0420.000
품목명0.0001.0000.6840.3600.7030.795
수출중량(톤)0.2310.6841.0000.6530.0000.000
수출금액(천불)0.0000.3600.6531.0000.0000.534
수입중량(톤)0.0420.7030.0000.0001.0000.800
수입금액(천불)0.0000.7950.0000.5340.8001.000
2023-12-12T17:04:52.364037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기간수출중량(톤)수출금액(천불)수입중량(톤)수입금액(천불)
기간1.0000.0470.0860.0260.070
수출중량(톤)0.0471.0000.9000.5690.476
수출금액(천불)0.0860.9001.0000.4830.510
수입중량(톤)0.0260.5690.4831.0000.871
수입금액(천불)0.0700.4760.5100.8711.000

Missing values

2023-12-12T17:04:49.088569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:04:49.270065image/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

기간품목명수출중량(톤)수출금액(천불)수입중량(톤)수입금액(천불)
02005텅스텐광279.01170.02
12006텅스텐광232.039341.0346
22007텅스텐광3.010.031
32008텅스텐광22.05937.030
42009텅스텐광21.014406.063
52010텅스텐광513.03330.02
62011텅스텐광24.06918.057
72012텅스텐광864.0197376.01337
82013텅스텐광588.058112.0264
92014텅스텐광255.02540160.02363
기간품목명수출중량(톤)수출금액(천불)수입중량(톤)수입금액(천불)
8742012황728883.012522536840.08556
8752013황763011.07768829940.03670
8762014황852193.010649838990.05272
8772015황978819.011838024042.03700
8782016황1339932.07012123784.02123
8792017황1205526.08752618846.01674
8802018황1254500.013772828574.02821
8812019황1318405.09410323205.02515
8822020황1313668.04103436590.01756
8832021황1371973.018699791425.08479