Overview

Dataset statistics

Number of variables7
Number of observations1076
Missing cells866
Missing cells (%)11.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory63.2 KiB
Average record size in memory60.1 B

Variable types

Categorical2
Text2
Numeric3

Dataset

Description국가별, 광종별 수출중량, 수출금액, 수입중량, 수입금액 등 수출입현황 자료 제공 또는 광물별 주요국가 교역통계 자료 제공
URLhttps://www.data.go.kr/data/3070183/fileData.do

Alerts

수출중량(톤) is highly overall correlated with 수출금액(천불)High correlation
수출금액(천불) is highly overall correlated with 수출중량(톤)High correlation
수출중량(톤) has 218 (20.3%) missing valuesMissing
수출금액(천불) has 218 (20.3%) missing valuesMissing
수입중량(톤) has 216 (20.1%) missing valuesMissing
수입금액(천불) has 214 (19.9%) missing valuesMissing
수출금액(천불) is highly skewed (γ1 = 25.74955137)Skewed
수출중량(톤) has 659 (61.2%) zerosZeros
수출금액(천불) has 632 (58.7%) zerosZeros
수입중량(톤) has 383 (35.6%) zerosZeros

Reproduction

Analysis started2023-12-12 03:37:21.918287
Analysis finished2023-12-12 03:37:23.627387
Duration1.71 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기간
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
2020
538 
2021
538 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 538
50.0%
2021 538
50.0%

Length

2023-12-12T12:37:23.688641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:37:23.775568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 538
50.0%
2021 538
50.0%

품목명
Categorical

Distinct27
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
석회석류
92 
마그네사이트
76 
동
72 
아연
 
66
규석
 
62
Other values (22)
708 

Length

Max length6
Median length4
Mean length2.7769517
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
석회석류 92
 
8.6%
마그네사이트 76
 
7.1%
동 72
 
6.7%
아연 66
 
6.1%
규석 62
 
5.8%
토상흑연 60
 
5.6%
연 54
 
5.0%
리튬 54
 
5.0%
토탄 52
 
4.8%
철 48
 
4.5%
Other values (17) 440
40.9%

Length

2023-12-12T12:37:23.889019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
석회석류 92
 
8.6%
마그네사이트 76
 
7.1%
동 72
 
6.7%
아연 66
 
6.1%
규석 62
 
5.8%
토상흑연 60
 
5.6%
연 54
 
5.0%
리튬 54
 
5.0%
토탄 52
 
4.8%
철 48
 
4.5%
Other values (17) 440
40.9%

국가명
Text

Distinct134
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
2023-12-12T12:37:24.115884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length3.4135688
Min length1

Characters and Unicode

Total characters3673
Distinct characters137
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row나미비아
2nd row남아프리카공화국
3rd row네덜란드
4th row러시아연방
5th row말레이시아
ValueCountFrequency (%)
중국 42
 
3.4%
미국 38
 
3.0%
네덜란드 36
 
2.9%
일본 36
 
2.9%
국 34
 
2.7%
인도 32
 
2.6%
호주 30
 
2.4%
베트남 28
 
2.2%
인도네시아 24
 
1.9%
캐나다 24
 
1.9%
Other values (151) 925
74.1%
2023-12-12T12:37:24.453149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 234
 
6.4%
국 178
 
4.8%
173
 
4.7%
스 144
 
3.9%
리 128
 
3.5%
시 90
 
2.5%
이 80
 
2.2%
인 76
 
2.1%
일 72
 
2.0%
네 70
 
1.9%
Other values (127) 2428
66.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3446
93.8%
Space Separator 173
 
4.7%
Uppercase Letter 54
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 234
 
6.8%
국 178
 
5.2%
스 144
 
4.2%
리 128
 
3.7%
시 90
 
2.6%
이 80
 
2.3%
인 76
 
2.2%
일 72
 
2.1%
네 70
 
2.0%
도 70
 
2.0%
Other values (123) 2304
66.9%
Uppercase Letter
ValueCountFrequency (%)
U 18
33.3%
A 18
33.3%
E 18
33.3%
Space Separator
ValueCountFrequency (%)
173
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3446
93.8%
Common 173
 
4.7%
Latin 54
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 234
 
6.8%
국 178
 
5.2%
스 144
 
4.2%
리 128
 
3.7%
시 90
 
2.6%
이 80
 
2.3%
인 76
 
2.2%
일 72
 
2.1%
네 70
 
2.0%
도 70
 
2.0%
Other values (123) 2304
66.9%
Latin
ValueCountFrequency (%)
U 18
33.3%
A 18
33.3%
E 18
33.3%
Common
ValueCountFrequency (%)
173
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3446
93.8%
ASCII 227
 
6.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 234
 
6.8%
국 178
 
5.2%
스 144
 
4.2%
리 128
 
3.7%
시 90
 
2.6%
이 80
 
2.3%
인 76
 
2.2%
일 72
 
2.1%
네 70
 
2.0%
도 70
 
2.0%
Other values (123) 2304
66.9%
ASCII
ValueCountFrequency (%)
173
76.2%
U 18
 
7.9%
A 18
 
7.9%
E 18
 
7.9%

수출중량(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct131
Distinct (%)15.3%
Missing218
Missing (%)20.3%
Infinite0
Infinite (%)0.0%
Mean3230.3438
Minimum0
Maximum553578
Zeros659
Zeros (%)61.2%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2023-12-12T12:37:24.571297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile477.2
Maximum553578
Range553578
Interquartile range (IQR)0

Descriptive statistics

Standard deviation32173.823
Coefficient of variation (CV)9.9598758
Kurtosis150.04369
Mean3230.3438
Median Absolute Deviation (MAD)0
Skewness11.662321
Sum2771635
Variance1.0351549 × 109
MonotonicityNot monotonic
2023-12-12T12:37:24.712314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 659
61.2%
1 21
 
2.0%
2 11
 
1.0%
4 6
 
0.6%
20 5
 
0.5%
7 4
 
0.4%
18 4
 
0.4%
10 3
 
0.3%
44 3
 
0.3%
12 3
 
0.3%
Other values (121) 139
 
12.9%
(Missing) 218
 
20.3%
ValueCountFrequency (%)
0 659
61.2%
1 21
 
2.0%
2 11
 
1.0%
3 1
 
0.1%
4 6
 
0.6%
5 2
 
0.2%
6 2
 
0.2%
7 4
 
0.4%
10 3
 
0.3%
11 2
 
0.2%
ValueCountFrequency (%)
553578 1
0.1%
317548 1
0.1%
302511 1
0.1%
302062 1
0.1%
286923 1
0.1%
277958 1
0.1%
274945 1
0.1%
261521 1
0.1%
22773 1
0.1%
20192 1
0.1%

수출금액(천불)
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct134
Distinct (%)15.6%
Missing218
Missing (%)20.3%
Infinite0
Infinite (%)0.0%
Mean2269.1608
Minimum0
Maximum1172999
Zeros632
Zeros (%)58.7%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2023-12-12T12:37:24.836815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1065.2
Maximum1172999
Range1172999
Interquartile range (IQR)1

Descriptive statistics

Standard deviation42264.832
Coefficient of variation (CV)18.625754
Kurtosis696.89969
Mean2269.1608
Median Absolute Deviation (MAD)0
Skewness25.749551
Sum1946940
Variance1.786316 × 109
MonotonicityNot monotonic
2023-12-12T12:37:24.966523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 632
58.7%
1 17
 
1.6%
3 12
 
1.1%
5 10
 
0.9%
2 9
 
0.8%
4 8
 
0.7%
17 6
 
0.6%
9 5
 
0.5%
11 4
 
0.4%
14 4
 
0.4%
Other values (124) 151
 
14.0%
(Missing) 218
 
20.3%
ValueCountFrequency (%)
0 632
58.7%
1 17
 
1.6%
2 9
 
0.8%
3 12
 
1.1%
4 8
 
0.7%
5 10
 
0.9%
6 2
 
0.2%
7 2
 
0.2%
8 3
 
0.3%
9 5
 
0.5%
ValueCountFrequency (%)
1172999 1
0.1%
387621 1
0.1%
65850 1
0.1%
36444 1
0.1%
29146 1
0.1%
23438 1
0.1%
18488 1
0.1%
17432 1
0.1%
14716 1
0.1%
12571 1
0.1%

수입중량(톤)
Real number (ℝ)

MISSING  ZEROS 

Distinct391
Distinct (%)45.5%
Missing216
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean482839.13
Minimum0
Maximum57696256
Zeros383
Zeros (%)35.6%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2023-12-12T12:37:25.100941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13
Q33122.25
95-th percentile473756.75
Maximum57696256
Range57696256
Interquartile range (IQR)3122.25

Descriptive statistics

Standard deviation3952062.8
Coefficient of variation (CV)8.1850509
Kurtosis151.3882
Mean482839.13
Median Absolute Deviation (MAD)13
Skewness11.825005
Sum4.1524165 × 108
Variance1.5618801 × 1013
MonotonicityNot monotonic
2023-12-12T12:37:25.252862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 383
35.6%
1 15
 
1.4%
40 7
 
0.7%
2 7
 
0.7%
24 6
 
0.6%
3 6
 
0.6%
20 5
 
0.5%
25 5
 
0.5%
9 4
 
0.4%
80 4
 
0.4%
Other values (381) 418
38.8%
(Missing) 216
20.1%
ValueCountFrequency (%)
0 383
35.6%
1 15
 
1.4%
2 7
 
0.7%
3 6
 
0.6%
4 1
 
0.1%
6 3
 
0.3%
7 2
 
0.2%
8 2
 
0.2%
9 4
 
0.4%
10 3
 
0.3%
ValueCountFrequency (%)
57696256 1
0.1%
55285371 1
0.1%
53243101 1
0.1%
44741608 1
0.1%
23284856 1
0.1%
23101810 1
0.1%
19336265 1
0.1%
18919109 1
0.1%
11836195 1
0.1%
9991706 1
0.1%

수입금액(천불)
Text

MISSING 

Distinct404
Distinct (%)46.9%
Missing214
Missing (%)19.9%
Memory size8.5 KiB
2023-12-12T12:37:25.557226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length2.5348028
Min length1

Characters and Unicode

Total characters2185
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique358 ?
Unique (%)41.5%

Sample

1st row0
2nd row2022
3rd row2427
4th row31132
5th row68619
ValueCountFrequency (%)
0 326
37.9%
1 30
 
3.5%
2 12
 
1.4%
6 9
 
1.0%
3 8
 
0.9%
9 6
 
0.7%
12 6
 
0.7%
5 6
 
0.7%
4 6
 
0.7%
8 5
 
0.6%
Other values (393) 446
51.9%
2023-12-12T12:37:26.049311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 450
20.6%
1 316
14.5%
2 233
10.7%
3 205
9.4%
4 188
8.6%
5 177
 
8.1%
6 161
 
7.4%
7 156
 
7.1%
8 152
 
7.0%
9 145
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2183
99.9%
Space Separator 2
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 450
20.6%
1 316
14.5%
2 233
10.7%
3 205
9.4%
4 188
8.6%
5 177
 
8.1%
6 161
 
7.4%
7 156
 
7.1%
8 152
 
7.0%
9 145
 
6.6%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2185
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 450
20.6%
1 316
14.5%
2 233
10.7%
3 205
9.4%
4 188
8.6%
5 177
 
8.1%
6 161
 
7.4%
7 156
 
7.1%
8 152
 
7.0%
9 145
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2185
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 450
20.6%
1 316
14.5%
2 233
10.7%
3 205
9.4%
4 188
8.6%
5 177
 
8.1%
6 161
 
7.4%
7 156
 
7.1%
8 152
 
7.0%
9 145
 
6.6%

Interactions

2023-12-12T12:37:22.934292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:37:22.299718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:37:22.596090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:37:23.052100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:37:22.409370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:37:22.690089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:37:23.169938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:37:22.500036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:37:22.805238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:37:26.225132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기간품목명수출중량(톤)수출금액(천불)수입중량(톤)
기간1.0000.0000.0230.0000.026
품목명0.0001.0000.0000.0000.216
수출중량(톤)0.0230.0001.0000.7290.000
수출금액(천불)0.0000.0000.7291.0000.000
수입중량(톤)0.0260.2160.0000.0001.000
2023-12-12T12:37:26.759514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기간품목명
기간1.0000.000
품목명0.0001.000
2023-12-12T12:37:26.885837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수출중량(톤)수출금액(천불)수입중량(톤)기간품목명
수출중량(톤)1.0000.920-0.1040.0150.000
수출금액(천불)0.9201.000-0.1530.0000.000
수입중량(톤)-0.104-0.1531.0000.0280.091
기간0.0150.0000.0281.0000.000
품목명0.0000.0000.0910.0001.000

Missing values

2023-12-12T12:37:23.306589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:37:23.434283image/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.
2023-12-12T12:37:23.552767image/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

기간품목명국가명수출중량(톤)수출금액(천불)수입중량(톤)수입금액(천불)
02020동나미비아0000
12020동남아프리카공화국0050562022
22020동네덜란드<NA><NA><NA><NA>
32020동러시아연방<NA><NA><NA><NA>
42020동말레이시아0025852427
52020동멕시코002052631132
62020동몬테네그로<NA><NA><NA><NA>
72020동미국005224068619
82020동베트남00841631
92020동브라질001455828899
기간품목명국가명수출중량(톤)수출금액(천불)수입중량(톤)수입금액(천불)
10662021토탄에스토니아0080951889
10672021토탄우크라이나00255
10682021토탄이탈리아002516
10692021토탄인도19200
10702021토탄일본207300
10712021토탄중국0010021
10722021토탄캐나다0033641421
10732021토탄폴란드00747253
10742021토탄핀란드0000
10752021토탄호주00265002279