Overview

Dataset statistics

Number of variables13
Number of observations3557
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory378.8 KiB
Average record size in memory109.0 B

Variable types

Categorical5
Text3
Numeric5

Dataset

Description수산물 수출입정보는 국내에 수출 및 수입되는 수산물에 대한 월별 통계정보로 관세청에서 보유한 데이터를 집계하여 제공하는 데이터로 국가별 수출입 현황를 제공하는 목록입니다.
Author해양수산부
URLhttps://www.data.go.kr/data/15102781/fileData.do

Alerts

기준년월 has constant value ""Constant
데이터기준일자 has constant value ""Constant
수출입구분코드 is highly overall correlated with 수출입구분명High correlation
수출입구분명 is highly overall correlated with 수출입구분코드High correlation
당월수출입중량(킬로그램) is highly overall correlated with 당월수출입미화금액(달러) and 2 other fieldsHigh correlation
당월수출입미화금액(달러) is highly overall correlated with 당월수출입중량(킬로그램) and 2 other fieldsHigh correlation
당해누계수출입중량(킬로그램) is highly overall correlated with 당월수출입중량(킬로그램) and 2 other fieldsHigh correlation
당해누계수출입미화금액(달러) is highly overall correlated with 당월수출입중량(킬로그램) and 2 other fieldsHigh correlation
당월수출입중량(킬로그램) is highly skewed (γ1 = 52.90266328)Skewed
당해누계수출입중량(킬로그램) is highly skewed (γ1 = 53.7440442)Skewed
당월수출입중량(킬로그램) has 1014 (28.5%) zerosZeros
당월수출입미화금액(달러) has 980 (27.6%) zerosZeros
당해누계수출입중량(킬로그램) has 60 (1.7%) zerosZeros

Reproduction

Analysis started2024-04-21 02:08:49.282090
Analysis finished2024-04-21 02:08:55.013316
Duration5.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.9 KiB
2024-02
3557 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-02
2nd row2024-02
3rd row2024-02
4th row2024-02
5th row2024-02

Common Values

ValueCountFrequency (%)
2024-02 3557
100.0%

Length

2024-04-21T11:08:55.084435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:08:55.192058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-02 3557
100.0%

국가코드
Text

Distinct139
Distinct (%)3.9%
Missing1
Missing (%)< 0.1%
Memory size27.9 KiB
2024-04-21T11:08:55.454234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters7112
Distinct characters26
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

Unique22 ?
Unique (%)0.6%

Sample

1st rowAE
2nd rowAE
3rd rowAE
4th rowAE
5th rowAE
ValueCountFrequency (%)
us 323
 
9.1%
cn 317
 
8.9%
jp 273
 
7.7%
vn 213
 
6.0%
th 175
 
4.9%
ca 153
 
4.3%
sg 122
 
3.4%
id 122
 
3.4%
au 115
 
3.2%
ph 114
 
3.2%
Other values (129) 1629
45.8%
2024-04-21T11:08:55.869743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 788
 
11.1%
U 605
 
8.5%
S 561
 
7.9%
C 548
 
7.7%
P 488
 
6.9%
H 453
 
6.4%
A 425
 
6.0%
T 384
 
5.4%
G 312
 
4.4%
M 298
 
4.2%
Other values (16) 2250
31.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7112
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 788
 
11.1%
U 605
 
8.5%
S 561
 
7.9%
C 548
 
7.7%
P 488
 
6.9%
H 453
 
6.4%
A 425
 
6.0%
T 384
 
5.4%
G 312
 
4.4%
M 298
 
4.2%
Other values (16) 2250
31.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 7112
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 788
 
11.1%
U 605
 
8.5%
S 561
 
7.9%
C 548
 
7.7%
P 488
 
6.9%
H 453
 
6.4%
A 425
 
6.0%
T 384
 
5.4%
G 312
 
4.4%
M 298
 
4.2%
Other values (16) 2250
31.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 788
 
11.1%
U 605
 
8.5%
S 561
 
7.9%
C 548
 
7.7%
P 488
 
6.9%
H 453
 
6.4%
A 425
 
6.0%
T 384
 
5.4%
G 312
 
4.4%
M 298
 
4.2%
Other values (16) 2250
31.6%

HSK품목코드
Real number (ℝ)

Distinct476
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0483497 × 109
Minimum1.06199 × 108
Maximum9.606291 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2024-04-21T11:08:56.028757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.06199 × 108
5-th percentile3.0224 × 108
Q13.04871 × 108
median3.0799411 × 108
Q31.6042041 × 109
95-th percentile2.501009 × 109
Maximum9.606291 × 109
Range9.500092 × 109
Interquartile range (IQR)1.2993331 × 109

Descriptive statistics

Standard deviation1.1667954 × 109
Coefficient of variation (CV)1.1129829
Kurtosis22.981149
Mean1.0483497 × 109
Median Absolute Deviation (MAD)6804110
Skewness3.8793835
Sum3.72898 × 1012
Variance1.3614114 × 1018
MonotonicityNot monotonic
2024-04-21T11:08:56.208759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2008995010 91
 
2.6%
2501009010 67
 
1.9%
2501009090 61
 
1.7%
1212212010 54
 
1.5%
303899099 50
 
1.4%
1604204090 42
 
1.2%
1212211010 41
 
1.2%
2008995090 35
 
1.0%
1604141019 34
 
1.0%
1604209000 34
 
1.0%
Other values (466) 3048
85.7%
ValueCountFrequency (%)
106199000 31
0.9%
106201000 4
 
0.1%
106202000 3
 
0.1%
106203000 5
 
0.1%
106209000 14
0.4%
106903010 2
 
0.1%
106903090 2
 
0.1%
301111000 4
 
0.1%
301119000 20
0.6%
301190000 12
 
0.3%
ValueCountFrequency (%)
9606291000 14
 
0.4%
9601901090 10
 
0.3%
7116102000 14
 
0.4%
7116101000 7
 
0.2%
7101220000 7
 
0.2%
7101210000 2
 
0.1%
2501009090 61
1.7%
2501009020 20
 
0.6%
2501009010 67
1.9%
2501001020 31
0.9%

수출입구분코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.9 KiB
E
1964 
I
1593 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
E 1964
55.2%
I 1593
44.8%

Length

2024-04-21T11:08:56.330711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:08:56.423671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e 1964
55.2%
i 1593
44.8%

수출입구분명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.9 KiB
수출
1964 
수입
1593 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
수출 1964
55.2%
수입 1593
44.8%

Length

2024-04-21T11:08:56.532366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:08:56.619762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수출 1964
55.2%
수입 1593
44.8%

국가명
Text

Distinct140
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size27.9 KiB
2024-04-21T11:08:56.862478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length2.9904414
Min length1

Characters and Unicode

Total characters10637
Distinct characters153
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

Unique23 ?
Unique (%)0.6%

Sample

1st row아랍에미리트연합
2nd row아랍에미리트연합
3rd row아랍에미리트연합
4th row아랍에미리트연합
5th row아랍에미리트연합
ValueCountFrequency (%)
미국 323
 
9.1%
중국 317
 
8.9%
일본 273
 
7.7%
베트남 213
 
6.0%
태국 175
 
4.9%
카나다 153
 
4.3%
싱가포르 122
 
3.4%
인도네시아 122
 
3.4%
호주 115
 
3.2%
필리핀 114
 
3.2%
Other values (130) 1630
45.8%
2024-04-21T11:08:57.498660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
국 878
 
8.3%
아 668
 
6.3%
미 417
 
3.9%
리 347
 
3.3%
일 320
 
3.0%
중 317
 
3.0%
트 312
 
2.9%
시 299
 
2.8%
나 284
 
2.7%
본 273
 
2.6%
Other values (143) 6522
61.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10637
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 878
 
8.3%
아 668
 
6.3%
미 417
 
3.9%
리 347
 
3.3%
일 320
 
3.0%
중 317
 
3.0%
트 312
 
2.9%
시 299
 
2.8%
나 284
 
2.7%
본 273
 
2.6%
Other values (143) 6522
61.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10637
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 878
 
8.3%
아 668
 
6.3%
미 417
 
3.9%
리 347
 
3.3%
일 320
 
3.0%
중 317
 
3.0%
트 312
 
2.9%
시 299
 
2.8%
나 284
 
2.7%
본 273
 
2.6%
Other values (143) 6522
61.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10637
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
국 878
 
8.3%
아 668
 
6.3%
미 417
 
3.9%
리 347
 
3.3%
일 320
 
3.0%
중 317
 
3.0%
트 312
 
2.9%
시 299
 
2.8%
나 284
 
2.7%
본 273
 
2.6%
Other values (143) 6522
61.3%

경제권명
Categorical

Distinct22
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size27.9 KiB
<NA>
438 
동남아국가연합/아시아.태평양 경제협력체/역내포괄적경제동반자협정
411 
아시아.태평양 경제협력체/경제협력개발기구/북미자유무역연합
323 
아시아.태평양 경제협력체/역내포괄적경제동반자협정/한중일
317 
아시아.태평양 경제협력체/경제협력개발기구/역내포괄적경제동반자협정/한중일/포괄적.점진적 환태평양 경제동반자협졍
273 
Other values (17)
1795 

Length

Max length68
Median length53
Mean length31.840877
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 438
12.3%
동남아국가연합/아시아.태평양 경제협력체/역내포괄적경제동반자협정 411
11.6%
아시아.태평양 경제협력체/경제협력개발기구/북미자유무역연합 323
9.1%
아시아.태평양 경제협력체/역내포괄적경제동반자협정/한중일 317
8.9%
아시아.태평양 경제협력체/경제협력개발기구/역내포괄적경제동반자협정/한중일/포괄적.점진적 환태평양 경제동반자협졍 273
 
7.7%
유럽연합/경제협력개발기구 263
 
7.4%
동남아국가연합/환태평양경제동반자협정/역내포괄적경제동반자협정/포괄적.점진적 환태평양 경제동반자협졍 213
 
6.0%
아시아.태평양 경제협력체 195
 
5.5%
아시아.태평양 경제협력체/경제협력개발기구/환태평양경제동반자협정/역내포괄적경제동반자협정/포괄적.점진적 환태평양 경제동반자협졍 193
 
5.4%
동남아국가연합/아시아.태평양 경제협력체/환태평양경제동반자협정/역내포괄적경제동반자협정/포괄적.점진적 환태평양 경제동반자협졍 193
 
5.4%
Other values (12) 738
20.7%

Length

2024-04-21T11:08:57.648482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
아시아.태평양 1511
19.2%
환태평양 1099
14.0%
경제동반자협졍 1099
14.0%
동남아국가연합/아시아.태평양 604
 
7.7%
na 438
 
5.6%
경제협력체/역내포괄적경제동반자협정 411
 
5.2%
경제협력체/경제협력개발기구/북미자유무역연합 323
 
4.1%
경제협력체/역내포괄적경제동반자협정/한중일 317
 
4.0%
경제협력체/경제협력개발기구/역내포괄적경제동반자협정/한중일/포괄적.점진적 273
 
3.5%
유럽연합/경제협력개발기구 263
 
3.3%
Other values (16) 1532
19.5%

수산물수출입품목명
Text

Distinct433
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size27.9 KiB
2024-04-21T11:08:57.869553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length108
Median length53
Mean length16.504358
Min length1

Characters and Unicode

Total characters58706
Distinct characters380
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)2.6%

Sample

1st row기타 포유동물
2nd row넙치(활어)
3rd row기타어류(활어)
4th row터벗(신선 또는 냉장)(프세타 맥시마)
5th row청어(클루페아 하렌구스,클루페아 팔라시)
ValueCountFrequency (%)
것 1019
 
10.6%
기타 591
 
6.1%
또는 446
 
4.6%
넣은 288
 
3.0%
이외 226
 
2.3%
것)(식용의 148
 
1.5%
김 126
 
1.3%
피레트 112
 
1.2%
저장처리)(밀폐용기에 111
 
1.1%
외 101
 
1.0%
Other values (621) 6487
67.2%
2024-04-21T11:08:58.239656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6132
 
10.4%
) 3451
 
5.9%
( 3442
 
5.9%
기 2259
 
3.8%
타 1725
 
2.9%
, 1559
 
2.7%
어 1554
 
2.6%
것 1488
 
2.5%
조 1238
 
2.1%
스 1091
 
1.9%
Other values (370) 34767
59.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 41310
70.4%
Space Separator 6132
 
10.4%
Open Punctuation 3583
 
6.1%
Close Punctuation 3575
 
6.1%
Other Punctuation 2036
 
3.5%
Lowercase Letter 1816
 
3.1%
Uppercase Letter 183
 
0.3%
Dash Punctuation 53
 
0.1%
Decimal Number 18
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 2259
 
5.5%
타 1725
 
4.2%
어 1554
 
3.8%
것 1488
 
3.6%
조 1238
 
3.0%
스 1091
 
2.6%
제 1013
 
2.5%
냉 935
 
2.3%
한 892
 
2.2%
동 855
 
2.1%
Other values (325) 28260
68.4%
Lowercase Letter
ValueCountFrequency (%)
o 237
13.1%
s 216
11.9%
a 205
11.3%
i 192
10.6%
r 132
7.3%
e 119
 
6.6%
l 112
 
6.2%
m 103
 
5.7%
t 94
 
5.2%
u 93
 
5.1%
Other values (9) 313
17.2%
Uppercase Letter
ValueCountFrequency (%)
S 56
30.6%
E 24
13.1%
O 22
 
12.0%
N 16
 
8.7%
L 15
 
8.2%
C 13
 
7.1%
R 13
 
7.1%
H 12
 
6.6%
P 9
 
4.9%
J 2
 
1.1%
Decimal Number
ValueCountFrequency (%)
6 7
38.9%
0 4
22.2%
3 3
16.7%
2 3
16.7%
5 1
 
5.6%
Open Punctuation
ValueCountFrequency (%)
( 3442
96.1%
[ 132
 
3.7%
{ 9
 
0.3%
Other Punctuation
ValueCountFrequency (%)
, 1559
76.6%
/ 442
 
21.7%
. 35
 
1.7%
Close Punctuation
ValueCountFrequency (%)
) 3451
96.5%
] 124
 
3.5%
Space Separator
ValueCountFrequency (%)
6132
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 41227
70.2%
Common 15397
 
26.2%
Latin 1999
 
3.4%
Han 83
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 2259
 
5.5%
타 1725
 
4.2%
어 1554
 
3.8%
것 1488
 
3.6%
조 1238
 
3.0%
스 1091
 
2.6%
제 1013
 
2.5%
냉 935
 
2.3%
한 892
 
2.2%
동 855
 
2.1%
Other values (315) 28177
68.3%
Latin
ValueCountFrequency (%)
o 237
11.9%
s 216
10.8%
a 205
10.3%
i 192
 
9.6%
r 132
 
6.6%
e 119
 
6.0%
l 112
 
5.6%
m 103
 
5.2%
t 94
 
4.7%
u 93
 
4.7%
Other values (20) 496
24.8%
Common
ValueCountFrequency (%)
6132
39.8%
) 3451
22.4%
( 3442
22.4%
, 1559
 
10.1%
/ 442
 
2.9%
[ 132
 
0.9%
] 124
 
0.8%
- 53
 
0.3%
. 35
 
0.2%
{ 9
 
0.1%
Other values (5) 18
 
0.1%
Han
ValueCountFrequency (%)
油 23
27.7%
肝 23
27.7%
岩 11
13.3%
鹽 11
13.3%
魚 6
 
7.2%
白 3
 
3.6%
卵 3
 
3.6%
水 1
 
1.2%
冷 1
 
1.2%
性 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 41122
70.0%
ASCII 17396
29.6%
Compat Jamo 105
 
0.2%
CJK 82
 
0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6132
35.2%
) 3451
19.8%
( 3442
19.8%
, 1559
 
9.0%
/ 442
 
2.5%
o 237
 
1.4%
s 216
 
1.2%
a 205
 
1.2%
i 192
 
1.1%
r 132
 
0.8%
Other values (35) 1388
 
8.0%
Hangul
ValueCountFrequency (%)
기 2259
 
5.5%
타 1725
 
4.2%
어 1554
 
3.8%
것 1488
 
3.6%
조 1238
 
3.0%
스 1091
 
2.7%
제 1013
 
2.5%
냉 935
 
2.3%
한 892
 
2.2%
동 855
 
2.1%
Other values (314) 28072
68.3%
Compat Jamo
ValueCountFrequency (%)
ㆍ 105
100.0%
CJK
ValueCountFrequency (%)
油 23
28.0%
肝 23
28.0%
岩 11
13.4%
鹽 11
13.4%
魚 6
 
7.3%
白 3
 
3.7%
卵 3
 
3.7%
水 1
 
1.2%
性 1
 
1.2%
CJK Compat Ideographs
ValueCountFrequency (%)
冷 1
100.0%

당월수출입중량(킬로그램)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1547
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178266.46
Minimum0
Maximum3.36072 × 108
Zeros1014
Zeros (%)28.5%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2024-04-21T11:08:58.376616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median100
Q34650
95-th percentile150082
Maximum3.36072 × 108
Range3.36072 × 108
Interquartile range (IQR)4650

Descriptive statistics

Standard deviation5923461.2
Coefficient of variation (CV)33.228131
Kurtosis2937.8749
Mean178266.46
Median Absolute Deviation (MAD)100
Skewness52.902663
Sum6.3409379 × 108
Variance3.5087393 × 1013
MonotonicityNot monotonic
2024-04-21T11:08:58.511777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1014
28.5%
1 109
 
3.1%
2 58
 
1.6%
5 40
 
1.1%
3 32
 
0.9%
6 30
 
0.8%
8 22
 
0.6%
10 21
 
0.6%
4 20
 
0.6%
20 19
 
0.5%
Other values (1537) 2192
61.6%
ValueCountFrequency (%)
0 1014
28.5%
1 109
 
3.1%
2 58
 
1.6%
3 32
 
0.9%
4 20
 
0.6%
5 40
 
1.1%
6 30
 
0.8%
7 14
 
0.4%
8 22
 
0.6%
9 12
 
0.3%
ValueCountFrequency (%)
336072000 1
< 0.1%
105622000 1
< 0.1%
15511067 1
< 0.1%
13522026 1
< 0.1%
8050570 1
< 0.1%
7095000 1
< 0.1%
6800000 1
< 0.1%
5538040 1
< 0.1%
5446816 1
< 0.1%
3604680 1
< 0.1%

당월수출입미화금액(달러)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2248
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195282.53
Minimum0
Maximum39792568
Zeros980
Zeros (%)27.6%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2024-04-21T11:08:58.665805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1448
Q336518
95-th percentile689249.4
Maximum39792568
Range39792568
Interquartile range (IQR)36518

Descriptive statistics

Standard deviation1208551.5
Coefficient of variation (CV)6.1887332
Kurtosis427.86409
Mean195282.53
Median Absolute Deviation (MAD)1448
Skewness17.316521
Sum6.9461994 × 108
Variance1.4605966 × 1012
MonotonicityNot monotonic
2024-04-21T11:08:58.816414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 980
 
27.6%
6 10
 
0.3%
15 9
 
0.3%
2 8
 
0.2%
20 8
 
0.2%
12 7
 
0.2%
21 7
 
0.2%
9 6
 
0.2%
23 6
 
0.2%
17 6
 
0.2%
Other values (2238) 2510
70.6%
ValueCountFrequency (%)
0 980
27.6%
1 6
 
0.2%
2 8
 
0.2%
3 5
 
0.1%
4 6
 
0.2%
5 4
 
0.1%
6 10
 
0.3%
7 6
 
0.2%
8 5
 
0.1%
9 6
 
0.2%
ValueCountFrequency (%)
39792568 1
< 0.1%
25183073 1
< 0.1%
19462980 1
< 0.1%
15336708 1
< 0.1%
14821537 1
< 0.1%
14378393 1
< 0.1%
13127856 1
< 0.1%
12814632 1
< 0.1%
12159914 1
< 0.1%
10836237 1
< 0.1%

당해누계수출입중량(킬로그램)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2065
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean348872.24
Minimum0
Maximum6.07999 × 108
Zeros60
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2024-04-21T11:08:58.950350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q149
median780
Q316339
95-th percentile339170.8
Maximum6.07999 × 108
Range6.07999 × 108
Interquartile range (IQR)16290

Descriptive statistics

Standard deviation10619872
Coefficient of variation (CV)30.440577
Kurtosis3031.6609
Mean348872.24
Median Absolute Deviation (MAD)778
Skewness53.744044
Sum1.2409385 × 109
Variance1.1278168 × 1014
MonotonicityNot monotonic
2024-04-21T11:08:59.085907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 165
 
4.6%
2 77
 
2.2%
0 60
 
1.7%
5 52
 
1.5%
3 44
 
1.2%
10 41
 
1.2%
4 33
 
0.9%
6 30
 
0.8%
20 27
 
0.8%
50 23
 
0.6%
Other values (2055) 3005
84.5%
ValueCountFrequency (%)
0 60
 
1.7%
1 165
4.6%
2 77
2.2%
3 44
 
1.2%
4 33
 
0.9%
5 52
 
1.5%
6 30
 
0.8%
7 13
 
0.4%
8 21
 
0.6%
9 16
 
0.4%
ValueCountFrequency (%)
607999003 1
< 0.1%
162252000 1
< 0.1%
44321375 1
< 0.1%
35825431 1
< 0.1%
27401417 1
< 0.1%
17219000 1
< 0.1%
12767903 1
< 0.1%
10156000 1
< 0.1%
10000006 1
< 0.1%
8725690 1
< 0.1%

당해누계수출입미화금액(달러)
Real number (ℝ)

HIGH CORRELATION 

Distinct3016
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean412444.27
Minimum0
Maximum50076264
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2024-04-21T11:08:59.227894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21
Q1740
median9536
Q389622
95-th percentile1483135.4
Maximum50076264
Range50076264
Interquartile range (IQR)88882

Descriptive statistics

Standard deviation2179072.1
Coefficient of variation (CV)5.2833127
Kurtosis180.71636
Mean412444.27
Median Absolute Deviation (MAD)9492
Skewness11.734068
Sum1.4670643 × 109
Variance4.7483551 × 1012
MonotonicityNot monotonic
2024-04-21T11:08:59.388846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 14
 
0.4%
15 12
 
0.3%
6 12
 
0.3%
20 12
 
0.3%
3 11
 
0.3%
9 10
 
0.3%
8 10
 
0.3%
67 10
 
0.3%
11 9
 
0.3%
16 9
 
0.3%
Other values (3006) 3448
96.9%
ValueCountFrequency (%)
0 5
 
0.1%
1 7
0.2%
2 14
0.4%
3 11
0.3%
4 7
0.2%
5 5
 
0.1%
6 12
0.3%
7 8
0.2%
8 10
0.3%
9 10
0.3%
ValueCountFrequency (%)
50076264 1
< 0.1%
41139688 1
< 0.1%
37613492 1
< 0.1%
26929715 1
< 0.1%
26791783 1
< 0.1%
26343742 1
< 0.1%
25271781 1
< 0.1%
24593503 1
< 0.1%
24182002 1
< 0.1%
22829590 1
< 0.1%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.9 KiB
2024-02-29
3557 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-02-29
2nd row2024-02-29
3rd row2024-02-29
4th row2024-02-29
5th row2024-02-29

Common Values

ValueCountFrequency (%)
2024-02-29 3557
100.0%

Length

2024-04-21T11:08:59.549479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:08:59.658361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-02-29 3557
100.0%

Interactions

2024-04-21T11:08:54.164332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:51.920624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:52.517184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:53.036655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:53.677727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:54.248881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:52.076847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:52.619137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:53.132174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:53.774611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:54.338139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:52.178180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:52.722013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:53.259883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:53.872076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:54.431356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:52.288815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:52.834132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:53.382491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:53.985800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:54.543022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:52.399793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:52.938536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:53.523478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:08:54.076097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T11:08:59.724691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
HSK품목코드수출입구분코드수출입구분명경제권명당월수출입중량(킬로그램)당월수출입미화금액(달러)당해누계수출입중량(킬로그램)당해누계수출입미화금액(달러)
HSK품목코드1.0000.0790.0790.2370.0430.0000.0430.000
수출입구분코드0.0791.0001.0000.4280.0070.0500.0070.069
수출입구분명0.0791.0001.0000.4280.0070.0500.0070.069
경제권명0.2370.4280.4281.0000.2290.2740.2290.220
당월수출입중량(킬로그램)0.0430.0070.0070.2291.0000.4291.0000.660
당월수출입미화금액(달러)0.0000.0500.0500.2740.4291.0000.4290.936
당해누계수출입중량(킬로그램)0.0430.0070.0070.2291.0000.4291.0000.660
당해누계수출입미화금액(달러)0.0000.0690.0690.2200.6600.9360.6601.000
2024-04-21T11:08:59.857675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수출입구분코드경제권명수출입구분명
수출입구분코드1.0000.3750.999
경제권명0.3751.0000.375
수출입구분명0.9990.3751.000
2024-04-21T11:08:59.965787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
HSK품목코드당월수출입중량(킬로그램)당월수출입미화금액(달러)당해누계수출입중량(킬로그램)당해누계수출입미화금액(달러)수출입구분코드수출입구분명경제권명
HSK품목코드1.000-0.089-0.092-0.148-0.1510.0960.0960.116
당월수출입중량(킬로그램)-0.0891.0000.9710.7150.6970.0110.0110.107
당월수출입미화금액(달러)-0.0920.9711.0000.6730.7240.0540.0540.106
당해누계수출입중량(킬로그램)-0.1480.7150.6731.0000.9350.0110.0110.107
당해누계수출입미화금액(달러)-0.1510.6970.7240.9351.0000.0690.0690.086
수출입구분코드0.0960.0110.0540.0110.0691.0000.9990.375
수출입구분명0.0960.0110.0540.0110.0690.9991.0000.375
경제권명0.1160.1070.1060.1070.0860.3750.3751.000

Missing values

2024-04-21T11:08:54.718199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T11:08:54.926053image/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

기준년월국가코드HSK품목코드수출입구분코드수출입구분명국가명경제권명수산물수출입품목명당월수출입중량(킬로그램)당월수출입미화금액(달러)당해누계수출입중량(킬로그램)당해누계수출입미화금액(달러)데이터기준일자
02024-02AE106199000E수출아랍에미리트연합걸프협력회의기타 포유동물4390218452024-02-29
12024-02AE301998000E수출아랍에미리트연합걸프협력회의넙치(활어)745173661543371012024-02-29
22024-02AE301999099E수출아랍에미리트연합걸프협력회의기타어류(활어)30012100300121002024-02-29
32024-02AE302240000E수출아랍에미리트연합걸프협력회의터벗(신선 또는 냉장)(프세타 맥시마)00304114252024-02-29
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