Overview

Dataset statistics

Number of variables4
Number of observations101
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory33.3 B

Variable types

Categorical3
Text1

Dataset

Description카테고리별(신성장, 기초원재료, 소재부품장비, 취약산업, 물가수급안정, 긴급할당관세 연장) 23년 할당관세 적용물품(총101개)에 대한 기본관세율 및 할당 관세율
Author기획재정부
URLhttps://www.data.go.kr/data/15121011/fileData.do

Alerts

구분 is highly overall correlated with 기본세율(퍼센트)High correlation
기본세율(퍼센트) is highly overall correlated with 구분High correlation
할당관세율(퍼센트) is highly imbalanced (75.0%)Imbalance
품목 has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:33:16.469066
Analysis finished2023-12-12 22:33:16.765200
Duration0.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size940.0 B
취약산업
31 
신성장
20 
기초원재료
19 
소재·부품·장비
14 
물가·수급 안정
11 

Length

Max length9
Median length8
Mean length5.2772277
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취약산업 31
30.7%
신성장 20
19.8%
기초원재료 19
18.8%
소재·부품·장비 14
13.9%
물가·수급 안정 11
 
10.9%
긴급할당관세 연장 6
 
5.9%

Length

2023-12-13T07:33:16.826368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:33:16.922375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
취약산업 31
26.3%
신성장 20
16.9%
기초원재료 19
16.1%
소재·부품·장비 14
11.9%
물가·수급 11
 
9.3%
안정 11
 
9.3%
긴급할당관세 6
 
5.1%
연장 6
 
5.1%

품목
Text

UNIQUE 

Distinct101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
2023-12-13T07:33:17.182427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length5.7128713
Min length2

Characters and Unicode

Total characters577
Distinct characters196
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)100.0%

Sample

1st row흑연화합물
2nd row전극
3rd row전해액
4th row리튬코발트산화물
5th row리튬니켈코발트 망간산화물
ValueCountFrequency (%)
lpg 2
 
1.8%
원유 2
 
1.8%
제조용 2
 
1.8%
이산화티타늄 1
 
0.9%
면실 1
 
0.9%
옥수수(가공용 1
 
0.9%
매니옥칩 1
 
0.9%
밀기울 1
 
0.9%
사료용근채류 1
 
0.9%
겉보리 1
 
0.9%
Other values (97) 97
88.2%
2023-12-13T07:33:17.617998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15
 
2.6%
리 15
 
2.6%
2 14
 
2.4%
3 13
 
2.3%
로 12
 
2.1%
- 12
 
2.1%
유 12
 
2.1%
) 11
 
1.9%
( 11
 
1.9%
트 10
 
1.7%
Other values (186) 452
78.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 461
79.9%
Decimal Number 48
 
8.3%
Uppercase Letter 18
 
3.1%
Dash Punctuation 12
 
2.1%
Close Punctuation 11
 
1.9%
Open Punctuation 11
 
1.9%
Space Separator 9
 
1.6%
Math Symbol 6
 
1.0%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 15
 
3.3%
로 12
 
2.6%
유 12
 
2.6%
트 10
 
2.2%
이 8
 
1.7%
제 8
 
1.7%
기 8
 
1.7%
산 8
 
1.7%
소 7
 
1.5%
탄 7
 
1.5%
Other values (164) 366
79.4%
Uppercase Letter
ValueCountFrequency (%)
P 4
22.2%
L 3
16.7%
G 3
16.7%
D 2
11.1%
F 1
 
5.6%
V 1
 
5.6%
E 1
 
5.6%
N 1
 
5.6%
A 1
 
5.6%
X 1
 
5.6%
Decimal Number
ValueCountFrequency (%)
0 15
31.2%
2 14
29.2%
3 13
27.1%
6 3
 
6.2%
1 2
 
4.2%
8 1
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%
Other Punctuation
ValueCountFrequency (%)
· 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 461
79.9%
Common 98
 
17.0%
Latin 18
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 15
 
3.3%
로 12
 
2.6%
유 12
 
2.6%
트 10
 
2.2%
이 8
 
1.7%
제 8
 
1.7%
기 8
 
1.7%
산 8
 
1.7%
소 7
 
1.5%
탄 7
 
1.5%
Other values (164) 366
79.4%
Common
ValueCountFrequency (%)
0 15
15.3%
2 14
14.3%
3 13
13.3%
- 12
12.2%
) 11
11.2%
( 11
11.2%
9
9.2%
~ 6
 
6.1%
6 3
 
3.1%
1 2
 
2.0%
Other values (2) 2
 
2.0%
Latin
ValueCountFrequency (%)
P 4
22.2%
L 3
16.7%
G 3
16.7%
D 2
11.1%
F 1
 
5.6%
V 1
 
5.6%
E 1
 
5.6%
N 1
 
5.6%
A 1
 
5.6%
X 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 461
79.9%
ASCII 115
 
19.9%
None 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15
13.0%
2 14
12.2%
3 13
11.3%
- 12
10.4%
) 11
9.6%
( 11
9.6%
9
7.8%
~ 6
 
5.2%
P 4
 
3.5%
L 3
 
2.6%
Other values (11) 17
14.8%
Hangul
ValueCountFrequency (%)
리 15
 
3.3%
로 12
 
2.6%
유 12
 
2.6%
트 10
 
2.2%
이 8
 
1.7%
제 8
 
1.7%
기 8
 
1.7%
산 8
 
1.7%
소 7
 
1.5%
탄 7
 
1.5%
Other values (164) 366
79.4%
None
ValueCountFrequency (%)
· 1
100.0%

기본세율(퍼센트)
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size940.0 B
8
37 
3
20 
5
11 
2
10 
1
4 
Other values (14)
19 

Length

Max length7
Median length1
Mean length1.4257426
Min length1

Unique

Unique10 ?
Unique (%)9.9%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 37
36.6%
3 20
19.8%
5 11
 
10.9%
2 10
 
9.9%
1 4
 
4.0%
5.5 3
 
3.0%
4 2
 
2.0%
10 2
 
2.0%
20 2
 
2.0%
30 1
 
1.0%
Other values (9) 9
 
8.9%

Length

2023-12-13T07:33:17.777605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8 37
35.9%
3 20
19.4%
5 11
 
10.7%
2 10
 
9.7%
1 4
 
3.9%
5.5 3
 
2.9%
4 2
 
1.9%
10 2
 
1.9%
20 2
 
1.9%
08일 2
 
1.9%
Other values (10) 10
 
9.7%

할당관세율(퍼센트)
Categorical

IMBALANCE 

Distinct7
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size940.0 B
0
91 
0/2
 
3
1
 
3
0/0.5
 
1
3
 
1
Other values (2)
 
2

Length

Max length5
Median length1
Mean length1.1089109
Min length1

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 91
90.1%
0/2 3
 
3.0%
1 3
 
3.0%
0/0.5 1
 
1.0%
3 1
 
1.0%
5 1
 
1.0%
10 1
 
1.0%

Length

2023-12-13T07:33:17.898918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:33:18.005987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 91
90.1%
0/2 3
 
3.0%
1 3
 
3.0%
0/0.5 1
 
1.0%
3 1
 
1.0%
5 1
 
1.0%
10 1
 
1.0%

Correlations

2023-12-13T07:33:18.087494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분기본세율(퍼센트)할당관세율(퍼센트)
구분1.0000.8350.285
기본세율(퍼센트)0.8351.0000.786
할당관세율(퍼센트)0.2850.7861.000
2023-12-13T07:33:18.193621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본세율(퍼센트)구분할당관세율(퍼센트)
기본세율(퍼센트)1.0000.5390.465
구분0.5391.0000.171
할당관세율(퍼센트)0.4650.1711.000
2023-12-13T07:33:18.286593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분기본세율(퍼센트)할당관세율(퍼센트)
구분1.0000.5390.171
기본세율(퍼센트)0.5391.0000.465
할당관세율(퍼센트)0.1710.4651.000

Missing values

2023-12-13T07:33:16.665451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:33:16.735715image/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

구분품목기본세율(퍼센트)할당관세율(퍼센트)
0신성장흑연화합물80
1신성장전극80
2신성장전해액80
3신성장리튬코발트산화물80
4신성장리튬니켈코발트 망간산화물80
5신성장테트라에틸암모늄 테트라플루오로보레이트80
6신성장아세틸렌블랙80
7신성장구리박80
8신성장황산코발트50
9신성장리튬망간산화물80
구분품목기본세율(퍼센트)할당관세율(퍼센트)
91물가·수급 안정대두유50
92물가·수급 안정해바라기씨유50
93물가·수급 안정커피원두(생두)20
94물가·수급 안정감자·변성전분80
95긴급할당관세 연장양파(~2023-02-28)5010
96긴급할당관세 연장닭고기(~2023-03-31)20~300
97긴급할당관세 연장고등어(~2023-03-31)100
98긴급할당관세 연장돼지고기(~2023-06-30)22.5/250
99긴급할당관세 연장계란가공품(~2023-06-30)8~300
100긴급할당관세 연장조주정(~2023-06-30)100