Overview

Dataset statistics

Number of variables5
Number of observations40
Missing cells28
Missing cells (%)14.0%
Duplicate rows1
Duplicate rows (%)2.5%
Total size in memory1.7 KiB
Average record size in memory43.3 B

Variable types

Text1
Categorical1
Unsupported3

Dataset

Description주요 FTA별 특혜관세 적용현황 및 FTA협정 이행국가별 교역 실적에 대한 데이터 입니다. 자세한 내용은 첨부파일을 참고하시기 바랍니다.
URLhttps://www.data.go.kr/data/15121019/fileData.do

Alerts

Dataset has 1 (2.5%) duplicate rowsDuplicates
* 주요 FTA별 특혜관세 적용현황 has 19 (47.5%) missing valuesMissing
Unnamed: 2 has 3 (7.5%) missing valuesMissing
Unnamed: 3 has 3 (7.5%) missing valuesMissing
Unnamed: 4 has 3 (7.5%) missing valuesMissing
Unnamed: 2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 3 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 4 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 07:57:13.920068
Analysis finished2023-12-12 07:57:14.441820
Duration0.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

* 주요 FTA별 특혜관세 적용현황
Text

MISSING 

Distinct21
Distinct (%)100.0%
Missing19
Missing (%)47.5%
Memory size452.0 B
2023-12-12T16:57:14.655166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length17
Mean length15.333333
Min length5

Characters and Unicode

Total characters322
Distinct characters105
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row협정(발효일)
2nd row칠레(’04.4.)
3rd rowEFTA(’06.9.)
4th rowASEAN(’07.6.)
5th row인도(’10.1.)
ValueCountFrequency (%)
2
 
4.3%
수출(수입)액 2
 
4.3%
활용률 2
 
4.3%
협정(발효일 1
 
2.2%
제외하고 1
 
2.2%
별도 1
 
2.2%
협정이 1
 
2.2%
있는 1
 
2.2%
asean 1
 
2.2%
호주 1
 
2.2%
Other values (33) 33
71.7%
2023-12-12T16:57:15.140421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 34
 
10.6%
24
 
7.5%
1 22
 
6.8%
( 21
 
6.5%
) 21
 
6.5%
’ 17
 
5.3%
2 10
 
3.1%
5 6
 
1.9%
국 6
 
1.9%
A 6
 
1.9%
Other values (95) 155
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 111
34.5%
Decimal Number 56
17.4%
Other Punctuation 41
 
12.7%
Uppercase Letter 27
 
8.4%
Space Separator 24
 
7.5%
Open Punctuation 21
 
6.5%
Close Punctuation 21
 
6.5%
Final Punctuation 17
 
5.3%
Control 3
 
0.9%
Math Symbol 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 6
 
5.4%
수 4
 
3.6%
중 4
 
3.6%
용 3
 
2.7%
일 3
 
2.7%
질 2
 
1.8%
랜 2
 
1.8%
뉴 2
 
1.8%
드 2
 
1.8%
혜 2
 
1.8%
Other values (64) 81
73.0%
Decimal Number
ValueCountFrequency (%)
1 22
39.3%
2 10
17.9%
5 6
 
10.7%
0 4
 
7.1%
4 3
 
5.4%
6 3
 
5.4%
3 3
 
5.4%
7 3
 
5.4%
9 1
 
1.8%
8 1
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
A 6
22.2%
E 6
22.2%
P 2
 
7.4%
R 2
 
7.4%
C 2
 
7.4%
F 2
 
7.4%
T 2
 
7.4%
S 2
 
7.4%
N 2
 
7.4%
U 1
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 34
82.9%
, 3
 
7.3%
* 2
 
4.9%
/ 1
 
2.4%
※ 1
 
2.4%
Space Separator
ValueCountFrequency (%)
24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 17
100.0%
Control
ValueCountFrequency (%)
3
100.0%
Math Symbol
ValueCountFrequency (%)
= 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 184
57.1%
Hangul 111
34.5%
Latin 27
 
8.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 6
 
5.4%
수 4
 
3.6%
중 4
 
3.6%
용 3
 
2.7%
일 3
 
2.7%
질 2
 
1.8%
랜 2
 
1.8%
뉴 2
 
1.8%
드 2
 
1.8%
혜 2
 
1.8%
Other values (64) 81
73.0%
Common
ValueCountFrequency (%)
. 34
18.5%
24
13.0%
1 22
12.0%
( 21
11.4%
) 21
11.4%
’ 17
9.2%
2 10
 
5.4%
5 6
 
3.3%
0 4
 
2.2%
4 3
 
1.6%
Other values (11) 22
12.0%
Latin
ValueCountFrequency (%)
A 6
22.2%
E 6
22.2%
P 2
 
7.4%
R 2
 
7.4%
C 2
 
7.4%
F 2
 
7.4%
T 2
 
7.4%
S 2
 
7.4%
N 2
 
7.4%
U 1
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 193
59.9%
Hangul 111
34.5%
Punctuation 18
 
5.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 34
17.6%
24
12.4%
1 22
11.4%
( 21
10.9%
) 21
10.9%
2 10
 
5.2%
5 6
 
3.1%
A 6
 
3.1%
E 6
 
3.1%
0 4
 
2.1%
Other values (19) 39
20.2%
Punctuation
ValueCountFrequency (%)
’ 17
94.4%
※ 1
 
5.6%
Hangul
ValueCountFrequency (%)
국 6
 
5.4%
수 4
 
3.6%
중 4
 
3.6%
용 3
 
2.7%
일 3
 
2.7%
질 2
 
1.8%
랜 2
 
1.8%
뉴 2
 
1.8%
드 2
 
1.8%
혜 2
 
1.8%
Other values (64) 81
73.0%

Unnamed: 1
Categorical

Distinct5
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
수출
18 
수입
18 
<NA>
2 
FTA 활용률(%)
 
1
구분
 
1

Length

Max length10
Median length2
Mean length2.3
Min length2

Unique

Unique2 ?
Unique (%)5.0%

Sample

1st rowFTA 활용률(%)
2nd row구분
3rd row수출
4th row수입
5th row수출

Common Values

ValueCountFrequency (%)
수출 18
45.0%
수입 18
45.0%
<NA> 2
 
5.0%
FTA 활용률(%) 1
 
2.5%
구분 1
 
2.5%

Length

2023-12-12T16:57:15.312543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:57:15.433476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수출 18
43.9%
수입 18
43.9%
na 2
 
4.9%
fta 1
 
2.4%
활용률 1
 
2.4%
구분 1
 
2.4%

Unnamed: 2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3
Missing (%)7.5%
Memory size452.0 B

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3
Missing (%)7.5%
Memory size452.0 B

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3
Missing (%)7.5%
Memory size452.0 B

Correlations

2023-12-12T16:57:15.515804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
* 주요 FTA별 특혜관세 적용현황Unnamed: 1
* 주요 FTA별 특혜관세 적용현황1.0001.000
Unnamed: 11.0001.000

Missing values

2023-12-12T16:57:14.082675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:57:14.220112image/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.
2023-12-12T16:57:14.347665image/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

* 주요 FTA별 특혜관세 적용현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4
0협정(발효일)FTA 활용률(%)NaNNaNNaN
1<NA>구분’20’21’22
2칠레(’04.4.)수출68.663.666.3
3<NA>수입99.199.398.6
4EFTA(’06.9.)수출80.17161.4
5<NA>수입76.969.675.7
6ASEAN(’07.6.)수출49.25258.1
7<NA>수입81.582.784.4
8인도(’10.1.)수출74.677.879.5
9<NA>수입55.65455.2
* 주요 FTA별 특혜관세 적용현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4
30영국(’21.1.)수출-90.289.3
31<NA>수입-67.363.9
32중미5개국(’21.3.)수출-19.325.9
33<NA>수입-79.868.9
34RCEP*(일본)(’22.2.)수출--39.1
35<NA>수입--25.5
36전체 평균수출74.875.775.5
37<NA>수입81.580.378.6
38* RCEP 회원국 중 별도 협정이 있는 ASEAN, 호주, 중국, 뉴질랜드는 제외하고 일본만 활용률 계산<NA>NaNNaNNaN
39※ FTA 활용률 = 실제로 특혜관세를 적용받은 수출(수입)액/ 특혜관세 대상품목의 수출(수입)액<NA>NaNNaNNaN

Duplicate rows

Most frequently occurring

* 주요 FTA별 특혜관세 적용현황Unnamed: 1# duplicates
0<NA>수입18