Overview

Dataset statistics

Number of variables6
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory52.4 B

Variable types

Text6

Dataset

Description한국무역보험공사 2019년 수출대금 결제비중을 글로벌 상위 30개 수입자업종별로 구분해 제시한 파일 데이터 자료입니다.
Author한국무역보험공사
URLhttps://www.data.go.kr/data/15088893/fileData.do

Alerts

글로벌 상위30개 수입자업종 has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:25:36.245035
Analysis finished2023-12-12 11:25:37.026260
Duration0.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

글로벌 상위30개 수입자업종
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T20:25:37.313270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length15.233333
Min length7

Characters and Unicode

Total characters457
Distinct characters95
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row이동전화기 제조업
2nd row기타 가공식품 도매업
3rd row기타 화학물질 및 화학제품 도매업
4th row액체연료 및 관련제품 도매업
5th row승용차 및 기타 여객용 자동차 제조업
ValueCountFrequency (%)
제조업 16
 
11.9%
및 13
 
9.6%
기타 12
 
8.9%
도매업 12
 
8.9%
그 5
 
3.7%
외 5
 
3.7%
자동차 4
 
3.0%
신품 4
 
3.0%
부품 3
 
2.2%
기계 2
 
1.5%
Other values (55) 59
43.7%
2023-12-12T20:25:37.965167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
105
23.0%
업 31
 
6.8%
제 21
 
4.6%
기 19
 
4.2%
품 16
 
3.5%
조 16
 
3.5%
매 14
 
3.1%
및 13
 
2.8%
타 13
 
2.8%
도 13
 
2.8%
Other values (85) 196
42.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 347
75.9%
Space Separator 105
 
23.0%
Other Punctuation 4
 
0.9%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 31
 
8.9%
제 21
 
6.1%
기 19
 
5.5%
품 16
 
4.6%
조 16
 
4.6%
매 14
 
4.0%
및 13
 
3.7%
타 13
 
3.7%
도 13
 
3.7%
차 10
 
2.9%
Other values (81) 181
52.2%
Other Punctuation
ValueCountFrequency (%)
, 3
75.0%
. 1
 
25.0%
Space Separator
ValueCountFrequency (%)
105
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 347
75.9%
Common 110
 
24.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 31
 
8.9%
제 21
 
6.1%
기 19
 
5.5%
품 16
 
4.6%
조 16
 
4.6%
매 14
 
4.0%
및 13
 
3.7%
타 13
 
3.7%
도 13
 
3.7%
차 10
 
2.9%
Other values (81) 181
52.2%
Common
ValueCountFrequency (%)
105
95.5%
, 3
 
2.7%
. 1
 
0.9%
1 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 347
75.9%
ASCII 110
 
24.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
105
95.5%
, 3
 
2.7%
. 1
 
0.9%
1 1
 
0.9%
Hangul
ValueCountFrequency (%)
업 31
 
8.9%
제 21
 
6.1%
기 19
 
5.5%
품 16
 
4.6%
조 16
 
4.6%
매 14
 
4.0%
및 13
 
3.7%
타 13
 
3.7%
도 13
 
3.7%
차 10
 
2.9%
Other values (81) 181
52.2%

30일 이내
Text

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T20:25:38.268685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.7
Min length2

Characters and Unicode

Total characters81
Distinct characters10
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

Unique16 ?
Unique (%)53.3%

Sample

1st row44%
2nd row61%
3rd row35%
4th row56%
5th row14%
ValueCountFrequency (%)
21 3
 
10.0%
14 3
 
10.0%
44 2
 
6.7%
6 2
 
6.7%
7 2
 
6.7%
17 2
 
6.7%
4 1
 
3.3%
41 1
 
3.3%
11 1
 
3.3%
23 1
 
3.3%
Other values (12) 12
40.0%
2023-12-12T20:25:38.769438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
% 30
37.0%
1 15
18.5%
4 11
 
13.6%
2 8
 
9.9%
5 5
 
6.2%
6 4
 
4.9%
7 4
 
4.9%
3 2
 
2.5%
0 1
 
1.2%
8 1
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51
63.0%
Other Punctuation 30
37.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15
29.4%
4 11
21.6%
2 8
15.7%
5 5
 
9.8%
6 4
 
7.8%
7 4
 
7.8%
3 2
 
3.9%
0 1
 
2.0%
8 1
 
2.0%
Other Punctuation
ValueCountFrequency (%)
% 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 81
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
% 30
37.0%
1 15
18.5%
4 11
 
13.6%
2 8
 
9.9%
5 5
 
6.2%
6 4
 
4.9%
7 4
 
4.9%
3 2
 
2.5%
0 1
 
1.2%
8 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
% 30
37.0%
1 15
18.5%
4 11
 
13.6%
2 8
 
9.9%
5 5
 
6.2%
6 4
 
4.9%
7 4
 
4.9%
3 2
 
2.5%
0 1
 
1.2%
8 1
 
1.2%

31-60일
Text

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T20:25:39.062886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9666667
Min length2

Characters and Unicode

Total characters89
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

Unique16 ?
Unique (%)53.3%

Sample

1st row28%
2nd row21%
3rd row32%
4th row39%
5th row38%
ValueCountFrequency (%)
39 3
 
10.0%
31 3
 
10.0%
28 2
 
6.7%
38 2
 
6.7%
26 2
 
6.7%
21 2
 
6.7%
15 1
 
3.3%
11 1
 
3.3%
8 1
 
3.3%
14 1
 
3.3%
Other values (12) 12
40.0%
2023-12-12T20:25:39.601736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
% 30
33.7%
2 12
 
13.5%
3 11
 
12.4%
1 11
 
12.4%
8 5
 
5.6%
4 5
 
5.6%
9 4
 
4.5%
6 4
 
4.5%
0 3
 
3.4%
7 2
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 59
66.3%
Other Punctuation 30
33.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 12
20.3%
3 11
18.6%
1 11
18.6%
8 5
8.5%
4 5
8.5%
9 4
 
6.8%
6 4
 
6.8%
0 3
 
5.1%
7 2
 
3.4%
5 2
 
3.4%
Other Punctuation
ValueCountFrequency (%)
% 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 89
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
% 30
33.7%
2 12
 
13.5%
3 11
 
12.4%
1 11
 
12.4%
8 5
 
5.6%
4 5
 
5.6%
9 4
 
4.5%
6 4
 
4.5%
0 3
 
3.4%
7 2
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
% 30
33.7%
2 12
 
13.5%
3 11
 
12.4%
1 11
 
12.4%
8 5
 
5.6%
4 5
 
5.6%
9 4
 
4.5%
6 4
 
4.5%
0 3
 
3.4%
7 2
 
2.2%

61-90일
Text

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T20:25:40.338588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9333333
Min length2

Characters and Unicode

Total characters88
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

Unique15 ?
Unique (%)50.0%

Sample

1st row24%
2nd row12%
3rd row28%
4th row3%
5th row34%
ValueCountFrequency (%)
24 3
 
10.0%
30 2
 
6.7%
26 2
 
6.7%
35 2
 
6.7%
12 2
 
6.7%
39 2
 
6.7%
34 2
 
6.7%
46 1
 
3.3%
6 1
 
3.3%
31 1
 
3.3%
Other values (12) 12
40.0%
2023-12-12T20:25:40.912133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
% 30
34.1%
2 13
14.8%
3 10
 
11.4%
4 9
 
10.2%
1 6
 
6.8%
5 5
 
5.7%
9 5
 
5.7%
6 4
 
4.5%
0 3
 
3.4%
8 2
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58
65.9%
Other Punctuation 30
34.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 13
22.4%
3 10
17.2%
4 9
15.5%
1 6
10.3%
5 5
 
8.6%
9 5
 
8.6%
6 4
 
6.9%
0 3
 
5.2%
8 2
 
3.4%
7 1
 
1.7%
Other Punctuation
ValueCountFrequency (%)
% 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 88
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
% 30
34.1%
2 13
14.8%
3 10
 
11.4%
4 9
 
10.2%
1 6
 
6.8%
5 5
 
5.7%
9 5
 
5.7%
6 4
 
4.5%
0 3
 
3.4%
8 2
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
% 30
34.1%
2 13
14.8%
3 10
 
11.4%
4 9
 
10.2%
1 6
 
6.8%
5 5
 
5.7%
9 5
 
5.7%
6 4
 
4.5%
0 3
 
3.4%
8 2
 
2.3%

91-120일
Text

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T20:25:41.194253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2.5
Mean length2.5
Min length2

Characters and Unicode

Total characters75
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

Unique14 ?
Unique (%)46.7%

Sample

1st row1%
2nd row3%
3rd row4%
4th row2%
5th row12%
ValueCountFrequency (%)
6 4
 
13.3%
13 3
 
10.0%
7 3
 
10.0%
2 2
 
6.7%
12 2
 
6.7%
14 2
 
6.7%
10 1
 
3.3%
8 1
 
3.3%
24 1
 
3.3%
43 1
 
3.3%
Other values (10) 10
33.3%
2023-12-12T20:25:41.741690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
% 30
40.0%
1 13
17.3%
2 6
 
8.0%
4 6
 
8.0%
6 5
 
6.7%
3 5
 
6.7%
7 4
 
5.3%
9 2
 
2.7%
0 2
 
2.7%
5 1
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45
60.0%
Other Punctuation 30
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13
28.9%
2 6
13.3%
4 6
13.3%
6 5
 
11.1%
3 5
 
11.1%
7 4
 
8.9%
9 2
 
4.4%
0 2
 
4.4%
5 1
 
2.2%
8 1
 
2.2%
Other Punctuation
ValueCountFrequency (%)
% 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 75
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
% 30
40.0%
1 13
17.3%
2 6
 
8.0%
4 6
 
8.0%
6 5
 
6.7%
3 5
 
6.7%
7 4
 
5.3%
9 2
 
2.7%
0 2
 
2.7%
5 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 75
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
% 30
40.0%
1 13
17.3%
2 6
 
8.0%
4 6
 
8.0%
6 5
 
6.7%
3 5
 
6.7%
7 4
 
5.3%
9 2
 
2.7%
0 2
 
2.7%
5 1
 
1.3%

120일 초과
Text

Distinct17
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T20:25:42.008525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.2666667
Min length2

Characters and Unicode

Total characters68
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

Unique11 ?
Unique (%)36.7%

Sample

1st row4%
2nd row2%
3rd row0%
4th row1%
5th row1%
ValueCountFrequency (%)
0 4
13.3%
6 4
13.3%
4 3
10.0%
1 3
10.0%
5 3
10.0%
10 2
 
6.7%
13 1
 
3.3%
22 1
 
3.3%
17 1
 
3.3%
19 1
 
3.3%
Other values (7) 7
23.3%
2023-12-12T20:25:42.516774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
% 30
44.1%
1 9
 
13.2%
0 6
 
8.8%
6 5
 
7.4%
2 4
 
5.9%
4 3
 
4.4%
5 3
 
4.4%
9 3
 
4.4%
7 2
 
2.9%
3 2
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38
55.9%
Other Punctuation 30
44.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9
23.7%
0 6
15.8%
6 5
13.2%
2 4
10.5%
4 3
 
7.9%
5 3
 
7.9%
9 3
 
7.9%
7 2
 
5.3%
3 2
 
5.3%
8 1
 
2.6%
Other Punctuation
ValueCountFrequency (%)
% 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 68
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
% 30
44.1%
1 9
 
13.2%
0 6
 
8.8%
6 5
 
7.4%
2 4
 
5.9%
4 3
 
4.4%
5 3
 
4.4%
9 3
 
4.4%
7 2
 
2.9%
3 2
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
% 30
44.1%
1 9
 
13.2%
0 6
 
8.8%
6 5
 
7.4%
2 4
 
5.9%
4 3
 
4.4%
5 3
 
4.4%
9 3
 
4.4%
7 2
 
2.9%
3 2
 
2.9%

Correlations

2023-12-12T20:25:42.723540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
글로벌 상위30개 수입자업종30일 이내31-60일61-90일91-120일120일 초과
글로벌 상위30개 수입자업종1.0001.0001.0001.0001.0001.000
30일 이내1.0001.0000.8320.9330.7250.870
31-60일1.0000.8321.0000.5650.0000.894
61-90일1.0000.9330.5651.0000.7140.639
91-120일1.0000.7250.0000.7141.0000.658
120일 초과1.0000.8700.8940.6390.6581.000

Missing values

2023-12-12T20:25:36.751667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:25:36.942686image/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

글로벌 상위30개 수입자업종30일 이내31-60일61-90일91-120일120일 초과
0이동전화기 제조업44%28%24%1%4%
1기타 가공식품 도매업61%21%12%3%2%
2기타 화학물질 및 화학제품 도매업35%32%28%4%0%
3액체연료 및 관련제품 도매업56%39%3%2%1%
4승용차 및 기타 여객용 자동차 제조업14%38%34%12%1%
5기타 자동차 신품 부품 및 내장품 판매업23%26%25%16%10%
6상품 종합 도매업41%16%30%6%7%
7그 외 기타 전자부품 제조업14%36%26%19%5%
8전기용 기계.장비 및 관련 기자재 도매업11%31%35%17%6%
9타이어 및 튜브 제조업21%31%19%20%9%
글로벌 상위30개 수입자업종30일 이내31-60일61-90일91-120일120일 초과
20그 외 기타 플라스틱 제품 제조업17%28%46%5%5%
21그 외 기타 분류 안된 화학제품 제조업22%38%29%7%4%
22기타 반도체소자 제조업15%26%17%43%0%
23자동차 차체용 신품 부품 제조업6%42%26%13%13%
24컴퓨터 및 주변장치, 소프트웨어 도매업6%14%39%24%17%
25기타 무선 통신장비 제조업7%39%39%8%6%
26종이 원지, 판지, 종이상자 도매업42%8%12%9%29%
27그 외 기타 전기장비 제조업12%39%31%14%4%
28기타 산업용 기계 및 장비 도매업21%31%30%12%6%
29그 외 자동차용 신품 부품 제조업8%24%45%13%10%