Overview

Dataset statistics

Number of variables7
Number of observations44
Missing cells90
Missing cells (%)29.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory59.0 B

Variable types

Categorical2
Text5

Dataset

Description한국무역보험공사의 17개국 주요 업종별 최근 5년간(2017년~2021년)의 평균 부도율(퍼센티지) 데이터입니다.
Author한국무역보험공사
URLhttps://www.data.go.kr/data/15104440/fileData.do

Alerts

2017 has 18 (40.9%) missing valuesMissing
2018 has 25 (56.8%) missing valuesMissing
2019 has 25 (56.8%) missing valuesMissing
2020 has 22 (50.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 15:45:12.388187
Analysis finished2023-12-12 15:45:13.280893
Duration0.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

국가
Categorical

Distinct17
Distinct (%)38.6%
Missing0
Missing (%)0.0%
Memory size484.0 B
중국
11 
미국
9 
인도
6 
이탈리아
3 
아랍에미리트 연합
2 
Other values (12)
13 

Length

Max length9
Median length2
Mean length2.7045455
Min length2

Unique

Unique11 ?
Unique (%)25.0%

Sample

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

Common Values

ValueCountFrequency (%)
중국 11
25.0%
미국 9
20.5%
인도 6
13.6%
이탈리아 3
 
6.8%
아랍에미리트 연합 2
 
4.5%
싱가포르 2
 
4.5%
베트남 1
 
2.3%
러시아 1
 
2.3%
페루 1
 
2.3%
칠레 1
 
2.3%
Other values (7) 7
15.9%

Length

2023-12-13T00:45:13.391798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중국 11
23.9%
미국 9
19.6%
인도 6
13.0%
이탈리아 3
 
6.5%
아랍에미리트 2
 
4.3%
연합 2
 
4.3%
싱가포르 2
 
4.3%
호주 1
 
2.2%
콜롬비아 1
 
2.2%
일본 1
 
2.2%
Other values (8) 8
17.4%

업종명
Text

Distinct26
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Memory size484.0 B
2023-12-13T00:45:13.738979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length17.363636
Min length7

Characters and Unicode

Total characters764
Distinct characters89
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)45.5%

Sample

1st row전기용 기계 장비 및 관련 기자재 도매업
2nd row액정 표시장치 제조업
3rd row그 외 기타 전자 부품 제조업
4th row플라스틱 물질 및 합성고무 도매업
5th row축전지 제조업
ValueCountFrequency (%)
기타 25
 
10.4%
및 25
 
10.4%
도매업 20
 
8.3%
제조업 15
 
6.2%
부품 9
 
3.7%
기계 9
 
3.7%
장비 9
 
3.7%
신품 8
 
3.3%
그 8
 
3.3%
외 8
 
3.3%
Other values (49) 105
43.6%
2023-12-13T00:45:14.251596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
199
26.0%
기 46
 
6.0%
업 46
 
6.0%
품 39
 
5.1%
제 28
 
3.7%
매 27
 
3.5%
및 25
 
3.3%
타 25
 
3.3%
도 21
 
2.7%
장 18
 
2.4%
Other values (79) 290
38.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 564
73.8%
Space Separator 199
 
26.0%
Decimal Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 46
 
8.2%
업 46
 
8.2%
품 39
 
6.9%
제 28
 
5.0%
매 27
 
4.8%
및 25
 
4.4%
타 25
 
4.4%
도 21
 
3.7%
장 18
 
3.2%
자 16
 
2.8%
Other values (77) 273
48.4%
Space Separator
ValueCountFrequency (%)
199
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 564
73.8%
Common 200
 
26.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 46
 
8.2%
업 46
 
8.2%
품 39
 
6.9%
제 28
 
5.0%
매 27
 
4.8%
및 25
 
4.4%
타 25
 
4.4%
도 21
 
3.7%
장 18
 
3.2%
자 16
 
2.8%
Other values (77) 273
48.4%
Common
ValueCountFrequency (%)
199
99.5%
1 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 564
73.8%
ASCII 200
 
26.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
199
99.5%
1 1
 
0.5%
Hangul
ValueCountFrequency (%)
기 46
 
8.2%
업 46
 
8.2%
품 39
 
6.9%
제 28
 
5.0%
매 27
 
4.8%
및 25
 
4.4%
타 25
 
4.4%
도 21
 
3.7%
장 18
 
3.2%
자 16
 
2.8%
Other values (77) 273
48.4%

2017
Text

MISSING 

Distinct15
Distinct (%)57.7%
Missing18
Missing (%)40.9%
Memory size484.0 B
2023-12-13T00:45:14.465764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4
Min length1

Characters and Unicode

Total characters104
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)38.5%

Sample

1st row1.90%
2nd row0.90%
3rd row4.20%
4th row4.20%
5th row
ValueCountFrequency (%)
7.70 3
15.8%
4.20 2
10.5%
4.50 2
10.5%
9.10 2
10.5%
1.90 1
 
5.3%
0.90 1
 
5.3%
3.60 1
 
5.3%
4.30 1
 
5.3%
12.50 1
 
5.3%
6.70 1
 
5.3%
Other values (4) 4
21.1%
2023-12-13T00:45:14.865084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22
21.2%
. 19
18.3%
% 19
18.3%
7
 
6.7%
7 7
 
6.7%
4 5
 
4.8%
5 5
 
4.8%
1 5
 
4.8%
9 4
 
3.8%
6 4
 
3.8%
Other values (3) 7
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 59
56.7%
Other Punctuation 38
36.5%
Space Separator 7
 
6.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22
37.3%
7 7
 
11.9%
4 5
 
8.5%
5 5
 
8.5%
1 5
 
8.5%
9 4
 
6.8%
6 4
 
6.8%
2 3
 
5.1%
3 3
 
5.1%
8 1
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 19
50.0%
% 19
50.0%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 104
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22
21.2%
. 19
18.3%
% 19
18.3%
7
 
6.7%
7 7
 
6.7%
4 5
 
4.8%
5 5
 
4.8%
1 5
 
4.8%
9 4
 
3.8%
6 4
 
3.8%
Other values (3) 7
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 104
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22
21.2%
. 19
18.3%
% 19
18.3%
7
 
6.7%
7 7
 
6.7%
4 5
 
4.8%
5 5
 
4.8%
1 5
 
4.8%
9 4
 
3.8%
6 4
 
3.8%
Other values (3) 7
 
6.7%

2018
Text

MISSING 

Distinct11
Distinct (%)57.9%
Missing25
Missing (%)56.8%
Memory size484.0 B
2023-12-13T00:45:15.097235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.6315789
Min length1

Characters and Unicode

Total characters69
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)42.1%

Sample

1st row3.80%
2nd row
3rd row
4th row7.10%
5th row
ValueCountFrequency (%)
8.30 2
16.7%
6.70 2
16.7%
3.80 1
8.3%
7.10 1
8.3%
4.30 1
8.3%
5.60 1
8.3%
5.90 1
8.3%
25.00 1
8.3%
4.20 1
8.3%
14.30 1
8.3%
2023-12-13T00:45:15.467644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13
18.8%
. 12
17.4%
% 12
17.4%
7
10.1%
3 5
 
7.2%
8 3
 
4.3%
6 3
 
4.3%
7 3
 
4.3%
4 3
 
4.3%
5 3
 
4.3%
Other values (3) 5
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38
55.1%
Other Punctuation 24
34.8%
Space Separator 7
 
10.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13
34.2%
3 5
 
13.2%
8 3
 
7.9%
6 3
 
7.9%
7 3
 
7.9%
4 3
 
7.9%
5 3
 
7.9%
1 2
 
5.3%
2 2
 
5.3%
9 1
 
2.6%
Other Punctuation
ValueCountFrequency (%)
. 12
50.0%
% 12
50.0%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13
18.8%
. 12
17.4%
% 12
17.4%
7
10.1%
3 5
 
7.2%
8 3
 
4.3%
6 3
 
4.3%
7 3
 
4.3%
4 3
 
4.3%
5 3
 
4.3%
Other values (3) 5
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13
18.8%
. 12
17.4%
% 12
17.4%
7
10.1%
3 5
 
7.2%
8 3
 
4.3%
6 3
 
4.3%
7 3
 
4.3%
4 3
 
4.3%
5 3
 
4.3%
Other values (3) 5
 
7.2%

2019
Text

MISSING 

Distinct13
Distinct (%)68.4%
Missing25
Missing (%)56.8%
Memory size484.0 B
2023-12-13T00:45:15.687275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.8947368
Min length1

Characters and Unicode

Total characters74
Distinct characters12
Distinct categories3 ?
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 (%)57.9%

Sample

1st row1.60%
2nd row3.70%
3rd row
4th row
5th row
ValueCountFrequency (%)
4.50 2
15.4%
1.60 1
7.7%
3.70 1
7.7%
8.30 1
7.7%
25.00 1
7.7%
13.30 1
7.7%
4.30 1
7.7%
5.30 1
7.7%
5.60 1
7.7%
7.10 1
7.7%
Other values (2) 2
15.4%
2023-12-13T00:45:16.085361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 17
23.0%
. 13
17.6%
% 13
17.6%
6
 
8.1%
3 6
 
8.1%
5 5
 
6.8%
4 3
 
4.1%
1 3
 
4.1%
6 2
 
2.7%
7 2
 
2.7%
Other values (2) 4
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 42
56.8%
Other Punctuation 26
35.1%
Space Separator 6
 
8.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17
40.5%
3 6
 
14.3%
5 5
 
11.9%
4 3
 
7.1%
1 3
 
7.1%
6 2
 
4.8%
7 2
 
4.8%
8 2
 
4.8%
2 2
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 13
50.0%
% 13
50.0%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 74
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17
23.0%
. 13
17.6%
% 13
17.6%
6
 
8.1%
3 6
 
8.1%
5 5
 
6.8%
4 3
 
4.1%
1 3
 
4.1%
6 2
 
2.7%
7 2
 
2.7%
Other values (2) 4
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17
23.0%
. 13
17.6%
% 13
17.6%
6
 
8.1%
3 6
 
8.1%
5 5
 
6.8%
4 3
 
4.1%
1 3
 
4.1%
6 2
 
2.7%
7 2
 
2.7%
Other values (2) 4
 
5.4%

2020
Text

MISSING 

Distinct11
Distinct (%)50.0%
Missing22
Missing (%)50.0%
Memory size484.0 B
2023-12-13T00:45:16.304620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length1
Mean length2.8636364
Min length1

Characters and Unicode

Total characters63
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)45.5%

Sample

1st row1.90%
2nd row2.00%
3rd row7.70%
4th row
5th row
ValueCountFrequency (%)
1.90 1
10.0%
2.00 1
10.0%
7.70 1
10.0%
5.90 1
10.0%
7.10 1
10.0%
5.60 1
10.0%
3.60 1
10.0%
6.30 1
10.0%
8.30 1
10.0%
19.00 1
10.0%
2023-12-13T00:45:16.727665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12
19.0%
0 12
19.0%
. 10
15.9%
% 10
15.9%
1 3
 
4.8%
9 3
 
4.8%
7 3
 
4.8%
6 3
 
4.8%
3 3
 
4.8%
5 2
 
3.2%
Other values (2) 2
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31
49.2%
Other Punctuation 20
31.7%
Space Separator 12
 
19.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12
38.7%
1 3
 
9.7%
9 3
 
9.7%
7 3
 
9.7%
6 3
 
9.7%
3 3
 
9.7%
5 2
 
6.5%
2 1
 
3.2%
8 1
 
3.2%
Other Punctuation
ValueCountFrequency (%)
. 10
50.0%
% 10
50.0%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 63
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
12
19.0%
0 12
19.0%
. 10
15.9%
% 10
15.9%
1 3
 
4.8%
9 3
 
4.8%
7 3
 
4.8%
6 3
 
4.8%
3 3
 
4.8%
5 2
 
3.2%
Other values (2) 2
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12
19.0%
0 12
19.0%
. 10
15.9%
% 10
15.9%
1 3
 
4.8%
9 3
 
4.8%
7 3
 
4.8%
6 3
 
4.8%
3 3
 
4.8%
5 2
 
3.2%
Other values (2) 2
 
3.2%

2021
Categorical

Distinct7
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Memory size484.0 B
<NA>
28 
10 
10.00%
 
2
5.30%
 
1
14.30%
 
1
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.5454545
Min length1

Unique

Unique4 ?
Unique (%)9.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 28
63.6%
10
 
22.7%
10.00% 2
 
4.5%
5.30% 1
 
2.3%
14.30% 1
 
2.3%
3.40% 1
 
2.3%
12.50% 1
 
2.3%

Length

2023-12-13T00:45:16.945182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:45:17.135631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 28
82.4%
10.00 2
 
5.9%
5.30 1
 
2.9%
14.30 1
 
2.9%
3.40 1
 
2.9%
12.50 1
 
2.9%

Correlations

2023-12-13T00:45:17.263252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국가업종명20172018201920202021
국가1.0000.0000.8700.0000.0000.0000.000
업종명0.0001.0000.0000.5610.9590.8940.804
20170.8700.0001.0000.8490.9130.8380.556
20180.0000.5610.8491.0000.8841.0000.564
20190.0000.9590.9130.8841.0001.0001.000
20200.0000.8940.8381.0001.0001.0001.000
20210.0000.8040.5560.5641.0001.0001.000
2023-12-13T00:45:17.402473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국가2021
국가1.0000.000
20210.0001.000
2023-12-13T00:45:17.518099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국가2021
국가1.0000.000
20210.0001.000

Missing values

2023-12-13T00:45:12.849467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:45:13.052647image/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.
2023-12-13T00:45:13.195725image/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

국가업종명20172018201920202021
0중국전기용 기계 장비 및 관련 기자재 도매업1.90%3.80%1.60%<NA><NA>
1중국액정 표시장치 제조업<NA><NA><NA>1.90%<NA>
2중국그 외 기타 전자 부품 제조업0.90%<NA><NA>2.00%<NA>
3중국플라스틱 물질 및 합성고무 도매업4.20%<NA><NA><NA><NA>
4중국축전지 제조업4.20%<NA>3.70%7.70%<NA>
5중국상품 종합 중개업<NA><NA><NA>5.30%
6중국그 외 기타 전기장비 제조업7.70%
7중국그 외 기타 플라스틱 제품 제조업7.70%
8중국그 외 기타 분류 안된 화학제품 제조업<NA><NA><NA><NA><NA>
9중국인쇄회로기판용 적층판 제조업<NA>7.10%
국가업종명20172018201920202021
34싱가포르전기용 기계 장비 및 관련 기자재 도매업5.30%<NA><NA><NA><NA>
35베트남기타 화학 물질 및 화학제품 도매업10.00%
36이탈리아그 외 기타 플라스틱 제품 제조업<NA><NA>8.00%<NA><NA>
37이탈리아기타 화학 물질 및 화학제품 도매업5.60%<NA><NA><NA><NA>
38이탈리아모직물 직조업<NA>6.70%<NA><NA>
39영국그 외 기타 플라스틱 제품 제조업9.10%
40칠레기타 자동차 신품 부품 및 내장품 판매업<NA>20.00%<NA>
41페루기타 자동차 신품 부품 및 내장품 판매업9.10%<NA><NA>
42러시아기타 자동차 신품 부품 및 내장품 판매업<NA><NA>4.50%<NA><NA>
43에콰도르기타 자동차 신품 부품 및 내장품 판매업6.80%<NA>4.50%19.00%12.50%