Overview

Dataset statistics

Number of variables4
Number of observations73
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory33.8 B

Variable types

Categorical2
Text2

Dataset

Description국가별 조폐기관 현황을 아시아지역, 유럽지역, 미주지역으로 나눠 표기하였습니다. 국가별로 조폐기관의 기관명, 기관형태, 주요생산 제품을 알 수 있습니다.
Author한국조폐공사
URLhttps://www.data.go.kr/data/15106313/fileData.do

Alerts

구 분 is highly overall correlated with 기관형태High correlation
기관형태 is highly overall correlated with 구 분High correlation

Reproduction

Analysis started2023-12-12 21:33:40.594514
Analysis finished2023-12-12 21:33:40.927064
Duration0.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구 분
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Memory size716.0 B
독 일
9 
프 랑 스
7 
스 페 인
5 
인 도
5 
일 본
 
4
Other values (16)
43 

Length

Max length6
Median length5
Mean length4.8630137
Min length3

Unique

Unique4 ?
Unique (%)5.5%

Sample

1st row일 본
2nd row일 본
3rd row일 본
4th row일 본
5th row중 국

Common Values

ValueCountFrequency (%)
독 일 9
 
12.3%
프 랑 스 7
 
9.6%
스 페 인 5
 
6.8%
인 도 5
 
6.8%
일 본 4
 
5.5%
미 국 4
 
5.5%
러 시 아 4
 
5.5%
이탈리아 4
 
5.5%
인 도네시아 4
 
5.5%
호 주 4
 
5.5%
Other values (11) 23
31.5%

Length

2023-12-13T06:33:40.999281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
인 14
 
8.6%
일 13
 
8.0%
스 12
 
7.4%
국 10
 
6.1%
독 9
 
5.5%
프 7
 
4.3%
랑 7
 
4.3%
시 5
 
3.1%
도 5
 
3.1%
페 5
 
3.1%
Other values (27) 76
46.6%

기 관 명
Text

Distinct44
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Memory size716.0 B
2023-12-13T06:33:41.270567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length140
Median length51
Mean length37.90411
Min length4

Characters and Unicode

Total characters2767
Distinct characters62
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)39.7%

Sample

1st rowNational Printing Bureau
2nd rowNational Printing Bureau
3rd rowJapan Mint
4th rowToppan
5th rowCBPM(China Banknote Printing & Minting Corporation)
ValueCountFrequency (%)
printing 13
 
4.6%
de 12
 
4.2%
mint 10
 
3.5%
9
 
3.2%
minting 7
 
2.5%
corporation 7
 
2.5%
of 6
 
2.1%
la 5
 
1.8%
royal 5
 
1.8%
note 5
 
1.8%
Other values (96) 204
72.1%
2023-12-13T06:33:41.726603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1135
41.0%
a 157
 
5.7%
n 150
 
5.4%
i 143
 
5.2%
e 116
 
4.2%
o 99
 
3.6%
t 97
 
3.5%
r 91
 
3.3%
d 52
 
1.9%
l 46
 
1.7%
Other values (52) 681
24.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1229
44.4%
Space Separator 1135
41.0%
Uppercase Letter 327
 
11.8%
Open Punctuation 23
 
0.8%
Close Punctuation 23
 
0.8%
Other Punctuation 13
 
0.5%
Other Letter 7
 
0.3%
Math Symbol 6
 
0.2%
Dash Punctuation 2
 
0.1%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 157
12.8%
n 150
12.2%
i 143
11.6%
e 116
9.4%
o 99
8.1%
t 97
7.9%
r 91
 
7.4%
d 52
 
4.2%
l 46
 
3.7%
u 43
 
3.5%
Other values (15) 235
19.1%
Uppercase Letter
ValueCountFrequency (%)
P 44
13.5%
M 41
12.5%
B 34
10.4%
C 28
 
8.6%
S 22
 
6.7%
G 18
 
5.5%
I 18
 
5.5%
N 17
 
5.2%
R 15
 
4.6%
L 15
 
4.6%
Other values (11) 75
22.9%
Other Letter
ValueCountFrequency (%)
방 1
14.3%
국 1
14.3%
폐 1
14.3%
조 1
14.3%
연 1
14.3%
개 1
14.3%
등 1
14.3%
Other Punctuation
ValueCountFrequency (%)
. 8
61.5%
& 5
38.5%
Space Separator
ValueCountFrequency (%)
1135
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Math Symbol
ValueCountFrequency (%)
+ 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Decimal Number
ValueCountFrequency (%)
4 1
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1556
56.2%
Common 1204
43.5%
Hangul 7
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 157
 
10.1%
n 150
 
9.6%
i 143
 
9.2%
e 116
 
7.5%
o 99
 
6.4%
t 97
 
6.2%
r 91
 
5.8%
d 52
 
3.3%
l 46
 
3.0%
P 44
 
2.8%
Other values (36) 561
36.1%
Common
ValueCountFrequency (%)
1135
94.3%
( 23
 
1.9%
) 23
 
1.9%
. 8
 
0.7%
+ 6
 
0.5%
& 5
 
0.4%
- 2
 
0.2%
4 1
 
0.1%
’ 1
 
0.1%
Hangul
ValueCountFrequency (%)
방 1
14.3%
국 1
14.3%
폐 1
14.3%
조 1
14.3%
연 1
14.3%
개 1
14.3%
등 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2759
99.7%
Hangul 7
 
0.3%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1135
41.1%
a 157
 
5.7%
n 150
 
5.4%
i 143
 
5.2%
e 116
 
4.2%
o 99
 
3.6%
t 97
 
3.5%
r 91
 
3.3%
d 52
 
1.9%
l 46
 
1.7%
Other values (44) 673
24.4%
Hangul
ValueCountFrequency (%)
방 1
14.3%
국 1
14.3%
폐 1
14.3%
조 1
14.3%
연 1
14.3%
개 1
14.3%
등 1
14.3%
Punctuation
ValueCountFrequency (%)
’ 1
100.0%

기관형태
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size716.0 B
공공기관
44 
민영기업
20 
중앙은행
5 
중앙은행
 
3
중앙은행 자회사
 
1

Length

Max length13
Median length4
Mean length4.1643836
Min length4

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st row공공기관
2nd row공공기관
3rd row공공기관
4th row민영기업
5th row공공기관

Common Values

ValueCountFrequency (%)
공공기관 44
60.3%
민영기업 20
27.4%
중앙은행 5
 
6.8%
중앙은행 3
 
4.1%
중앙은행 자회사 1
 
1.4%

Length

2023-12-13T06:33:41.893264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:33:42.034898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공기관 44
59.5%
민영기업 20
27.0%
중앙은행 9
 
12.2%
자회사 1
 
1.4%

주요 생산 제품
Text

Distinct38
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Memory size716.0 B
2023-12-13T06:33:42.256853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length27
Mean length13.219178
Min length5

Characters and Unicode

Total characters965
Distinct characters105
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)39.7%

Sample

1st row - 은행권, 은행권용지, 보안용지
2nd row - 여권
3rd row - 주화, 메달
4th row - 보안인쇄, 보안필름, 홀로그램
5th row - 은행권, 은행권용지, 보안용지
ValueCountFrequency (%)
73
29.4%
은행권 21
 
8.5%
메달 20
 
8.1%
주화 19
 
7.7%
여권 15
 
6.0%
은행권용지 12
 
4.8%
보안용지 9
 
3.6%
신분증 8
 
3.2%
불리온 6
 
2.4%
솔루션 5
 
2.0%
Other values (43) 60
24.2%
2023-12-13T06:33:42.640013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
250
25.9%
, 83
 
8.6%
- 73
 
7.6%
권 52
 
5.4%
행 36
 
3.7%
은 35
 
3.6%
지 27
 
2.8%
주 26
 
2.7%
용 23
 
2.4%
화 22
 
2.3%
Other values (95) 338
35.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 533
55.2%
Space Separator 250
25.9%
Other Punctuation 83
 
8.6%
Dash Punctuation 73
 
7.6%
Lowercase Letter 12
 
1.2%
Uppercase Letter 10
 
1.0%
Close Punctuation 2
 
0.2%
Open Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
권 52
 
9.8%
행 36
 
6.8%
은 35
 
6.6%
지 27
 
5.1%
주 26
 
4.9%
용 23
 
4.3%
화 22
 
4.1%
보 22
 
4.1%
메 20
 
3.8%
달 20
 
3.8%
Other values (77) 250
46.9%
Lowercase Letter
ValueCountFrequency (%)
e 3
25.0%
n 2
16.7%
a 2
16.7%
q 1
 
8.3%
u 1
 
8.3%
d 1
 
8.3%
c 1
 
8.3%
r 1
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
I 4
40.0%
D 3
30.0%
B 1
 
10.0%
T 1
 
10.0%
F 1
 
10.0%
Space Separator
ValueCountFrequency (%)
250
100.0%
Other Punctuation
ValueCountFrequency (%)
, 83
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 73
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 533
55.2%
Common 410
42.5%
Latin 22
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
권 52
 
9.8%
행 36
 
6.8%
은 35
 
6.6%
지 27
 
5.1%
주 26
 
4.9%
용 23
 
4.3%
화 22
 
4.1%
보 22
 
4.1%
메 20
 
3.8%
달 20
 
3.8%
Other values (77) 250
46.9%
Latin
ValueCountFrequency (%)
I 4
18.2%
D 3
13.6%
e 3
13.6%
n 2
9.1%
a 2
9.1%
B 1
 
4.5%
T 1
 
4.5%
q 1
 
4.5%
u 1
 
4.5%
d 1
 
4.5%
Other values (3) 3
13.6%
Common
ValueCountFrequency (%)
250
61.0%
, 83
 
20.2%
- 73
 
17.8%
) 2
 
0.5%
( 2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 533
55.2%
ASCII 432
44.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
250
57.9%
, 83
 
19.2%
- 73
 
16.9%
I 4
 
0.9%
D 3
 
0.7%
e 3
 
0.7%
) 2
 
0.5%
n 2
 
0.5%
a 2
 
0.5%
( 2
 
0.5%
Other values (8) 8
 
1.9%
Hangul
ValueCountFrequency (%)
권 52
 
9.8%
행 36
 
6.8%
은 35
 
6.6%
지 27
 
5.1%
주 26
 
4.9%
용 23
 
4.3%
화 22
 
4.1%
보 22
 
4.1%
메 20
 
3.8%
달 20
 
3.8%
Other values (77) 250
46.9%

Correlations

2023-12-13T06:33:42.752470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구 분기 관 명기관형태주요 생산 제품
구 분1.0001.0000.8640.000
기 관 명1.0001.0001.0000.000
기관형태0.8641.0001.0000.000
주요 생산 제품0.0000.0000.0001.000
2023-12-13T06:33:42.847227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구 분기관형태
구 분1.0000.562
기관형태0.5621.000
2023-12-13T06:33:42.941965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구 분기관형태
구 분1.0000.562
기관형태0.5621.000

Missing values

2023-12-13T06:33:40.816148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:33:40.896791image/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

구 분기 관 명기관형태주요 생산 제품
0일 본National Printing Bureau공공기관- 은행권, 은행권용지, 보안용지
1일 본National Printing Bureau공공기관- 여권
2일 본Japan Mint공공기관- 주화, 메달
3일 본Toppan민영기업- 보안인쇄, 보안필름, 홀로그램
4중 국CBPM(China Banknote Printing & Minting Corporation)공공기관- 은행권, 은행권용지, 보안용지
5중 국CBPM(China Banknote Printing & Minting Corporation)공공기관- 주화, 메달, 불리온
6중 국CBPM(China Banknote Printing & Minting Corporation)공공기관- 여권, ID
7싱가폴Singapore Mint민영기업- 주화, 메달
8호 주Note Printing Australia공공기관- 은행권
9호 주Note Printing Australia공공기관- 여권
구 분기 관 명기관형태주요 생산 제품
63캐 나 다Royal Canadian Mint공공기관- 주화, 메달, 불리온
64브 라 질Casa da Moeda do Brasil공공기관- 은행권, 은행권용지, 보안용지
65브 라 질Casa da Moeda do Brasil공공기관- 주화, 메달
66브 라 질Casa da Moeda do Brasil공공기관- 신분증, 여권
67콜롬비아Banco de la Republica - Banknote Printing work중앙은행- 은행권
68콜롬비아Banco de la Republica - La Casa de Moneda중앙은행- 주화, 메달
69필 리 핀Bangko Sentral ng Pilipinas중앙은행- 은행권, 은행권용지
70필 리 핀Bangko Sentral ng Pilipinas중앙은행- 주화
71필 리 핀Bangko Sentral ng Pilipinas중앙은행- 신분증, 여권
72멕 시 코Banco de Mexico중앙은행- 은행권