Overview

Dataset statistics

Number of variables8
Number of observations58
Missing cells139
Missing cells (%)30.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory66.3 B

Variable types

Categorical4
Text2
DateTime2

Dataset

Description1. 국가별 운전면허 상호인정 현황 목록 조회: 한글 국가명 또는 ISO국가코드(다.참고 1 ISO국가코드 이용)를 이용하여 국가별 운전면허 상호인정 현황 목록 조회
Author외교부
URLhttps://www.data.go.kr/data/15099236/fileData.do

Alerts

개정일 has constant value ""Constant
비고 has constant value ""Constant
iso 2자리코드 is highly overall correlated with 국가명 and 2 other fieldsHigh correlation
국가명 is highly overall correlated with 국가영문명 and 2 other fieldsHigh correlation
국가영문명 is highly overall correlated with 국가명 and 2 other fieldsHigh correlation
상호인정성격 is highly overall correlated with 국가명 and 2 other fieldsHigh correlation
주명 has 25 (43.1%) missing valuesMissing
개정일 has 57 (98.3%) missing valuesMissing
비고 has 57 (98.3%) missing valuesMissing

Reproduction

Analysis started2023-12-12 01:59:48.481999
Analysis finished2023-12-12 01:59:49.390427
Duration0.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

국가명
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)46.6%
Missing0
Missing (%)0.0%
Memory size596.0 B
미국
23 
캐나다
10 
에콰도르
 
1
라트비아
 
1
리투아니아
 
1
Other values (22)
22 

Length

Max length6
Median length5
Mean length2.9655172
Min length2

Unique

Unique25 ?
Unique (%)43.1%

Sample

1st row과테말라
2nd row니카라과
3rd row라트비아
4th row리투아니아
5th row모잠비크

Common Values

ValueCountFrequency (%)
미국 23
39.7%
캐나다 10
17.2%
에콰도르 1
 
1.7%
라트비아 1
 
1.7%
리투아니아 1
 
1.7%
모잠비크 1
 
1.7%
벨기에 1
 
1.7%
불가리아 1
 
1.7%
스페인 1
 
1.7%
슬로바키아 1
 
1.7%
Other values (17) 17
29.3%

Length

2023-12-12T10:59:49.486285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미국 23
39.7%
캐나다 10
17.2%
우즈베키스탄 1
 
1.7%
과테말라 1
 
1.7%
온두라스 1
 
1.7%
피지 1
 
1.7%
폴란드 1
 
1.7%
페루 1
 
1.7%
키르기즈 1
 
1.7%
콜롬비아 1
 
1.7%
Other values (17) 17
29.3%

국가영문명
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)46.6%
Missing0
Missing (%)0.0%
Memory size596.0 B
United States of America
23 
Canada
10 
Ecuador
 
1
Latvia
 
1
Lithuania
 
1
Other values (22)
22 

Length

Max length26
Median length24
Mean length13.913793
Min length4

Unique

Unique25 ?
Unique (%)43.1%

Sample

1st rowGuatemala
2nd rowNicaragua
3rd rowLatvia
4th rowLithuania
5th rowMazambique

Common Values

ValueCountFrequency (%)
United States of America 23
39.7%
Canada 10
17.2%
Ecuador 1
 
1.7%
Latvia 1
 
1.7%
Lithuania 1
 
1.7%
Mazambique 1
 
1.7%
Belgium 1
 
1.7%
Bulgaria 1
 
1.7%
Spain 1
 
1.7%
Slovakia 1
 
1.7%
Other values (17) 17
29.3%

Length

2023-12-12T10:59:49.651741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united 24
18.3%
of 23
17.6%
america 23
17.6%
states 23
17.6%
canada 10
7.6%
colombia 1
 
0.8%
iran 1
 
0.8%
israel 1
 
0.8%
italia 1
 
0.8%
chile 1
 
0.8%
Other values (23) 23
17.6%

iso 2자리코드
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)46.6%
Missing0
Missing (%)0.0%
Memory size596.0 B
US
23 
CA
10 
EC
 
1
LV
 
1
LT
 
1
Other values (22)
22 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique25 ?
Unique (%)43.1%

Sample

1st rowGT
2nd rowNI
3rd rowLV
4th rowLT
5th rowMZ

Common Values

ValueCountFrequency (%)
US 23
39.7%
CA 10
17.2%
EC 1
 
1.7%
LV 1
 
1.7%
LT 1
 
1.7%
MZ 1
 
1.7%
BE 1
 
1.7%
BG 1
 
1.7%
ES 1
 
1.7%
SK 1
 
1.7%
Other values (17) 17
29.3%

Length

2023-12-12T10:59:49.794546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
us 23
39.7%
ca 10
17.2%
uz 1
 
1.7%
gt 1
 
1.7%
hn 1
 
1.7%
fj 1
 
1.7%
pl 1
 
1.7%
pe 1
 
1.7%
kg 1
 
1.7%
co 1
 
1.7%
Other values (17) 17
29.3%

주명
Text

MISSING 

Distinct33
Distinct (%)100.0%
Missing25
Missing (%)43.1%
Memory size596.0 B
2023-12-12T10:59:50.039811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length5.6060606
Min length3

Characters and Unicode

Total characters185
Distinct characters84
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)100.0%

Sample

1st row메릴랜드주
2nd row버지니아주
3rd row워싱턴주
4th row매사추세츠주
5th row텍사스주
ValueCountFrequency (%)
아이다호주 1
 
3.0%
하와이주 1
 
3.0%
펜실베니아주 1
 
3.0%
위스콘신주 1
 
3.0%
오클라호마주 1
 
3.0%
애리조나주 1
 
3.0%
루이지애나주 1
 
3.0%
온타리오주 1
 
3.0%
퀘벡주 1
 
3.0%
메릴랜드주 1
 
3.0%
Other values (23) 23
69.7%
2023-12-12T10:59:50.455379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 33
 
17.8%
아 10
 
5.4%
스 7
 
3.8%
라 6
 
3.2%
드 5
 
2.7%
이 5
 
2.7%
리 5
 
2.7%
시 4
 
2.2%
지 4
 
2.2%
니 4
 
2.2%
Other values (74) 102
55.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 185
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 33
 
17.8%
아 10
 
5.4%
스 7
 
3.8%
라 6
 
3.2%
드 5
 
2.7%
이 5
 
2.7%
리 5
 
2.7%
시 4
 
2.2%
지 4
 
2.2%
니 4
 
2.2%
Other values (74) 102
55.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 185
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 33
 
17.8%
아 10
 
5.4%
스 7
 
3.8%
라 6
 
3.2%
드 5
 
2.7%
이 5
 
2.7%
리 5
 
2.7%
시 4
 
2.2%
지 4
 
2.2%
니 4
 
2.2%
Other values (74) 102
55.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 185
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 33
 
17.8%
아 10
 
5.4%
스 7
 
3.8%
라 6
 
3.2%
드 5
 
2.7%
이 5
 
2.7%
리 5
 
2.7%
시 4
 
2.2%
지 4
 
2.2%
니 4
 
2.2%
Other values (74) 102
55.1%

체결일
Date

Distinct57
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size596.0 B
Minimum1990-01-08 00:00:00
Maximum2019-11-12 00:00:00
2023-12-12T10:59:50.671215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:59:50.853044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

개정일
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing57
Missing (%)98.3%
Memory size596.0 B
Minimum2017-07-27 00:00:00
Maximum2017-07-27 00:00:00
2023-12-12T10:59:51.000118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:59:51.118322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

상호인정성격
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size596.0 B
약정
37 
협정
21 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
약정 37
63.8%
협정 21
36.2%

Length

2023-12-12T10:59:51.266417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:59:51.377275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
약정 37
63.8%
협정 21
36.2%

비고
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing57
Missing (%)98.3%
Memory size596.0 B
2023-12-12T10:59:51.500745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters15
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row미발효이지만 상호주의로 인정
ValueCountFrequency (%)
미발효이지만 1
33.3%
상호주의로 1
33.3%
인정 1
33.3%
2023-12-12T10:59:51.812010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2
13.3%
미 1
 
6.7%
발 1
 
6.7%
효 1
 
6.7%
이 1
 
6.7%
지 1
 
6.7%
만 1
 
6.7%
상 1
 
6.7%
호 1
 
6.7%
주 1
 
6.7%
Other values (4) 4
26.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13
86.7%
Space Separator 2
 
13.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
미 1
 
7.7%
발 1
 
7.7%
효 1
 
7.7%
이 1
 
7.7%
지 1
 
7.7%
만 1
 
7.7%
상 1
 
7.7%
호 1
 
7.7%
주 1
 
7.7%
의 1
 
7.7%
Other values (3) 3
23.1%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13
86.7%
Common 2
 
13.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
미 1
 
7.7%
발 1
 
7.7%
효 1
 
7.7%
이 1
 
7.7%
지 1
 
7.7%
만 1
 
7.7%
상 1
 
7.7%
호 1
 
7.7%
주 1
 
7.7%
의 1
 
7.7%
Other values (3) 3
23.1%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13
86.7%
ASCII 2
 
13.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2
100.0%
Hangul
ValueCountFrequency (%)
미 1
 
7.7%
발 1
 
7.7%
효 1
 
7.7%
이 1
 
7.7%
지 1
 
7.7%
만 1
 
7.7%
상 1
 
7.7%
호 1
 
7.7%
주 1
 
7.7%
의 1
 
7.7%
Other values (3) 3
23.1%

Correlations

2023-12-12T10:59:51.948718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국가명국가영문명iso 2자리코드주명체결일상호인정성격
국가명1.0001.0001.0001.0000.9751.000
국가영문명1.0001.0001.0001.0000.9751.000
iso 2자리코드1.0001.0001.0001.0000.9751.000
주명1.0001.0001.0001.0001.000NaN
체결일0.9750.9750.9751.0001.0001.000
상호인정성격1.0001.0001.000NaN1.0001.000
2023-12-12T10:59:52.089682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
iso 2자리코드국가명상호인정성격국가영문명
iso 2자리코드1.0001.0000.7441.000
국가명1.0001.0000.7441.000
상호인정성격0.7440.7441.0000.744
국가영문명1.0001.0000.7441.000
2023-12-12T10:59:52.192754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국가명국가영문명iso 2자리코드상호인정성격
국가명1.0001.0001.0000.744
국가영문명1.0001.0001.0000.744
iso 2자리코드1.0001.0001.0000.744
상호인정성격0.7440.7440.7441.000

Missing values

2023-12-12T10:59:48.956495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:59:49.146629image/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.
2023-12-12T10:59:49.304197image/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

국가명국가영문명iso 2자리코드주명체결일개정일상호인정성격비고
0과테말라GuatemalaGT<NA>2015-05-14<NA>협정<NA>
1니카라과NicaraguaNI<NA>2016-11-04<NA>협정<NA>
2라트비아LatviaLV<NA>2011-05-28<NA>협정<NA>
3리투아니아LithuaniaLT<NA>2014-10-14<NA>협정<NA>
4모잠비크MazambiqueMZ<NA>2018-04-30<NA>협정<NA>
5미국United States of AmericaUS메릴랜드주2010-12-16<NA>약정<NA>
6미국United States of AmericaUS버지니아주2011-03-14<NA>약정<NA>
7미국United States of AmericaUS워싱턴주2011-05-24<NA>약정<NA>
8미국United States of AmericaUS매사추세츠주2011-08-08<NA>약정<NA>
9미국United States of AmericaUS텍사스주2011-09-09<NA>약정<NA>
국가명국가영문명iso 2자리코드주명체결일개정일상호인정성격비고
48캐나다CanadaCA프린스에드워드아일랜드주2007-11-14<NA>약정<NA>
49캐나다CanadaCA뉴펀들랜드라브라도주2007-11-15<NA>약정<NA>
50캐나다CanadaCA뉴브런즈윅주2008-02-12<NA>약정<NA>
51캐나다CanadaCA노바스코시아주2011-03-10<NA>약정<NA>
52콜롬비아ColombiaCO<NA>2018-08-01<NA>협정<NA>
53키르기즈KyrgyzKG<NA>2013-11-19<NA>협정<NA>
54페루PeruPE<NA>2012-12-17<NA>협정<NA>
55폴란드PolandPL<NA>1998-11-25<NA>협정<NA>
56피지FijiFJ<NA>2013-05-23<NA>협정미발효이지만 상호주의로 인정
57헝가리HungaryHU<NA>2014-11-28<NA>협정<NA>