Overview

Dataset statistics

Number of variables3
Number of observations60
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory27.2 B

Variable types

Numeric1
Text2

Dataset

Description우리나라 주요 현행법령명을 영문으로 번역하여 제공하는 대한민국 영문법령 웹사이트에서 법령을 제공할때 분야별로 검색이 가능하도록 코드화 하고, 해당 분야를 영문으로 표기한 자료
Author한국법제연구원
URLhttps://www.data.go.kr/data/15087144/fileData.do

Alerts

분야연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 19:43:12.594120
Analysis finished2023-12-12 19:43:12.989968
Duration0.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분야연번
Real number (ℝ)

UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.5
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T04:43:13.068532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.95
Q115.75
median30.5
Q345.25
95-th percentile57.05
Maximum60
Range59
Interquartile range (IQR)29.5

Descriptive statistics

Standard deviation17.464249
Coefficient of variation (CV)0.57259833
Kurtosis-1.2
Mean30.5
Median Absolute Deviation (MAD)15
Skewness0
Sum1830
Variance305
MonotonicityStrictly increasing
2023-12-13T04:43:13.240778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.7%
32 1
 
1.7%
34 1
 
1.7%
35 1
 
1.7%
36 1
 
1.7%
37 1
 
1.7%
38 1
 
1.7%
39 1
 
1.7%
40 1
 
1.7%
41 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
1 1
1.7%
2 1
1.7%
3 1
1.7%
4 1
1.7%
5 1
1.7%
6 1
1.7%
7 1
1.7%
8 1
1.7%
9 1
1.7%
10 1
1.7%
ValueCountFrequency (%)
60 1
1.7%
59 1
1.7%
58 1
1.7%
57 1
1.7%
56 1
1.7%
55 1
1.7%
54 1
1.7%
53 1
1.7%
52 1
1.7%
51 1
1.7%

국문분야명
Text

Distinct56
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size612.0 B
2023-12-13T04:43:13.430709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length23
Mean length6.6
Min length2

Characters and Unicode

Total characters396
Distinct characters95
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)86.7%

Sample

1st row헌법
2nd row국회
3rd row선거ㆍ정당
4th row행정일반
5th row국가공무원
ValueCountFrequency (%)
정보통신 2
 
3.3%
형사법 2
 
3.3%
외무 2
 
3.3%
민사법 2
 
3.3%
농업/축산/산림/수산/담배·인삼 1
 
1.7%
경찰/민방위·소방/군사/병무/국가보훈 1
 
1.7%
헌법 1
 
1.7%
전기ㆍ가스 1
 
1.7%
국토개발·도시 1
 
1.7%
주택·건축·도로 1
 
1.7%
Other values (46) 46
76.7%
2023-12-13T04:43:13.755490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
· 32
 
8.1%
/ 28
 
7.1%
국 12
 
3.0%
사 12
 
3.0%
공 12
 
3.0%
ㆍ 12
 
3.0%
업 11
 
2.8%
법 11
 
2.8%
무 10
 
2.5%
지 8
 
2.0%
Other values (85) 248
62.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 336
84.8%
Other Punctuation 60
 
15.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 12
 
3.6%
사 12
 
3.6%
공 12
 
3.6%
ㆍ 12
 
3.6%
업 11
 
3.3%
법 11
 
3.3%
무 10
 
3.0%
지 8
 
2.4%
보 8
 
2.4%
정 7
 
2.1%
Other values (83) 233
69.3%
Other Punctuation
ValueCountFrequency (%)
· 32
53.3%
/ 28
46.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 336
84.8%
Common 60
 
15.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 12
 
3.6%
사 12
 
3.6%
공 12
 
3.6%
ㆍ 12
 
3.6%
업 11
 
3.3%
법 11
 
3.3%
무 10
 
3.0%
지 8
 
2.4%
보 8
 
2.4%
정 7
 
2.1%
Other values (83) 233
69.3%
Common
ValueCountFrequency (%)
· 32
53.3%
/ 28
46.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 324
81.8%
None 32
 
8.1%
ASCII 28
 
7.1%
Compat Jamo 12
 
3.0%

Most frequent character per block

None
ValueCountFrequency (%)
· 32
100.0%
ASCII
ValueCountFrequency (%)
/ 28
100.0%
Hangul
ValueCountFrequency (%)
국 12
 
3.7%
사 12
 
3.7%
공 12
 
3.7%
업 11
 
3.4%
법 11
 
3.4%
무 10
 
3.1%
지 8
 
2.5%
보 8
 
2.5%
정 7
 
2.2%
경 7
 
2.2%
Other values (82) 226
69.8%
Compat Jamo
ValueCountFrequency (%)
ㆍ 12
100.0%

영문분야명
Text

Distinct56
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size612.0 B
2023-12-13T04:43:13.960371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length142
Median length37
Mean length30.7
Min length5

Characters and Unicode

Total characters1842
Distinct characters48
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)86.7%

Sample

1st rowConstitution
2nd rowNational Assembly
3rd rowElection and Political Party
4th rowAdministration in General
5th rowPublic Official
ValueCountFrequency (%)
and 40
 
19.2%
affairs 13
 
6.2%
transportation 4
 
1.9%
land 4
 
1.9%
civil 3
 
1.4%
agriculture 3
 
1.4%
public 3
 
1.4%
information 2
 
1.0%
industrial 2
 
1.0%
national 2
 
1.0%
Other values (98) 132
63.5%
2023-12-13T04:43:14.310501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 166
 
9.0%
i 162
 
8.8%
n 160
 
8.7%
148
 
8.0%
r 128
 
6.9%
e 114
 
6.2%
t 106
 
5.8%
o 102
 
5.5%
d 72
 
3.9%
s 72
 
3.9%
Other values (38) 612
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1462
79.4%
Uppercase Letter 194
 
10.5%
Space Separator 148
 
8.0%
Other Punctuation 38
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 166
11.4%
i 162
11.1%
n 160
10.9%
r 128
8.8%
e 114
 
7.8%
t 106
 
7.3%
o 102
 
7.0%
d 72
 
4.9%
s 72
 
4.9%
c 70
 
4.8%
Other values (14) 310
21.2%
Uppercase Letter
ValueCountFrequency (%)
A 30
15.5%
C 24
12.4%
T 22
11.3%
P 16
 
8.2%
E 12
 
6.2%
L 10
 
5.2%
F 10
 
5.2%
M 10
 
5.2%
R 8
 
4.1%
S 8
 
4.1%
Other values (11) 44
22.7%
Other Punctuation
ValueCountFrequency (%)
/ 28
73.7%
, 10
 
26.3%
Space Separator
ValueCountFrequency (%)
148
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1656
89.9%
Common 186
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 166
 
10.0%
i 162
 
9.8%
n 160
 
9.7%
r 128
 
7.7%
e 114
 
6.9%
t 106
 
6.4%
o 102
 
6.2%
d 72
 
4.3%
s 72
 
4.3%
c 70
 
4.2%
Other values (35) 504
30.4%
Common
ValueCountFrequency (%)
148
79.6%
/ 28
 
15.1%
, 10
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 166
 
9.0%
i 162
 
8.8%
n 160
 
8.7%
148
 
8.0%
r 128
 
6.9%
e 114
 
6.2%
t 106
 
5.8%
o 102
 
5.5%
d 72
 
3.9%
s 72
 
3.9%
Other values (38) 612
33.2%

Interactions

2023-12-13T04:43:12.765963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:43:14.402632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분야연번국문분야명영문분야명
분야연번1.0000.7170.717
국문분야명0.7171.0001.000
영문분야명0.7171.0001.000

Missing values

2023-12-13T04:43:12.875941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:43:12.957546image/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

분야연번국문분야명영문분야명
01헌법Constitution
12국회National Assembly
23선거ㆍ정당Election and Political Party
34행정일반Administration in General
45국가공무원Public Official
56법원Court
67법무Judicial Affairs
78민사법Civil Affairs
89형사법Crimes and Criminal Procedure
910지방제도Local Government
분야연번국문분야명영문분야명
5051법원/법무Court/Judicial Affairs
5152정보통신Information and Telecommunication
5253형사법Crimes and Criminal Procedure
5354환경/노동Environment/Labor
5455보건·의사/약사/사회복지Health and Medical Affairs/Pharmaceutical Affairs/Social Welfare
5556국토개발·도시/주택·건축·도로/수자원·토지·건설Territorial Development and Town/Housing, Agriculture and Road/Water Resources, Land and Construction
5657외무Foreign Affairs
5758농업/축산/산림/수산/담배·인삼Agriculture/Livestock/Forest/Fishery/Tobacco and Ginseng
5859경찰/민방위·소방/군사/병무/국가보훈Commercial Affairs/Civil Defence and Firefighting/Military Affairs/Conscription Affairs/Patriots and Veterans Affairs
5960육운·항공·관광/해운Land Transportation, Aviation and Tourism/Marine Transportation