Overview

Dataset statistics

Number of variables12
Number of observations63
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 KiB
Average record size in memory105.1 B

Variable types

Categorical9
Text2
DateTime1

Dataset

Description- 전국의 궤도시설 현황을 알 수 있도록 정보제공, 세부내용으로 지역, 업체명, 위치, 준공일, 용도, 설비종류를 포함
URLhttps://www.data.go.kr/data/15104299/fileData.do

Alerts

노면전차 has constant value ""Constant
화물모노레일 has constant value ""Constant
자기부상열차 has constant value ""Constant
케이블철도 is highly overall correlated with 합계High correlation
모노레일 is highly overall correlated with 합계High correlation
합계 is highly overall correlated with 케이블철도 and 1 other fieldsHigh correlation
케이블철도 is highly imbalanced (75.2%)Imbalance
경전철 is highly imbalanced (88.2%)Imbalance
합계 is highly imbalanced (57.0%)Imbalance
업체명 has unique valuesUnique
준공일 has unique valuesUnique

Reproduction

Analysis started2023-12-12 00:29:58.332548
Analysis finished2023-12-12 00:29:59.107917
Duration0.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Categorical

Distinct13
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Memory size636.0 B
강원
10 
전남
8 
경남
8 
경북
7 
충북
6 
Other values (8)
24 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)1.6%

Sample

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

Common Values

ValueCountFrequency (%)
강원 10
15.9%
전남 8
12.7%
경남 8
12.7%
경북 7
11.1%
충북 6
9.5%
부산 5
7.9%
경기 5
7.9%
충남 4
 
6.3%
전북 3
 
4.8%
대구 2
 
3.2%
Other values (3) 5
7.9%

Length

2023-12-12T09:29:59.167459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강원 10
15.9%
전남 8
12.7%
경남 8
12.7%
경북 7
11.1%
충북 6
9.5%
부산 5
7.9%
경기 5
7.9%
충남 4
 
6.3%
전북 3
 
4.8%
대구 2
 
3.2%
Other values (3) 5
7.9%

업체명
Text

UNIQUE 

Distinct63
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size636.0 B
2023-12-12T09:29:59.397947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length12
Mean length6
Min length3

Characters and Unicode

Total characters378
Distinct characters138
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)100.0%

Sample

1st row부산광역시 서구청
2nd row부산동구청
3rd row부산중구청
4th row세운철강(주)
5th row해운대블루라인(주)
ValueCountFrequency (%)
부산광역시 1
 
1.6%
서구청 1
 
1.6%
구미시 1
 
1.6%
서대산약용식물원 1
 
1.6%
예산군청 1
 
1.6%
현대제철(주 1
 
1.6%
남원테마파크(주 1
 
1.6%
무주군 1
 
1.6%
태권도진흥재단 1
 
1.6%
주)땅끝모노레일 1
 
1.6%
Other values (54) 54
84.4%
2023-12-12T09:29:59.877962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 21
 
5.6%
군 20
 
5.3%
) 19
 
5.0%
( 19
 
5.0%
시 12
 
3.2%
천 9
 
2.4%
강 8
 
2.1%
원 8
 
2.1%
산 7
 
1.9%
제 7
 
1.9%
Other values (128) 248
65.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 337
89.2%
Close Punctuation 19
 
5.0%
Open Punctuation 19
 
5.0%
Uppercase Letter 2
 
0.5%
Space Separator 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 21
 
6.2%
군 20
 
5.9%
시 12
 
3.6%
천 9
 
2.7%
강 8
 
2.4%
원 8
 
2.4%
산 7
 
2.1%
제 7
 
2.1%
영 6
 
1.8%
통 6
 
1.8%
Other values (123) 233
69.1%
Uppercase Letter
ValueCountFrequency (%)
J 1
50.0%
H 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 337
89.2%
Common 39
 
10.3%
Latin 2
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 21
 
6.2%
군 20
 
5.9%
시 12
 
3.6%
천 9
 
2.7%
강 8
 
2.4%
원 8
 
2.4%
산 7
 
2.1%
제 7
 
2.1%
영 6
 
1.8%
통 6
 
1.8%
Other values (123) 233
69.1%
Common
ValueCountFrequency (%)
) 19
48.7%
( 19
48.7%
1
 
2.6%
Latin
ValueCountFrequency (%)
J 1
50.0%
H 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 337
89.2%
ASCII 41
 
10.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 21
 
6.2%
군 20
 
5.9%
시 12
 
3.6%
천 9
 
2.7%
강 8
 
2.4%
원 8
 
2.4%
산 7
 
2.1%
제 7
 
2.1%
영 6
 
1.8%
통 6
 
1.8%
Other values (123) 233
69.1%
ASCII
ValueCountFrequency (%)
) 19
46.3%
( 19
46.3%
1
 
2.4%
J 1
 
2.4%
H 1
 
2.4%

위치
Text

Distinct62
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size636.0 B
2023-12-12T09:30:00.170267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length5.2063492
Min length2

Characters and Unicode

Total characters328
Distinct characters148
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)96.8%

Sample

1st row소망계단
2nd row초량168계단
3rd row영주동오름길
4th row목장원
5th row청사포-미포
ValueCountFrequency (%)
석산생태농원 2
 
2.9%
가우도 1
 
1.4%
모후산 1
 
1.4%
국가정원박람회장 1
 
1.4%
빛가람전망대 1
 
1.4%
생태숲 1
 
1.4%
구례 1
 
1.4%
섬진강기차마을 1
 
1.4%
땅끝전망대 1
 
1.4%
예봉산 1
 
1.4%
Other values (58) 58
84.1%
2023-12-12T09:30:00.590772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 17
 
5.2%
원 10
 
3.0%
화 7
 
2.1%
리 6
 
1.8%
도 6
 
1.8%
6
 
1.8%
대 6
 
1.8%
림 6
 
1.8%
양 6
 
1.8%
휴 5
 
1.5%
Other values (138) 253
77.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 309
94.2%
Space Separator 6
 
1.8%
Other Punctuation 4
 
1.2%
Uppercase Letter 4
 
1.2%
Decimal Number 3
 
0.9%
Dash Punctuation 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 17
 
5.5%
원 10
 
3.2%
화 7
 
2.3%
리 6
 
1.9%
도 6
 
1.9%
대 6
 
1.9%
림 6
 
1.9%
양 6
 
1.9%
휴 5
 
1.6%
가 5
 
1.6%
Other values (131) 235
76.1%
Decimal Number
ValueCountFrequency (%)
8 1
33.3%
6 1
33.3%
1 1
33.3%
Space Separator
ValueCountFrequency (%)
6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
C 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 309
94.2%
Common 15
 
4.6%
Latin 4
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 17
 
5.5%
원 10
 
3.2%
화 7
 
2.3%
리 6
 
1.9%
도 6
 
1.9%
대 6
 
1.9%
림 6
 
1.9%
양 6
 
1.9%
휴 5
 
1.6%
가 5
 
1.6%
Other values (131) 235
76.1%
Common
ValueCountFrequency (%)
6
40.0%
, 4
26.7%
- 2
 
13.3%
8 1
 
6.7%
6 1
 
6.7%
1 1
 
6.7%
Latin
ValueCountFrequency (%)
C 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 309
94.2%
ASCII 19
 
5.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 17
 
5.5%
원 10
 
3.2%
화 7
 
2.3%
리 6
 
1.9%
도 6
 
1.9%
대 6
 
1.9%
림 6
 
1.9%
양 6
 
1.9%
휴 5
 
1.6%
가 5
 
1.6%
Other values (131) 235
76.1%
ASCII
ValueCountFrequency (%)
6
31.6%
, 4
21.1%
C 4
21.1%
- 2
 
10.5%
8 1
 
5.3%
6 1
 
5.3%
1 1
 
5.3%

준공일
Date

UNIQUE 

Distinct63
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size636.0 B
Minimum2005-03-23 00:00:00
Maximum2023-05-03 00:00:00
2023-12-12T09:30:00.761385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:00.906388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

용도
Categorical

Distinct4
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size636.0 B
관광용
47 
전용
12 
스키장용
 
3
화물용
 
1

Length

Max length4
Median length3
Mean length2.8571429
Min length2

Unique

Unique1 ?
Unique (%)1.6%

Sample

1st row전용
2nd row전용
3rd row전용
4th row전용
5th row관광용

Common Values

ValueCountFrequency (%)
관광용 47
74.6%
전용 12
 
19.0%
스키장용 3
 
4.8%
화물용 1
 
1.6%

Length

2023-12-12T09:30:01.101789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:30:01.263222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관광용 47
74.6%
전용 12
 
19.0%
스키장용 3
 
4.8%
화물용 1
 
1.6%

케이블철도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size636.0 B
0
59 
1
 
3
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 59
93.7%
1 3
 
4.8%
2 1
 
1.6%

Length

2023-12-12T09:30:01.407848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:30:01.552152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 59
93.7%
1 3
 
4.8%
2 1
 
1.6%

노면전차
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size636.0 B
0
63 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 63
100.0%

Length

2023-12-12T09:30:01.685482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:30:01.791876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 63
100.0%

화물모노레일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size636.0 B
0
63 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 63
100.0%

Length

2023-12-12T09:30:01.892685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:30:01.996427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 63
100.0%

모노레일
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size636.0 B
1
51 
2
7 
0
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 51
81.0%
2 7
 
11.1%
0 5
 
7.9%

Length

2023-12-12T09:30:02.116481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:30:02.247482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 51
81.0%
2 7
 
11.1%
0 5
 
7.9%

자기부상열차
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size636.0 B
0
63 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 63
100.0%

Length

2023-12-12T09:30:02.421464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:30:02.537971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 63
100.0%

경전철
Categorical

IMBALANCE 

Distinct2
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size636.0 B
0
62 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 62
98.4%
1 1
 
1.6%

Length

2023-12-12T09:30:02.648640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:30:02.776061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 62
98.4%
1 1
 
1.6%

합계
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size636.0 B
1
52 
2
8 
0
 
2
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.6%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 52
82.5%
2 8
 
12.7%
0 2
 
3.2%
3 1
 
1.6%

Length

2023-12-12T09:30:02.889184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:30:03.034323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 52
82.5%
2 8
 
12.7%
0 2
 
3.2%
3 1
 
1.6%

Correlations

2023-12-12T09:30:03.158905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역업체명위치준공일용도케이블철도모노레일경전철합계
지역1.0001.0001.0001.0000.4480.4950.2850.0000.000
업체명1.0001.0001.0001.0001.0001.0001.0001.0001.000
위치1.0001.0001.0001.0001.0001.0001.0001.0001.000
준공일1.0001.0001.0001.0001.0001.0001.0001.0001.000
용도0.4481.0001.0001.0001.0000.0000.2470.0000.751
케이블철도0.4951.0001.0001.0000.0001.0000.6380.0000.668
모노레일0.2851.0001.0001.0000.2470.6381.0000.2460.730
경전철0.0001.0001.0001.0000.0000.0000.2461.0000.000
합계0.0001.0001.0001.0000.7510.6680.7300.0001.000
2023-12-12T09:30:03.315602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용도경전철케이블철도모노레일지역합계
용도1.0000.0000.0000.2320.2490.388
경전철0.0001.0000.0000.3970.0000.000
케이블철도0.0000.0001.0000.3010.2900.691
모노레일0.2320.3970.3011.0000.1420.768
지역0.2490.0000.2900.1421.0000.000
합계0.3880.0000.6910.7680.0001.000
2023-12-12T09:30:03.463307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역용도케이블철도모노레일경전철합계
지역1.0000.2490.2900.1420.0000.000
용도0.2491.0000.0000.2320.0000.388
케이블철도0.2900.0001.0000.3010.0000.691
모노레일0.1420.2320.3011.0000.3970.768
경전철0.0000.0000.0000.3971.0000.000
합계0.0000.3880.6910.7680.0001.000

Missing values

2023-12-12T09:29:58.896547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:29:59.047225image/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

지역업체명위치준공일용도케이블철도노면전차화물모노레일모노레일자기부상열차경전철합계
0부산부산광역시 서구청소망계단2022-04-23전용0002002
1부산부산동구청초량168계단2015-05-22전용0001001
2부산부산중구청영주동오름길2014-04-04전용0001001
3부산세운철강(주)목장원2017-09-13전용0001001
4부산해운대블루라인(주)청사포-미포2021-01-29관광용0001001
5대구군위군청석산생태농원2016-07-07관광용0001001
6대구석산생태농원석산생태농원2016-05-09관광용0001001
7인천(주)강화모노레일강화2022-04-29관광용0001001
8인천인천교통공사월미도2019-07-25관광용0001001
9울산국립자연휴양림관리소신불산자연휴양림2018-01-12관광용0001001
지역업체명위치준공일용도케이블철도노면전차화물모노레일모노레일자기부상열차경전철합계
53경북울릉군울릉도2020-03-28관광용0002002
54경북울진군엑스포공원, 죽변항2021-07-01관광용0001001
55경남(주)창원컨트리클럽창원CC2015-12-17전용2001003
56경남거제해양관광개발공사포로수용소2018-02-02관광용0001001
57경남고견사고견사2014-04-25전용0001001
58경남진주시진양호2023-05-03관광용0001001
59경남창원시제황산2009-03-27관광용0001001
60경남통영관광개발공사미륵산, 욕지도2019-05-02관광용0001001
61경남함양군대봉산자연휴양림2020-02-18관광용0001001
62경남합천군영상테마파크2018-01-09관광용0001001