Overview

Dataset statistics

Number of variables10
Number of observations34
Missing cells25
Missing cells (%)7.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory86.9 B

Variable types

Categorical7
Text3

Dataset

Description대전교통공사에서 운영하는 도시광역철도역들의 철도운영기관명, 선명, 역명, 지상지하구분, 제세동기의 역층, 출입구번호, 상세위치, 제세동기출력에너지,제세동기운영방식,수량 에 대한 데이터가 있습니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15041488/fileData.do

Alerts

철도운영기관명 has constant value ""Constant
선명 has constant value ""Constant
제세동기출력에너지 has constant value ""Constant
수량 has constant value ""Constant
제세동기운영방식 is highly imbalanced (67.7%)Imbalance
출입구번호 has 25 (73.5%) missing valuesMissing

Reproduction

Analysis started2023-12-12 18:13:51.740557
Analysis finished2023-12-12 18:13:52.274808
Duration0.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

철도운영기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
대전교통공사
34 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대전교통공사 34
100.0%

Length

2023-12-13T03:13:52.331343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:13:52.423382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전교통공사 34
100.0%

선명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
1호선
34 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1호선
2nd row1호선
3rd row1호선
4th row1호선
5th row1호선

Common Values

ValueCountFrequency (%)
1호선 34
100.0%

Length

2023-12-13T03:13:52.515503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:13:52.918696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1호선 34
100.0%

역명
Text

Distinct22
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-13T03:13:53.047173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.5882353
Min length2

Characters and Unicode

Total characters122
Distinct characters50
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

Unique12 ?
Unique (%)35.3%

Sample

1st row판암
2nd row신흥
3rd row대동역
4th row대전
5th row대전
ValueCountFrequency (%)
유성온천역 4
 
11.8%
대전 2
 
5.9%
노은역 2
 
5.9%
월드컵경기장역 2
 
5.9%
현충원역 2
 
5.9%
구암역 2
 
5.9%
갑천역 2
 
5.9%
월평역 2
 
5.9%
반석역 2
 
5.9%
지족역 2
 
5.9%
Other values (12) 12
35.3%
2023-12-13T03:13:53.365210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 28
23.0%
천 6
 
4.9%
유 4
 
3.3%
성 4
 
3.3%
온 4
 
3.3%
대 4
 
3.3%
월 4
 
3.3%
구 3
 
2.5%
암 3
 
2.5%
전 3
 
2.5%
Other values (40) 59
48.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 122
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 28
23.0%
천 6
 
4.9%
유 4
 
3.3%
성 4
 
3.3%
온 4
 
3.3%
대 4
 
3.3%
월 4
 
3.3%
구 3
 
2.5%
암 3
 
2.5%
전 3
 
2.5%
Other values (40) 59
48.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 122
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 28
23.0%
천 6
 
4.9%
유 4
 
3.3%
성 4
 
3.3%
온 4
 
3.3%
대 4
 
3.3%
월 4
 
3.3%
구 3
 
2.5%
암 3
 
2.5%
전 3
 
2.5%
Other values (40) 59
48.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 122
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 28
23.0%
천 6
 
4.9%
유 4
 
3.3%
성 4
 
3.3%
온 4
 
3.3%
대 4
 
3.3%
월 4
 
3.3%
구 3
 
2.5%
암 3
 
2.5%
전 3
 
2.5%
Other values (40) 59
48.4%

지상지하구분
Categorical

Distinct2
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
지하
30 
지상
4 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지하 30
88.2%
지상 4
 
11.8%

Length

2023-12-13T03:13:53.493899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:13:53.599014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지하 30
88.2%
지상 4
 
11.8%

역층
Categorical

Distinct3
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size404.0 B
1
26 
2
5 
3
3 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row3
5th row3

Common Values

ValueCountFrequency (%)
1 26
76.5%
2 5
 
14.7%
3 3
 
8.8%

Length

2023-12-13T03:13:53.690914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:13:53.794389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 26
76.5%
2 5
 
14.7%
3 3
 
8.8%

출입구번호
Text

MISSING 

Distinct6
Distinct (%)66.7%
Missing25
Missing (%)73.5%
Memory size404.0 B
2023-12-13T03:13:53.889987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)33.3%

Sample

1st row1
2nd row5
3rd row3
4th row
5th row3
ValueCountFrequency (%)
1 2
28.6%
3 2
28.6%
5 1
14.3%
2 1
14.3%
6 1
14.3%
2023-12-13T03:13:54.244627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2
22.2%
3 2
22.2%
2
22.2%
5 1
11.1%
2 1
11.1%
6 1
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7
77.8%
Space Separator 2
 
22.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2
28.6%
3 2
28.6%
5 1
14.3%
2 1
14.3%
6 1
14.3%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2
22.2%
3 2
22.2%
2
22.2%
5 1
11.1%
2 1
11.1%
6 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2
22.2%
3 2
22.2%
2
22.2%
5 1
11.1%
2 1
11.1%
6 1
11.1%

상세위치
Text

Distinct18
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-13T03:13:54.512960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length13
Mean length17.147059
Min length12

Characters and Unicode

Total characters583
Distinct characters68
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

Unique15 ?
Unique (%)44.1%

Sample

1st row(B1) 고객안내센터 앞
2nd row(B2) 고객안내센터 앞
3rd row(B1) 고객안내센터 앞
4th row(B3) 고객안내센터 앞
5th row(B3) 가 개집표기 옆
ValueCountFrequency (%)
앞 24
16.4%
b1 22
15.1%
고객안내센터 22
15.1%
옆 6
 
4.1%
방향 6
 
4.1%
b2 5
 
3.4%
역무실 5
 
3.4%
근처 5
 
3.4%
대합실 4
 
2.7%
곳 4
 
2.7%
Other values (32) 43
29.5%
2023-12-13T03:13:54.987230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
115
19.7%
( 37
 
6.3%
) 37
 
6.3%
B 30
 
5.1%
내 28
 
4.8%
1 27
 
4.6%
앞 24
 
4.1%
고 22
 
3.8%
객 22
 
3.8%
안 22
 
3.8%
Other values (58) 219
37.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 312
53.5%
Space Separator 115
 
19.7%
Decimal Number 42
 
7.2%
Open Punctuation 37
 
6.3%
Close Punctuation 37
 
6.3%
Uppercase Letter 35
 
6.0%
Dash Punctuation 3
 
0.5%
Other Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
내 28
 
9.0%
앞 24
 
7.7%
고 22
 
7.1%
객 22
 
7.1%
안 22
 
7.1%
센 22
 
7.1%
터 22
 
7.1%
실 12
 
3.8%
역 8
 
2.6%
방 8
 
2.6%
Other values (44) 122
39.1%
Decimal Number
ValueCountFrequency (%)
1 27
64.3%
2 7
 
16.7%
3 6
 
14.3%
4 1
 
2.4%
6 1
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
B 30
85.7%
F 3
 
8.6%
E 1
 
2.9%
L 1
 
2.9%
Space Separator
ValueCountFrequency (%)
115
100.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Close Punctuation
ValueCountFrequency (%)
) 37
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 312
53.5%
Common 236
40.5%
Latin 35
 
6.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
내 28
 
9.0%
앞 24
 
7.7%
고 22
 
7.1%
객 22
 
7.1%
안 22
 
7.1%
센 22
 
7.1%
터 22
 
7.1%
실 12
 
3.8%
역 8
 
2.6%
방 8
 
2.6%
Other values (44) 122
39.1%
Common
ValueCountFrequency (%)
115
48.7%
( 37
 
15.7%
) 37
 
15.7%
1 27
 
11.4%
2 7
 
3.0%
3 6
 
2.5%
- 3
 
1.3%
/ 2
 
0.8%
4 1
 
0.4%
6 1
 
0.4%
Latin
ValueCountFrequency (%)
B 30
85.7%
F 3
 
8.6%
E 1
 
2.9%
L 1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 312
53.5%
ASCII 271
46.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
115
42.4%
( 37
 
13.7%
) 37
 
13.7%
B 30
 
11.1%
1 27
 
10.0%
2 7
 
2.6%
3 6
 
2.2%
F 3
 
1.1%
- 3
 
1.1%
/ 2
 
0.7%
Other values (4) 4
 
1.5%
Hangul
ValueCountFrequency (%)
내 28
 
9.0%
앞 24
 
7.7%
고 22
 
7.1%
객 22
 
7.1%
안 22
 
7.1%
센 22
 
7.1%
터 22
 
7.1%
실 12
 
3.8%
역 8
 
2.6%
방 8
 
2.6%
Other values (44) 122
39.1%

제세동기출력에너지
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
200
34 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row200
2nd row200
3rd row200
4th row200
5th row200

Common Values

ValueCountFrequency (%)
200 34
100.0%

Length

2023-12-13T03:13:55.199264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:13:55.334771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
200 34
100.0%

제세동기운영방식
Categorical

IMBALANCE 

Distinct2
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
자동
32 
수동
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
자동 32
94.1%
수동 2
 
5.9%

Length

2023-12-13T03:13:55.475056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:13:55.591775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동 32
94.1%
수동 2
 
5.9%

수량
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
1
34 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 34
100.0%

Length

2023-12-13T03:13:55.728823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:13:55.853101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 34
100.0%

Correlations

2023-12-13T03:13:55.923974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역명지상지하구분역층출입구번호상세위치제세동기운영방식
역명1.0001.0000.9321.0000.0001.000
지상지하구분1.0001.0000.0001.0001.0000.000
역층0.9320.0001.000NaN1.0000.045
출입구번호1.0001.000NaN1.0000.638NaN
상세위치0.0001.0001.0000.6381.0000.116
제세동기운영방식1.0000.0000.045NaN0.1161.000
2023-12-13T03:13:56.059823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지상지하구분제세동기운영방식역층
지상지하구분1.0000.0000.000
제세동기운영방식0.0001.0000.061
역층0.0000.0611.000
2023-12-13T03:13:56.188888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지상지하구분역층제세동기운영방식
지상지하구분1.0000.0000.000
역층0.0001.0000.061
제세동기운영방식0.0000.0611.000

Missing values

2023-12-13T03:13:52.074462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:13:52.228582image/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

철도운영기관명선명역명지상지하구분역층출입구번호상세위치제세동기출력에너지제세동기운영방식수량
0대전교통공사1호선판암지하1<NA>(B1) 고객안내센터 앞200자동1
1대전교통공사1호선신흥지하2<NA>(B2) 고객안내센터 앞200자동1
2대전교통공사1호선대동역지하1<NA>(B1) 고객안내센터 앞200자동1
3대전교통공사1호선대전지하3<NA>(B3) 고객안내센터 앞200자동1
4대전교통공사1호선대전지하3<NA>(B3) 가 개집표기 옆200자동1
5대전교통공사1호선중앙로역지하3<NA>(B3) 고객안내센터 앞200자동1
6대전교통공사1호선중구청역지하2<NA>(B2) 고객안내센터 앞200자동1
7대전교통공사1호선서대전네거리역지하2<NA>(B2) 고객안내센터 E/L 3호기 앞200수동1
8대전교통공사1호선오룡역지하1<NA>(B1) 고객안내센터 앞200수동1
9대전교통공사1호선용문역지하11(B1) 고객안내센터 앞200자동1
철도운영기관명선명역명지상지하구분역층출입구번호상세위치제세동기출력에너지제세동기운영방식수량
24대전교통공사1호선갑천역지하1<NA>(B1) 표내는 곳 옆(역무실 옆 상행 방향)200자동1
25대전교통공사1호선유성온천역지하2<NA>(B2) 갑천역 방향(상행) 승강장 3-2 출입문 앞200자동1
26대전교통공사1호선유성온천역지하2<NA>(B2) 구암역 방향(하행) 승강장 1-4/2-1 출입문 사이 방향200자동1
27대전교통공사1호선유성온천역지하1<NA>(B1) 표내는 곳 옆 (역무실 옆 상행 방향)200자동1
28대전교통공사1호선구암역지상13(1F) 대합실 역무실 근처 3번 출입구 기준 표내는 곳 방향200자동1
29대전교통공사1호선현충원역지하11(B1) 대합실 역무실 근처 표 내는 곳 통과 후 직진 방향200자동1
30대전교통공사1호선월드컵경기장역지하1<NA>(B1) 자동발매개실 옆200자동1
31대전교통공사1호선노은역지하1(B1) 역무실 출입문 옆200자동1
32대전교통공사1호선지족역지상12대합실 비상게이트 근처200자동1
33대전교통공사1호선반석역지하16(B1) 대합실 표내는곳 근처 6번 출입구 방향200자동1