Overview

Dataset statistics

Number of variables5
Number of observations67
Missing cells4
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 KiB
Average record size in memory42.0 B

Variable types

Categorical1
Text4

Dataset

Description파일 다운로드
Author서울교통공사
URLhttps://data.seoul.go.kr/dataList/OA-13290/F/1/datasetView.do

Alerts

Unnamed: 1 has 1 (1.5%) missing valuesMissing
Unnamed: 2 has 1 (1.5%) missing valuesMissing
Unnamed: 3 has 1 (1.5%) missing valuesMissing
Unnamed: 4 has 1 (1.5%) missing valuesMissing

Reproduction

Analysis started2024-04-29 16:48:59.033565
Analysis finished2024-04-29 16:48:59.755294
Duration0.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

□ 환승역 환승거리 및 소요시간
Categorical

Distinct6
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size668.0 B
2
27 
3
16 
4
13 
1
9 
[환승소요시간 산출 : 159m/1.2m(초당 이동거리) = 132.5초 ∴ 2분 13초] 환승거리는 환승연결통로 최단거리 기준
 
1

Length

Max length71
Median length1
Mean length2.0597015
Min length1

Unique

Unique2 ?
Unique (%)3.0%

Sample

1st row[환승소요시간 산출 : 159m/1.2m(초당 이동거리) = 132.5초 ∴ 2분 13초] 환승거리는 환승연결통로 최단거리 기준
2nd row호선
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 27
40.3%
3 16
23.9%
4 13
19.4%
1 9
 
13.4%
[환승소요시간 산출 : 159m/1.2m(초당 이동거리) = 132.5초 ∴ 2분 13초] 환승거리는 환승연결통로 최단거리 기준 1
 
1.5%
호선 1
 
1.5%

Length

2024-04-30T01:48:59.819311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:48:59.923002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 27
33.8%
3 16
20.0%
4 13
16.2%
1 9
 
11.2%
2
 
2.5%
2분 1
 
1.2%
기준 1
 
1.2%
최단거리 1
 
1.2%
환승연결통로 1
 
1.2%
환승거리는 1
 
1.2%
Other values (8) 8
 
10.0%

Unnamed: 1
Text

MISSING 

Distinct45
Distinct (%)68.2%
Missing1
Missing (%)1.5%
Memory size668.0 B
2024-04-30T01:49:00.129265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.469697
Min length3

Characters and Unicode

Total characters229
Distinct characters82
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)47.0%

Sample

1st row환승역명
2nd row서울역
3rd row서울역
4th row시 청
5th row종로3가
ValueCountFrequency (%)
동대문 5
 
5.5%
수 4
 
4.4%
종로3가 4
 
4.4%
당 4
 
4.4%
서울역 3
 
3.3%
왕십리 3
 
3.3%
대 3
 
3.3%
고속터미널 2
 
2.2%
동 2
 
2.2%
신도림 2
 
2.2%
Other values (50) 59
64.8%
2024-04-30T01:49:00.444712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
25
 
10.9%
대 12
 
5.2%
동 11
 
4.8%
로 10
 
4.4%
가 8
 
3.5%
문 7
 
3.1%
신 7
 
3.1%
3 6
 
2.6%
역 6
 
2.6%
종 5
 
2.2%
Other values (72) 132
57.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 197
86.0%
Space Separator 25
 
10.9%
Decimal Number 7
 
3.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 12
 
6.1%
동 11
 
5.6%
로 10
 
5.1%
가 8
 
4.1%
문 7
 
3.6%
신 7
 
3.6%
역 6
 
3.0%
종 5
 
2.5%
구 5
 
2.5%
수 4
 
2.0%
Other values (69) 122
61.9%
Decimal Number
ValueCountFrequency (%)
3 6
85.7%
4 1
 
14.3%
Space Separator
ValueCountFrequency (%)
25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 197
86.0%
Common 32
 
14.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 12
 
6.1%
동 11
 
5.6%
로 10
 
5.1%
가 8
 
4.1%
문 7
 
3.6%
신 7
 
3.6%
역 6
 
3.0%
종 5
 
2.5%
구 5
 
2.5%
수 4
 
2.0%
Other values (69) 122
61.9%
Common
ValueCountFrequency (%)
25
78.1%
3 6
 
18.8%
4 1
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 197
86.0%
ASCII 32
 
14.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
25
78.1%
3 6
 
18.8%
4 1
 
3.1%
Hangul
ValueCountFrequency (%)
대 12
 
6.1%
동 11
 
5.6%
로 10
 
5.1%
가 8
 
4.1%
문 7
 
3.6%
신 7
 
3.6%
역 6
 
3.0%
종 5
 
2.5%
구 5
 
2.5%
수 4
 
2.0%
Other values (69) 122
61.9%

Unnamed: 2
Text

MISSING 

Distinct44
Distinct (%)66.7%
Missing1
Missing (%)1.5%
Memory size668.0 B
2024-04-30T01:49:00.620311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.969697
Min length4

Characters and Unicode

Total characters328
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)39.4%

Sample

1st row환승노선
2nd row1→4호선
3rd row1→공항철도
4th row1→2호선
5th row1→3호선
ValueCountFrequency (%)
2→5호선 5
 
7.6%
3→6호선 3
 
4.5%
2→4호선 2
 
3.0%
2→9호선 2
 
3.0%
2→6호선 2
 
3.0%
2→7호선 2
 
3.0%
2→지선 2
 
3.0%
2→분당선 2
 
3.0%
3→분당선 2
 
3.0%
4→2호선 2
 
3.0%
Other values (34) 42
63.6%
2024-04-30T01:49:00.944168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
→ 65
19.8%
선 59
18.0%
호 47
14.3%
2 33
10.1%
3 20
 
6.1%
4 18
 
5.5%
1 14
 
4.3%
5 9
 
2.7%
6 7
 
2.1%
철 7
 
2.1%
Other values (18) 49
14.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 151
46.0%
Decimal Number 112
34.1%
Math Symbol 65
19.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
선 59
39.1%
호 47
31.1%
철 7
 
4.6%
분 6
 
4.0%
당 6
 
4.0%
국 4
 
2.6%
항 3
 
2.0%
공 3
 
2.0%
도 3
 
2.0%
신 2
 
1.3%
Other values (8) 11
 
7.3%
Decimal Number
ValueCountFrequency (%)
2 33
29.5%
3 20
17.9%
4 18
16.1%
1 14
12.5%
5 9
 
8.0%
6 7
 
6.2%
7 5
 
4.5%
9 4
 
3.6%
8 2
 
1.8%
Math Symbol
ValueCountFrequency (%)
→ 65
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 177
54.0%
Hangul 151
46.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
선 59
39.1%
호 47
31.1%
철 7
 
4.6%
분 6
 
4.0%
당 6
 
4.0%
국 4
 
2.6%
항 3
 
2.0%
공 3
 
2.0%
도 3
 
2.0%
신 2
 
1.3%
Other values (8) 11
 
7.3%
Common
ValueCountFrequency (%)
→ 65
36.7%
2 33
18.6%
3 20
 
11.3%
4 18
 
10.2%
1 14
 
7.9%
5 9
 
5.1%
6 7
 
4.0%
7 5
 
2.8%
9 4
 
2.3%
8 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 151
46.0%
ASCII 112
34.1%
Arrows 65
19.8%

Most frequent character per block

Arrows
ValueCountFrequency (%)
→ 65
100.0%
Hangul
ValueCountFrequency (%)
선 59
39.1%
호 47
31.1%
철 7
 
4.6%
분 6
 
4.0%
당 6
 
4.0%
국 4
 
2.6%
항 3
 
2.0%
공 3
 
2.0%
도 3
 
2.0%
신 2
 
1.3%
Other values (8) 11
 
7.3%
ASCII
ValueCountFrequency (%)
2 33
29.5%
3 20
17.9%
4 18
16.1%
1 14
12.5%
5 9
 
8.0%
6 7
 
6.2%
7 5
 
4.5%
9 4
 
3.6%
8 2
 
1.8%

Unnamed: 3
Text

MISSING 

Distinct49
Distinct (%)74.2%
Missing1
Missing (%)1.5%
Memory size668.0 B
2024-04-30T01:49:01.135118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.6666667
Min length2

Characters and Unicode

Total characters176
Distinct characters17
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

Unique36 ?
Unique (%)54.5%

Sample

1st row환승거리(m)
2nd row159
3rd row365
4th row101
5th row118
ValueCountFrequency (%)
159 4
 
6.1%
81 3
 
4.5%
155 3
 
4.5%
194 2
 
3.0%
75 2
 
3.0%
78 2
 
3.0%
118 2
 
3.0%
101 2
 
3.0%
77 2
 
3.0%
149 2
 
3.0%
Other values (39) 42
63.6%
2024-04-30T01:49:01.436435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 44
25.0%
5 25
14.2%
7 18
10.2%
9 16
 
9.1%
4 16
 
9.1%
2 14
 
8.0%
8 12
 
6.8%
0 9
 
5.1%
3 8
 
4.5%
6 7
 
4.0%
Other values (7) 7
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 169
96.0%
Other Letter 4
 
2.3%
Open Punctuation 1
 
0.6%
Lowercase Letter 1
 
0.6%
Close Punctuation 1
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 44
26.0%
5 25
14.8%
7 18
10.7%
9 16
 
9.5%
4 16
 
9.5%
2 14
 
8.3%
8 12
 
7.1%
0 9
 
5.3%
3 8
 
4.7%
6 7
 
4.1%
Other Letter
ValueCountFrequency (%)
환 1
25.0%
승 1
25.0%
거 1
25.0%
리 1
25.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 171
97.2%
Hangul 4
 
2.3%
Latin 1
 
0.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 44
25.7%
5 25
14.6%
7 18
10.5%
9 16
 
9.4%
4 16
 
9.4%
2 14
 
8.2%
8 12
 
7.0%
0 9
 
5.3%
3 8
 
4.7%
6 7
 
4.1%
Other values (2) 2
 
1.2%
Hangul
ValueCountFrequency (%)
환 1
25.0%
승 1
25.0%
거 1
25.0%
리 1
25.0%
Latin
ValueCountFrequency (%)
m 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 172
97.7%
Hangul 4
 
2.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 44
25.6%
5 25
14.5%
7 18
10.5%
9 16
 
9.3%
4 16
 
9.3%
2 14
 
8.1%
8 12
 
7.0%
0 9
 
5.2%
3 8
 
4.7%
6 7
 
4.1%
Other values (3) 3
 
1.7%
Hangul
ValueCountFrequency (%)
환 1
25.0%
승 1
25.0%
거 1
25.0%
리 1
25.0%

Unnamed: 4
Text

MISSING 

Distinct47
Distinct (%)71.2%
Missing1
Missing (%)1.5%
Memory size668.0 B
2024-04-30T01:49:01.629297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length6
Mean length5.4545455
Min length2

Characters and Unicode

Total characters360
Distinct characters21
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)51.5%

Sample

1st row환승 소요시간(초)
2nd row2분 13초
3rd row5분 4초
4th row1분 25초
5th row1분 39초
ValueCountFrequency (%)
1분 28
22.6%
2분 20
16.1%
5초 6
 
4.8%
3분 6
 
4.8%
10초 4
 
3.2%
13초 4
 
3.2%
8초 3
 
2.4%
39초 3
 
2.4%
38초 3
 
2.4%
15초 3
 
2.4%
Other values (31) 44
35.5%
2024-04-30T01:49:02.016694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
초 65
18.1%
분 58
16.1%
58
16.1%
1 46
12.8%
2 40
11.1%
3 26
 
7.2%
5 22
 
6.1%
8 10
 
2.8%
0 10
 
2.8%
4 7
 
1.9%
Other values (11) 18
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 171
47.5%
Other Letter 129
35.8%
Space Separator 58
 
16.1%
Open Punctuation 1
 
0.3%
Close Punctuation 1
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 46
26.9%
2 40
23.4%
3 26
15.2%
5 22
12.9%
8 10
 
5.8%
0 10
 
5.8%
4 7
 
4.1%
9 5
 
2.9%
7 3
 
1.8%
6 2
 
1.2%
Other Letter
ValueCountFrequency (%)
초 65
50.4%
분 58
45.0%
환 1
 
0.8%
승 1
 
0.8%
소 1
 
0.8%
요 1
 
0.8%
시 1
 
0.8%
간 1
 
0.8%
Space Separator
ValueCountFrequency (%)
58
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 231
64.2%
Hangul 129
35.8%

Most frequent character per script

Common
ValueCountFrequency (%)
58
25.1%
1 46
19.9%
2 40
17.3%
3 26
11.3%
5 22
 
9.5%
8 10
 
4.3%
0 10
 
4.3%
4 7
 
3.0%
9 5
 
2.2%
7 3
 
1.3%
Other values (3) 4
 
1.7%
Hangul
ValueCountFrequency (%)
초 65
50.4%
분 58
45.0%
환 1
 
0.8%
승 1
 
0.8%
소 1
 
0.8%
요 1
 
0.8%
시 1
 
0.8%
간 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 231
64.2%
Hangul 129
35.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
초 65
50.4%
분 58
45.0%
환 1
 
0.8%
승 1
 
0.8%
소 1
 
0.8%
요 1
 
0.8%
시 1
 
0.8%
간 1
 
0.8%
ASCII
ValueCountFrequency (%)
58
25.1%
1 46
19.9%
2 40
17.3%
3 26
11.3%
5 22
 
9.5%
8 10
 
4.3%
0 10
 
4.3%
4 7
 
3.0%
9 5
 
2.2%
7 3
 
1.3%
Other values (3) 4
 
1.7%

Correlations

2024-04-30T01:49:02.134423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
□ 환승역 환승거리 및 소요시간Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4
□ 환승역 환승거리 및 소요시간1.0000.8411.0000.7150.688
Unnamed: 10.8411.0000.0000.9910.986
Unnamed: 21.0000.0001.0000.8260.808
Unnamed: 30.7150.9910.8261.0001.000
Unnamed: 40.6880.9860.8081.0001.000

Missing values

2024-04-30T01:48:59.505852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T01:48:59.607333image/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.
2024-04-30T01:48:59.695469image/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

□ 환승역 환승거리 및 소요시간Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4
0[환승소요시간 산출 : 159m/1.2m(초당 이동거리) = 132.5초 ∴ 2분 13초] 환승거리는 환승연결통로 최단거리 기준<NA><NA><NA><NA>
1호선환승역명환승노선환승거리(m)환승 소요시간(초)
21서울역1→4호선1592분 13초
31서울역1→공항철도3655분 4초
41시 청1→2호선1011분 25초
51종로3가1→3호선1181분 39초
61종로3가1→5호선3124분 20초
71동대문1→4호선1942분 42초
81동묘앞1→6호선961분 20초
91신설동1→2호선1592분 13초
□ 환승역 환승거리 및 소요시간Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4
574동대문4→2호선4538초
584역사문화공원4→5호선1071분 30초
594사 당4→2호선741분 2초
604충무로4→3호선1715초
614노 원4→7호선2783분 52초
624창 동4→국철841분 10초
634이 촌4→중앙선781분 5초
644삼각지4→6호선1552분 10초
654동 작4→9호선2453분 25초
664총신대입구4→7호선1712분 23초