Overview

Dataset statistics

Number of variables17
Number of observations898
Missing cells1455
Missing cells (%)9.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory121.1 KiB
Average record size in memory138.1 B

Variable types

Text8
Categorical6
Numeric2
DateTime1

Dataset

Description레일포털에서 제공하는 전국 역사정보의 역번호, 역사명, 노선번호, 노선명, 영문역사명, 한자역사명, 환승역 구분, 환승 노선번호, 환승 노선명, 역위도, 역경도, 운영기관명, 역사 도로명주소, 역사전화번호, 데이터기준일자 정보가 포함되어있는 표준데이터 입니다.<br/> 「대도시권광역교통관리에관한특별법」 및 「도시철도법」등에 따라 지방자치단체, 도시철도공사, 한국철도공사 등이 관리하는 도시철도 역사 정보 (광역철도로 지정된 역사정보 포함 )
Author국가철도공단
URLhttps://www.data.go.kr/data/15013205/standard.do

Alerts

제공기관명 is highly overall correlated with 역위도 and 5 other fieldsHigh correlation
노선명 is highly overall correlated with 역위도 and 5 other fieldsHigh correlation
제공기관코드 is highly overall correlated with 역위도 and 5 other fieldsHigh correlation
노선번호 is highly overall correlated with 역위도 and 5 other fieldsHigh correlation
운영기관명 is highly overall correlated with 역위도 and 5 other fieldsHigh correlation
역위도 is highly overall correlated with 역경도 and 5 other fieldsHigh correlation
역경도 is highly overall correlated with 역위도 and 5 other fieldsHigh correlation
한자역사명 has 12 (1.3%) missing valuesMissing
환승노선번호 has 736 (82.0%) missing valuesMissing
환승노선명 has 707 (78.7%) missing valuesMissing

Reproduction

Analysis started2023-12-12 11:19:54.428719
Analysis finished2023-12-12 11:19:58.307534
Duration3.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

역번호
Text

Distinct547
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2023-12-12T20:19:58.753336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.3385301
Min length2

Characters and Unicode

Total characters2998
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique404 ?
Unique (%)45.0%

Sample

1st row201
2nd row0205
3rd row0206
4th row0207
5th row0208
ValueCountFrequency (%)
116 8
 
0.9%
119 7
 
0.8%
115 7
 
0.8%
117 7
 
0.8%
118 6
 
0.7%
121 6
 
0.7%
111 6
 
0.7%
114 6
 
0.7%
122 6
 
0.7%
113 6
 
0.7%
Other values (537) 833
92.8%
2023-12-12T20:19:59.563986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 671
22.4%
1 631
21.0%
3 375
12.5%
0 308
10.3%
4 294
9.8%
5 213
 
7.1%
6 154
 
5.1%
7 148
 
4.9%
8 99
 
3.3%
9 90
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2983
99.5%
Uppercase Letter 15
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 671
22.5%
1 631
21.2%
3 375
12.6%
0 308
10.3%
4 294
9.9%
5 213
 
7.1%
6 154
 
5.2%
7 148
 
5.0%
8 99
 
3.3%
9 90
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
U 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2983
99.5%
Latin 15
 
0.5%

Most frequent character per script

Common
ValueCountFrequency (%)
2 671
22.5%
1 631
21.2%
3 375
12.6%
0 308
10.3%
4 294
9.9%
5 213
 
7.1%
6 154
 
5.2%
7 148
 
5.0%
8 99
 
3.3%
9 90
 
3.0%
Latin
ValueCountFrequency (%)
U 15
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 671
22.4%
1 631
21.0%
3 375
12.5%
0 308
10.3%
4 294
9.8%
5 213
 
7.1%
6 154
 
5.1%
7 148
 
4.9%
8 99
 
3.3%
9 90
 
3.0%

역사명
Text

Distinct797
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2023-12-12T20:20:00.096375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length3.3463252
Min length2

Characters and Unicode

Total characters3005
Distinct characters313
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

Unique703 ?
Unique (%)78.3%

Sample

1st row장산역
2nd row동대문역사문화공원
3rd row신당
4th row상왕십리
5th row왕십리
ValueCountFrequency (%)
교대 3
 
0.3%
동대문역사문화공원 3
 
0.3%
시청 3
 
0.3%
금곡역 3
 
0.3%
사상역 3
 
0.3%
종로3가 3
 
0.3%
중동역 3
 
0.3%
문현역 2
 
0.2%
부암역 2
 
0.2%
구명역 2
 
0.2%
Other values (799) 887
97.0%
2023-12-12T20:20:00.812292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 471
 
15.7%
대 102
 
3.4%
산 80
 
2.7%
동 65
 
2.2%
구 62
 
2.1%
천 49
 
1.6%
신 49
 
1.6%
정 48
 
1.6%
원 46
 
1.5%
수 37
 
1.2%
Other values (303) 1996
66.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2934
97.6%
Decimal Number 21
 
0.7%
Open Punctuation 17
 
0.6%
Close Punctuation 17
 
0.6%
Space Separator 16
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 471
 
16.1%
대 102
 
3.5%
산 80
 
2.7%
동 65
 
2.2%
구 62
 
2.1%
천 49
 
1.7%
신 49
 
1.7%
정 48
 
1.6%
원 46
 
1.6%
수 37
 
1.3%
Other values (295) 1925
65.6%
Decimal Number
ValueCountFrequency (%)
3 9
42.9%
4 5
23.8%
2 4
19.0%
5 2
 
9.5%
1 1
 
4.8%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Space Separator
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2934
97.6%
Common 71
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 471
 
16.1%
대 102
 
3.5%
산 80
 
2.7%
동 65
 
2.2%
구 62
 
2.1%
천 49
 
1.7%
신 49
 
1.7%
정 48
 
1.6%
원 46
 
1.6%
수 37
 
1.3%
Other values (295) 1925
65.6%
Common
ValueCountFrequency (%)
( 17
23.9%
) 17
23.9%
16
22.5%
3 9
12.7%
4 5
 
7.0%
2 4
 
5.6%
5 2
 
2.8%
1 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2934
97.6%
ASCII 71
 
2.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 471
 
16.1%
대 102
 
3.5%
산 80
 
2.7%
동 65
 
2.2%
구 62
 
2.1%
천 49
 
1.7%
신 49
 
1.7%
정 48
 
1.6%
원 46
 
1.6%
수 37
 
1.3%
Other values (295) 1925
65.6%
ASCII
ValueCountFrequency (%)
( 17
23.9%
) 17
23.9%
16
22.5%
3 9
12.7%
4 5
 
7.0%
2 4
 
5.6%
5 2
 
2.8%
1 1
 
1.4%

노선번호
Categorical

HIGH CORRELATION 

Distinct36
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
S2602
76 
S1105
 
53
S1102
 
51
S1107
 
51
S2601
 
40
Other values (31)
627 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS2602
2nd rowS1102
3rd rowS1102
4th rowS1102
5th rowS1102

Common Values

ValueCountFrequency (%)
S2602 76
 
8.5%
S1105 53
 
5.9%
S1102 51
 
5.7%
S1107 51
 
5.7%
S2601 40
 
4.5%
S1106 39
 
4.3%
k0201 38
 
4.2%
k0304 35
 
3.9%
S1103 34
 
3.8%
S2701 32
 
3.6%
Other values (26) 449
50.0%

Length

2023-12-12T20:20:01.032870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s2602 76
 
8.5%
s1105 53
 
5.9%
s1102 51
 
5.7%
s1107 51
 
5.7%
s2601 40
 
4.5%
s1106 39
 
4.3%
k0201 38
 
4.2%
k0304 35
 
3.9%
s1103 34
 
3.8%
s2701 32
 
3.6%
Other values (26) 449
50.0%

노선명
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
부산도시철도 2호선
86 
경의중앙선
 
54
서울 도시철도 7호선
 
51
서울 도시철도 5호선
 
51
서울 도시철도 2호선
 
51
Other values (30)
605 

Length

Max length11
Median length10
Mean length8.2572383
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row부산도시철도 2호선
2nd row서울 도시철도 2호선
3rd row서울 도시철도 2호선
4th row서울 도시철도 2호선
5th row서울 도시철도 2호선

Common Values

ValueCountFrequency (%)
부산도시철도 2호선 86
 
9.6%
경의중앙선 54
 
6.0%
서울 도시철도 7호선 51
 
5.7%
서울 도시철도 5호선 51
 
5.7%
서울 도시철도 2호선 51
 
5.7%
부산도시철도 1호선 40
 
4.5%
서울 도시철도 6호선 39
 
4.3%
경부선 37
 
4.1%
분당선 35
 
3.9%
서울 도시철도 3호선 34
 
3.8%
Other values (25) 420
46.8%

Length

2023-12-12T20:20:01.275578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
도시철도 383
20.6%
서울 292
15.7%
2호선 166
8.9%
부산도시철도 157
 
8.4%
1호선 124
 
6.7%
대구 91
 
4.9%
3호선 81
 
4.3%
경의중앙선 54
 
2.9%
7호선 51
 
2.7%
5호선 51
 
2.7%
Other values (22) 413
22.2%

영문역사명
Text

Distinct748
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2023-12-12T20:20:01.872625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length36
Mean length9.5077951
Min length3

Characters and Unicode

Total characters8538
Distinct characters64
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique614 ?
Unique (%)68.4%

Sample

1st rowJangsan
2nd rowDongdaemun History & Culture Park
3rd rowSindang
4th rowSangwangsimni
5th rowWangsimni
ValueCountFrequency (%)
univ 44
 
3.8%
park 16
 
1.4%
office 15
 
1.3%
city 12
 
1.0%
market 10
 
0.9%
complex 9
 
0.8%
of 9
 
0.8%
busan 9
 
0.8%
hall 9
 
0.8%
seoul 8
 
0.7%
Other values (773) 1022
87.9%
2023-12-12T20:20:02.772073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 1089
 
12.8%
o 817
 
9.6%
a 787
 
9.2%
e 677
 
7.9%
g 655
 
7.7%
u 377
 
4.4%
i 344
 
4.0%
269
 
3.2%
s 241
 
2.8%
m 222
 
2.6%
Other values (54) 3060
35.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6889
80.7%
Uppercase Letter 1152
 
13.5%
Space Separator 269
 
3.2%
Other Punctuation 119
 
1.4%
Dash Punctuation 55
 
0.6%
Close Punctuation 17
 
0.2%
Open Punctuation 17
 
0.2%
Decimal Number 11
 
0.1%
Modifier Symbol 6
 
0.1%
Final Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1089
15.8%
o 817
11.9%
a 787
11.4%
e 677
9.8%
g 655
9.5%
u 377
 
5.5%
i 344
 
5.0%
s 241
 
3.5%
m 222
 
3.2%
l 205
 
3.0%
Other values (15) 1475
21.4%
Uppercase Letter
ValueCountFrequency (%)
S 183
15.9%
G 126
10.9%
D 107
9.3%
C 79
 
6.9%
B 78
 
6.8%
M 74
 
6.4%
H 70
 
6.1%
J 67
 
5.8%
U 56
 
4.9%
N 54
 
4.7%
Other values (14) 258
22.4%
Other Punctuation
ValueCountFrequency (%)
' 64
53.8%
. 51
42.9%
& 3
 
2.5%
· 1
 
0.8%
Decimal Number
ValueCountFrequency (%)
3 5
45.5%
4 3
27.3%
5 2
 
18.2%
1 1
 
9.1%
Space Separator
ValueCountFrequency (%)
269
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 55
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 6
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 2
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8041
94.2%
Common 497
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1089
13.5%
o 817
 
10.2%
a 787
 
9.8%
e 677
 
8.4%
g 655
 
8.1%
u 377
 
4.7%
i 344
 
4.3%
s 241
 
3.0%
m 222
 
2.8%
l 205
 
2.5%
Other values (39) 2627
32.7%
Common
ValueCountFrequency (%)
269
54.1%
' 64
 
12.9%
- 55
 
11.1%
. 51
 
10.3%
) 17
 
3.4%
( 17
 
3.4%
` 6
 
1.2%
3 5
 
1.0%
4 3
 
0.6%
& 3
 
0.6%
Other values (5) 7
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8534
> 99.9%
Punctuation 3
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1089
 
12.8%
o 817
 
9.6%
a 787
 
9.2%
e 677
 
7.9%
g 655
 
7.7%
u 377
 
4.4%
i 344
 
4.0%
269
 
3.2%
s 241
 
2.8%
m 222
 
2.6%
Other values (51) 3056
35.8%
Punctuation
ValueCountFrequency (%)
’ 2
66.7%
‘ 1
33.3%
None
ValueCountFrequency (%)
· 1
100.0%

한자역사명
Text

MISSING 

Distinct770
Distinct (%)86.9%
Missing12
Missing (%)1.3%
Memory size7.1 KiB
2023-12-12T20:20:03.250786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length2.979684
Min length1

Characters and Unicode

Total characters2640
Distinct characters640
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique670 ?
Unique (%)75.6%

Sample

1st row山
2nd row東大門歷史文化公園
3rd row新堂
4th row上往十里
5th row往十里
ValueCountFrequency (%)
山 14
 
1.3%
新 8
 
0.7%
水 8
 
0.7%
없음 7
 
0.7%
谷 6
 
0.6%
平 6
 
0.6%
大 6
 
0.6%
村 5
 
0.5%
石 5
 
0.5%
浦 5
 
0.5%
Other values (793) 1004
93.5%
2023-12-12T20:20:03.899174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
188
 
7.1%
大 93
 
3.5%
山 82
 
3.1%
新 41
 
1.6%
川 35
 
1.3%
東 29
 
1.1%
南 28
 
1.1%
谷 28
 
1.1%
洞 27
 
1.0%
廳 27
 
1.0%
Other values (630) 2062
78.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2357
89.3%
Space Separator 188
 
7.1%
Lowercase Letter 61
 
2.3%
Uppercase Letter 11
 
0.4%
Other Punctuation 6
 
0.2%
Dash Punctuation 5
 
0.2%
Close Punctuation 4
 
0.2%
Open Punctuation 4
 
0.2%
Decimal Number 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
大 93
 
3.9%
山 82
 
3.5%
新 41
 
1.7%
川 35
 
1.5%
東 29
 
1.2%
南 28
 
1.2%
谷 28
 
1.2%
洞 27
 
1.1%
廳 27
 
1.1%
浦 27
 
1.1%
Other values (596) 1940
82.3%
Lowercase Letter
ValueCountFrequency (%)
a 7
11.5%
e 7
11.5%
n 5
 
8.2%
u 5
 
8.2%
m 5
 
8.2%
y 4
 
6.6%
i 4
 
6.6%
t 4
 
6.6%
o 4
 
6.6%
l 3
 
4.9%
Other values (8) 13
21.3%
Uppercase Letter
ValueCountFrequency (%)
C 6
54.5%
P 1
 
9.1%
D 1
 
9.1%
S 1
 
9.1%
O 1
 
9.1%
H 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
? 3
50.0%
· 2
33.3%
. 1
 
16.7%
Decimal Number
ValueCountFrequency (%)
3 2
50.0%
5 1
25.0%
4 1
25.0%
Space Separator
ValueCountFrequency (%)
188
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 2305
87.3%
Common 211
 
8.0%
Latin 72
 
2.7%
Hangul 52
 
2.0%

Most frequent character per script

Han
ValueCountFrequency (%)
大 93
 
4.0%
山 82
 
3.6%
新 41
 
1.8%
川 35
 
1.5%
東 29
 
1.3%
南 28
 
1.2%
谷 28
 
1.2%
洞 27
 
1.2%
廳 27
 
1.2%
浦 27
 
1.2%
Other values (565) 1888
81.9%
Hangul
ValueCountFrequency (%)
음 7
 
13.5%
없 7
 
13.5%
앞 4
 
7.7%
디 3
 
5.8%
털 2
 
3.8%
지 2
 
3.8%
해 2
 
3.8%
거 2
 
3.8%
월 1
 
1.9%
드 1
 
1.9%
Other values (21) 21
40.4%
Latin
ValueCountFrequency (%)
a 7
 
9.7%
e 7
 
9.7%
C 6
 
8.3%
n 5
 
6.9%
u 5
 
6.9%
m 5
 
6.9%
y 4
 
5.6%
i 4
 
5.6%
t 4
 
5.6%
o 4
 
5.6%
Other values (14) 21
29.2%
Common
ValueCountFrequency (%)
188
89.1%
- 5
 
2.4%
) 4
 
1.9%
( 4
 
1.9%
? 3
 
1.4%
3 2
 
0.9%
· 2
 
0.9%
5 1
 
0.5%
4 1
 
0.5%
. 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
CJK 2233
84.6%
ASCII 281
 
10.6%
CJK Compat Ideographs 72
 
2.7%
Hangul 51
 
1.9%
None 2
 
0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
188
66.9%
a 7
 
2.5%
e 7
 
2.5%
C 6
 
2.1%
- 5
 
1.8%
n 5
 
1.8%
u 5
 
1.8%
m 5
 
1.8%
) 4
 
1.4%
y 4
 
1.4%
Other values (23) 45
 
16.0%
CJK
ValueCountFrequency (%)
大 93
 
4.2%
山 82
 
3.7%
新 41
 
1.8%
川 35
 
1.6%
東 29
 
1.3%
南 28
 
1.3%
谷 28
 
1.3%
洞 27
 
1.2%
廳 27
 
1.2%
浦 27
 
1.2%
Other values (532) 1816
81.3%
CJK Compat Ideographs
ValueCountFrequency (%)
龍 11
 
15.3%
梁 6
 
8.3%
樂 5
 
6.9%
梨 4
 
5.6%
老 3
 
4.2%
歷 3
 
4.2%
蓮 3
 
4.2%
栗 3
 
4.2%
蘆 2
 
2.8%
丹 2
 
2.8%
Other values (23) 30
41.7%
Hangul
ValueCountFrequency (%)
음 7
 
13.7%
없 7
 
13.7%
앞 4
 
7.8%
디 3
 
5.9%
털 2
 
3.9%
지 2
 
3.9%
해 2
 
3.9%
거 2
 
3.9%
월 1
 
2.0%
드 1
 
2.0%
Other values (20) 20
39.2%
None
ValueCountFrequency (%)
· 2
100.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%

환승역구분
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
일반역
708 
환승역
190 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반역
2nd row환승역
3rd row환승역
4th row일반역
5th row환승역

Common Values

ValueCountFrequency (%)
일반역 708
78.8%
환승역 190
 
21.2%

Length

2023-12-12T20:20:04.080960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:20:04.274556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반역 708
78.8%
환승역 190
 
21.2%

환승노선번호
Text

MISSING 

Distinct63
Distinct (%)38.9%
Missing736
Missing (%)82.0%
Memory size7.1 KiB
2023-12-12T20:20:04.484309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length5
Mean length7.654321
Min length1

Characters and Unicode

Total characters1240
Distinct characters17
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

Unique37 ?
Unique (%)22.8%

Sample

1st rowS1104+S1105
2nd rowS1106
3rd rowS1105+I4105+I4107
4th rowS1107
5th rowS1108
ValueCountFrequency (%)
s1102 14
 
8.6%
s1101 11
 
6.8%
k2015 9
 
5.6%
s1103 9
 
5.6%
s1106 8
 
4.9%
s1105 8
 
4.9%
i4105 7
 
4.3%
k0304+k0218 7
 
4.3%
s1109 6
 
3.7%
s1104 6
 
3.7%
Other values (53) 77
47.5%
2023-12-12T20:20:04.948017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 294
23.7%
0 265
21.4%
S 139
11.2%
2 130
10.5%
+ 73
 
5.9%
4 53
 
4.3%
k 46
 
3.7%
5 43
 
3.5%
3 43
 
3.5%
6 41
 
3.3%
Other values (7) 113
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 932
75.2%
Uppercase Letter 189
 
15.2%
Math Symbol 73
 
5.9%
Lowercase Letter 46
 
3.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 294
31.5%
0 265
28.4%
2 130
13.9%
4 53
 
5.7%
5 43
 
4.6%
3 43
 
4.6%
6 41
 
4.4%
7 30
 
3.2%
8 21
 
2.3%
9 12
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
S 139
73.5%
I 34
 
18.0%
K 9
 
4.8%
L 5
 
2.6%
N 2
 
1.1%
Math Symbol
ValueCountFrequency (%)
+ 73
100.0%
Lowercase Letter
ValueCountFrequency (%)
k 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1005
81.0%
Latin 235
 
19.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 294
29.3%
0 265
26.4%
2 130
12.9%
+ 73
 
7.3%
4 53
 
5.3%
5 43
 
4.3%
3 43
 
4.3%
6 41
 
4.1%
7 30
 
3.0%
8 21
 
2.1%
Latin
ValueCountFrequency (%)
S 139
59.1%
k 46
 
19.6%
I 34
 
14.5%
K 9
 
3.8%
L 5
 
2.1%
N 2
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 294
23.7%
0 265
21.4%
S 139
11.2%
2 130
10.5%
+ 73
 
5.9%
4 53
 
4.3%
k 46
 
3.7%
5 43
 
3.5%
3 43
 
3.5%
6 41
 
3.3%
Other values (7) 113
 
9.1%

환승노선명
Text

MISSING 

Distinct101
Distinct (%)52.9%
Missing707
Missing (%)78.7%
Memory size7.1 KiB
2023-12-12T20:20:05.284200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length10.842932
Min length1

Characters and Unicode

Total characters2071
Distinct characters61
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique75 ?
Unique (%)39.3%

Sample

1st row서울 도시철도 4호선+서울 도시철도 5호선
2nd row서울 도시철도 6호선
3rd row서울 도시철도 5호선+분당선+경의중앙선
4th row서울 도시철도 7호선
5th row서울 도시철도 8호선
ValueCountFrequency (%)
도시철도 106
23.7%
서울 85
19.0%
2호선 21
 
4.7%
3호선 20
 
4.5%
1호선 19
 
4.3%
부산도시철도 16
 
3.6%
5호선 13
 
2.9%
대구 12
 
2.7%
6호선 9
 
2.0%
9호선 8
 
1.8%
Other values (82) 138
30.9%
2023-12-12T20:20:05.865284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 278
13.4%
256
12.4%
선 175
 
8.5%
철 149
 
7.2%
호 142
 
6.9%
시 139
 
6.7%
서 95
 
4.6%
울 94
 
4.5%
경 67
 
3.2%
/ 66
 
3.2%
Other values (51) 610
29.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1509
72.9%
Space Separator 256
 
12.4%
Decimal Number 184
 
8.9%
Other Punctuation 66
 
3.2%
Math Symbol 52
 
2.5%
Uppercase Letter 2
 
0.1%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 278
18.4%
선 175
11.6%
철 149
9.9%
호 142
9.4%
시 139
9.2%
서 95
 
6.3%
울 94
 
6.2%
경 67
 
4.4%
부 49
 
3.2%
산 39
 
2.6%
Other values (37) 282
18.7%
Decimal Number
ValueCountFrequency (%)
2 38
20.7%
1 34
18.5%
3 30
16.3%
4 24
13.0%
5 18
9.8%
6 14
 
7.6%
7 11
 
6.0%
9 10
 
5.4%
8 5
 
2.7%
Space Separator
ValueCountFrequency (%)
256
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 66
100.0%
Math Symbol
ValueCountFrequency (%)
+ 52
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1509
72.9%
Common 560
 
27.0%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 278
18.4%
선 175
11.6%
철 149
9.9%
호 142
9.4%
시 139
9.2%
서 95
 
6.3%
울 94
 
6.2%
경 67
 
4.4%
부 49
 
3.2%
산 39
 
2.6%
Other values (37) 282
18.7%
Common
ValueCountFrequency (%)
256
45.7%
/ 66
 
11.8%
+ 52
 
9.3%
2 38
 
6.8%
1 34
 
6.1%
3 30
 
5.4%
4 24
 
4.3%
5 18
 
3.2%
6 14
 
2.5%
7 11
 
2.0%
Other values (3) 17
 
3.0%
Latin
ValueCountFrequency (%)
N 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1509
72.9%
ASCII 562
 
27.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 278
18.4%
선 175
11.6%
철 149
9.9%
호 142
9.4%
시 139
9.2%
서 95
 
6.3%
울 94
 
6.2%
경 67
 
4.4%
부 49
 
3.2%
산 39
 
2.6%
Other values (37) 282
18.7%
ASCII
ValueCountFrequency (%)
256
45.6%
/ 66
 
11.7%
+ 52
 
9.3%
2 38
 
6.8%
1 34
 
6.0%
3 30
 
5.3%
4 24
 
4.3%
5 18
 
3.2%
6 14
 
2.5%
7 11
 
2.0%
Other values (4) 19
 
3.4%

역위도
Real number (ℝ)

HIGH CORRELATION 

Distinct826
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.769498
Minimum35.048621
Maximum37.948608
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2023-12-12T20:20:06.091059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.048621
5-th percentile35.14462
Q135.834161
median37.467625
Q337.554954
95-th percentile37.701445
Maximum37.948608
Range2.899987
Interquartile range (IQR)1.7207934

Descriptive statistics

Standard deviation1.0268097
Coefficient of variation (CV)0.027925584
Kurtosis-1.3596827
Mean36.769498
Median Absolute Deviation (MAD)0.1742925
Skewness-0.67300668
Sum33019.01
Variance1.0543382
MonotonicityNot monotonic
2023-12-12T20:20:06.304830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.57139366 3
 
0.3%
35.169914 2
 
0.2%
35.167228 2
 
0.2%
35.157916 2
 
0.2%
35.149771 2
 
0.2%
35.142139 2
 
0.2%
35.137585 2
 
0.2%
35.135153 2
 
0.2%
35.134731 2
 
0.2%
35.135574 2
 
0.2%
Other values (816) 877
97.7%
ValueCountFrequency (%)
35.048621 1
0.1%
35.057419 1
0.1%
35.065265 1
0.1%
35.074433 1
0.1%
35.081631 1
0.1%
35.089951 1
0.1%
35.095179 1
0.1%
35.097372 1
0.1%
35.097953 1
0.1%
35.099816 1
0.1%
ValueCountFrequency (%)
37.948608 1
0.1%
37.927569 1
0.1%
37.914268 1
0.1%
37.901818 1
0.1%
37.892361 1
0.1%
37.888364 1
0.1%
37.884391 1
0.1%
37.864071 1
0.1%
37.854565 1
0.1%
37.843216 1
0.1%

역경도
Real number (ℝ)

HIGH CORRELATION 

Distinct831
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.60675
Minimum126.61694
Maximum129.23331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2023-12-12T20:20:06.573100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.61694
5-th percentile126.76958
Q1126.96361
median127.07743
Q3128.61216
95-th percentile129.09505
Maximum129.23331
Range2.616371
Interquartile range (IQR)1.6485487

Descriptive statistics

Standard deviation0.896927
Coefficient of variation (CV)0.0070288367
Kurtosis-1.2186901
Mean127.60675
Median Absolute Deviation (MAD)0.19756745
Skewness0.77905164
Sum114590.86
Variance0.80447805
MonotonicityNot monotonic
2023-12-12T20:20:06.837305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.176986 2
 
0.2%
129.020533 2
 
0.2%
129.110961 2
 
0.2%
129.107978 2
 
0.2%
129.100548 2
 
0.2%
129.092161 2
 
0.2%
129.084415 2
 
0.2%
129.074365 2
 
0.2%
129.067399 2
 
0.2%
129.066755 2
 
0.2%
Other values (821) 878
97.8%
ValueCountFrequency (%)
126.616936 2
0.2%
126.624509 1
0.1%
126.632598 1
0.1%
126.638035 1
0.1%
126.642824 1
0.1%
126.649832 1
0.1%
126.657165 1
0.1%
126.66846 1
0.1%
126.678991 2
0.2%
126.680702 1
0.1%
ValueCountFrequency (%)
129.233307 1
0.1%
129.218551 1
0.1%
129.208291 1
0.1%
129.176986 2
0.2%
129.176696 1
0.1%
129.171823 1
0.1%
129.168604 2
0.2%
129.160443699 1
0.1%
129.158908 2
0.2%
129.154024 1
0.1%

운영기관명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
서울교통공사
294 
한국철도공사
278 
부산교통공사
157 
대구광역시 대구도시철도공사
91 
대전광역시 도시철도공사
 
22
Other values (3)
56 

Length

Max length14
Median length6
Mean length7.1614699
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산교통공사
2nd row서울교통공사
3rd row서울교통공사
4th row서울교통공사
5th row서울교통공사

Common Values

ValueCountFrequency (%)
서울교통공사 294
32.7%
한국철도공사 278
31.0%
부산교통공사 157
17.5%
대구광역시 대구도시철도공사 91
 
10.1%
대전광역시 도시철도공사 22
 
2.4%
부산-김해경전철㈜ 21
 
2.3%
광주광역시 도시철도공사 20
 
2.2%
㈜우진메트로 15
 
1.7%

Length

2023-12-12T20:20:07.107705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:20:07.328683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울교통공사 294
28.5%
한국철도공사 278
27.0%
부산교통공사 157
15.2%
대구광역시 91
 
8.8%
대구도시철도공사 91
 
8.8%
도시철도공사 42
 
4.1%
대전광역시 22
 
2.1%
부산-김해경전철㈜ 21
 
2.0%
광주광역시 20
 
1.9%
㈜우진메트로 15
 
1.5%

역사도로명주소
Text

Distinct811
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2023-12-12T20:20:07.842529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length33
Mean length22.738307
Min length14

Characters and Unicode

Total characters20419
Distinct characters285
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique729 ?
Unique (%)81.2%

Sample

1st row부산광역시 해운대구 해운대로 지하 820
2nd row서울특별시 중구 을지로 지하 279 (을지로7가)
3rd row서울특별시 중구 퇴계로 지하 431-1 (신당동)
4th row서울특별시 성동구 왕십리로 지하 374 (하왕십리동)
5th row서울특별시 성동구 왕십리로 지하300(행당동)
ValueCountFrequency (%)
지하 311
 
7.0%
서울특별시 274
 
6.2%
경기도 200
 
4.5%
부산광역시 170
 
3.8%
대구광역시 88
 
2.0%
서울시 56
 
1.3%
중구 44
 
1.0%
북구 37
 
0.8%
중앙대로 32
 
0.7%
강남구 31
 
0.7%
Other values (1500) 3186
71.9%
2023-12-12T20:20:08.632406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3532
 
17.3%
시 896
 
4.4%
구 895
 
4.4%
로 882
 
4.3%
동 679
 
3.3%
1 585
 
2.9%
지 545
 
2.7%
하 522
 
2.6%
( 473
 
2.3%
) 472
 
2.3%
Other values (275) 10938
53.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12716
62.3%
Space Separator 3532
 
17.3%
Decimal Number 3083
 
15.1%
Open Punctuation 473
 
2.3%
Close Punctuation 472
 
2.3%
Dash Punctuation 140
 
0.7%
Other Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 896
 
7.0%
구 895
 
7.0%
로 882
 
6.9%
동 679
 
5.3%
지 545
 
4.3%
하 522
 
4.1%
대 450
 
3.5%
서 448
 
3.5%
광 365
 
2.9%
산 339
 
2.7%
Other values (260) 6695
52.7%
Decimal Number
ValueCountFrequency (%)
1 585
19.0%
2 454
14.7%
3 331
10.7%
0 314
10.2%
5 261
8.5%
7 260
8.4%
4 259
8.4%
6 222
 
7.2%
9 217
 
7.0%
8 180
 
5.8%
Space Separator
ValueCountFrequency (%)
3532
100.0%
Open Punctuation
ValueCountFrequency (%)
( 473
100.0%
Close Punctuation
ValueCountFrequency (%)
) 472
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 140
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12716
62.3%
Common 7703
37.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 896
 
7.0%
구 895
 
7.0%
로 882
 
6.9%
동 679
 
5.3%
지 545
 
4.3%
하 522
 
4.1%
대 450
 
3.5%
서 448
 
3.5%
광 365
 
2.9%
산 339
 
2.7%
Other values (260) 6695
52.7%
Common
ValueCountFrequency (%)
3532
45.9%
1 585
 
7.6%
( 473
 
6.1%
) 472
 
6.1%
2 454
 
5.9%
3 331
 
4.3%
0 314
 
4.1%
5 261
 
3.4%
7 260
 
3.4%
4 259
 
3.4%
Other values (5) 762
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12716
62.3%
ASCII 7703
37.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3532
45.9%
1 585
 
7.6%
( 473
 
6.1%
) 472
 
6.1%
2 454
 
5.9%
3 331
 
4.3%
0 314
 
4.1%
5 261
 
3.4%
7 260
 
3.4%
4 259
 
3.4%
Other values (5) 762
 
9.9%
Hangul
ValueCountFrequency (%)
시 896
 
7.0%
구 895
 
7.0%
로 882
 
6.9%
동 679
 
5.3%
지 545
 
4.3%
하 522
 
4.1%
대 450
 
3.5%
서 448
 
3.5%
광 365
 
2.9%
산 339
 
2.7%
Other values (260) 6695
52.7%

역사전화번호
Text

Distinct509
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2023-12-12T20:20:09.161423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.071269
Min length9

Characters and Unicode

Total characters9942
Distinct characters11
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

Unique448 ?
Unique (%)49.9%

Sample

1st row051-678-6201
2nd row02-6110-2051
3rd row02-6110-2061
4th row02-6110-2071
5th row02-6110-2081
ValueCountFrequency (%)
1544-7788 278
31.0%
055-310-9601 21
 
2.3%
031-820-1000 15
 
1.7%
053-640-7521 6
 
0.7%
053-640-7611 6
 
0.7%
053-640-7431 6
 
0.7%
053-640-7381 6
 
0.7%
053-640-7581 6
 
0.7%
051-678-6228 2
 
0.2%
051-678-6221 2
 
0.2%
Other values (499) 550
61.2%
2023-12-12T20:20:09.879287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1635
16.4%
- 1518
15.3%
0 1062
10.7%
7 935
9.4%
6 886
8.9%
8 859
8.6%
4 815
8.2%
5 800
8.0%
2 722
7.3%
3 569
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8424
84.7%
Dash Punctuation 1518
 
15.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1635
19.4%
0 1062
12.6%
7 935
11.1%
6 886
10.5%
8 859
10.2%
4 815
9.7%
5 800
9.5%
2 722
8.6%
3 569
 
6.8%
9 141
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 1518
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9942
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1635
16.4%
- 1518
15.3%
0 1062
10.7%
7 935
9.4%
6 886
8.9%
8 859
8.6%
4 815
8.2%
5 800
8.0%
2 722
7.3%
3 569
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9942
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1635
16.4%
- 1518
15.3%
0 1062
10.7%
7 935
9.4%
6 886
8.9%
8 859
8.6%
4 815
8.2%
5 800
8.0%
2 722
7.3%
3 569
 
5.7%

데이터기준일자
Date

Distinct10
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
Minimum2019-05-20 00:00:00
Maximum2021-09-01 00:00:00
2023-12-12T20:20:10.098167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:10.291701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)

제공기관코드
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
B553766
292 
B551457
278 
B551542
114 
B552109
91 
5380000
43 
Other values (5)
80 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5380000
2nd rowB553766
3rd rowB553766
4th rowB553766
5th rowB553766

Common Values

ValueCountFrequency (%)
B553766 292
32.5%
B551457 278
31.0%
B551542 114
 
12.7%
B552109 91
 
10.1%
5380000 43
 
4.8%
B551918 22
 
2.4%
5350000 21
 
2.3%
B551232 20
 
2.2%
3820000 15
 
1.7%
4040000 2
 
0.2%

Length

2023-12-12T20:20:10.514769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:20:10.717055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b553766 292
32.5%
b551457 278
31.0%
b551542 114
 
12.7%
b552109 91
 
10.1%
5380000 43
 
4.8%
b551918 22
 
2.4%
5350000 21
 
2.3%
b551232 20
 
2.2%
3820000 15
 
1.7%
4040000 2
 
0.2%

제공기관명
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
서울교통공사
292 
한국철도공사
278 
부산교통공사
114 
대구도시철도공사
91 
경상남도 양산시
43 
Other values (5)
80 

Length

Max length11
Median length6
Mean length6.6146993
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상남도 양산시
2nd row서울교통공사
3rd row서울교통공사
4th row서울교통공사
5th row서울교통공사

Common Values

ValueCountFrequency (%)
서울교통공사 292
32.5%
한국철도공사 278
31.0%
부산교통공사 114
 
12.7%
대구도시철도공사 91
 
10.1%
경상남도 양산시 43
 
4.8%
대전광역시도시철도공사 22
 
2.4%
경상남도 김해시 21
 
2.3%
광주광역시도시철도공사 20
 
2.2%
경기도 의정부시 15
 
1.7%
경기도 하남시 2
 
0.2%

Length

2023-12-12T20:20:11.424299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:20:11.641092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울교통공사 292
29.8%
한국철도공사 278
28.4%
부산교통공사 114
 
11.6%
대구도시철도공사 91
 
9.3%
경상남도 64
 
6.5%
양산시 43
 
4.4%
대전광역시도시철도공사 22
 
2.2%
김해시 21
 
2.1%
광주광역시도시철도공사 20
 
2.0%
경기도 17
 
1.7%
Other values (2) 17
 
1.7%

Interactions

2023-12-12T20:19:57.141901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:19:56.775861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:19:57.328963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:19:56.957795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:20:11.834536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선번호노선명환승역구분환승노선번호역위도역경도운영기관명데이터기준일자제공기관코드제공기관명
노선번호1.0000.9990.4050.9860.9520.9481.0000.9870.9870.987
노선명0.9991.0000.3640.9830.9530.9251.0000.9980.9980.998
환승역구분0.4050.3641.0001.0000.3860.3050.3680.3600.3600.360
환승노선번호0.9860.9831.0001.0000.9770.9991.0000.9920.9920.992
역위도0.9520.9530.3860.9771.0000.7870.8830.9450.9450.945
역경도0.9480.9250.3050.9990.7871.0000.8410.8360.8360.836
운영기관명1.0001.0000.3681.0000.8830.8411.0001.0001.0001.000
데이터기준일자0.9870.9980.3600.9920.9450.8361.0001.0001.0001.000
제공기관코드0.9870.9980.3600.9920.9450.8361.0001.0001.0001.000
제공기관명0.9870.9980.3600.9920.9450.8361.0001.0001.0001.000
2023-12-12T20:20:12.090122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제공기관명노선명제공기관코드환승역구분노선번호운영기관명
제공기관명1.0000.9461.0000.2750.8860.999
노선명0.9461.0000.9460.3020.9640.985
제공기관코드1.0000.9461.0000.2750.8860.999
환승역구분0.2750.3020.2751.0000.3160.276
노선번호0.8860.9640.8860.3161.0000.984
운영기관명0.9990.9850.9990.2760.9841.000
2023-12-12T20:20:12.322487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역위도역경도노선번호노선명환승역구분운영기관명제공기관코드제공기관명
역위도1.000-0.5970.7290.7210.2950.6890.6070.607
역경도-0.5971.0000.6700.6580.3040.6190.5820.582
노선번호0.7290.6701.0000.9640.3160.9840.8860.886
노선명0.7210.6580.9641.0000.3020.9850.9460.946
환승역구분0.2950.3040.3160.3021.0000.2760.2750.275
운영기관명0.6890.6190.9840.9850.2761.0000.9990.999
제공기관코드0.6070.5820.8860.9460.2750.9991.0001.000
제공기관명0.6070.5820.8860.9460.2750.9991.0001.000

Missing values

2023-12-12T20:19:57.596598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:19:57.979241image/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.
2023-12-12T20:19:58.215863image/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

역번호역사명노선번호노선명영문역사명한자역사명환승역구분환승노선번호환승노선명역위도역경도운영기관명역사도로명주소역사전화번호데이터기준일자제공기관코드제공기관명
0201장산역S2602부산도시철도 2호선Jangsan山일반역<NA><NA>35.169914129.176986부산교통공사부산광역시 해운대구 해운대로 지하 820051-678-62012020-06-055380000경상남도 양산시
10205동대문역사문화공원S1102서울 도시철도 2호선Dongdaemun History & Culture Park東大門歷史文化公園환승역S1104+S1105서울 도시철도 4호선+서울 도시철도 5호선37.565003127.007394서울교통공사서울특별시 중구 을지로 지하 279 (을지로7가)02-6110-20512021-04-09B553766서울교통공사
20206신당S1102서울 도시철도 2호선Sindang新堂환승역S1106서울 도시철도 6호선37.565829127.018102서울교통공사서울특별시 중구 퇴계로 지하 431-1 (신당동)02-6110-20612021-04-09B553766서울교통공사
30207상왕십리S1102서울 도시철도 2호선Sangwangsimni上往十里일반역<NA><NA>37.564171127.029352서울교통공사서울특별시 성동구 왕십리로 지하 374 (하왕십리동)02-6110-20712021-04-09B553766서울교통공사
40208왕십리S1102서울 도시철도 2호선Wangsimni往十里환승역S1105+I4105+I4107서울 도시철도 5호선+분당선+경의중앙선37.561525127.037508서울교통공사서울특별시 성동구 왕십리로 지하300(행당동)02-6110-20812021-04-09B553766서울교통공사
50209한양대S1102서울 도시철도 2호선Hanyang Univ.漢陽大일반역<NA><NA>37.555222127.043492서울교통공사서울특별시 성동구 왕십리로 206 (행당동)02-6110-20912021-04-09B553766서울교통공사
60210뚝섬S1102서울 도시철도 2호선Ttukseom纛島일반역<NA><NA>37.547122127.047365서울교통공사서울특별시 성동구 아차산로 18 (성수동1가)02-6110-21012021-04-09B553766서울교통공사
70211성수S1102서울 도시철도 2호선Seongsu聖水일반역<NA><NA>37.544434127.055989서울교통공사서울특별시 성동구 아차산로 100 (성수동2가)02-6110-21112021-04-09B553766서울교통공사
80212건대입구S1102서울 도시철도 2호선Konkuk Univ.建大入口환승역S1107서울 도시철도 7호선37.540609127.070063서울교통공사서울특별시 광진구 아차산로 243 (화양동)02-6110-21212021-04-09B553766서울교통공사
90213구의S1102서울 도시철도 2호선Guui九宜일반역<NA><NA>37.536919127.08573서울교통공사서울특별시 광진구 아차산로 384-1 (구의동)02-6110-21312021-04-09B553766서울교통공사
역번호역사명노선번호노선명영문역사명한자역사명환승역구분환승노선번호환승노선명역위도역경도운영기관명역사도로명주소역사전화번호데이터기준일자제공기관코드제공기관명
8884129봉은사S1109서울 도시철도 9호선Bongeunsa奉恩寺일반역<NA><NA>37.514361127.060892서울교통공사서울특별시 강남구 봉은사로 지하 60102-2656-09292021-04-09B553766서울교통공사
8894130종합운동장S1109서울 도시철도 9호선Sports Complex綜合運動場환승역S1102서울 도시철도 2호선37.510969127.073356서울교통공사서울특별시 송파구 올림픽로 지하 2302-2656-09302021-04-09B553766서울교통공사
8904131삼전S1109서울 도시철도 9호선Samjeon三 田일반역<NA><NA>37.504745127.087781서울교통공사서울특별시 송파구 백제고분로 18702-2656-09312021-04-09B553766서울교통공사
8914132석촌고분S1109서울 도시철도 9호선Seokchon Gobun石村古墳일반역<NA><NA>37.502028127.096622서울교통공사서울특별시 송파구 삼학사로 5302-2656-09322021-04-09B553766서울교통공사
8924133석촌S1109서울 도시철도 9호선Seokchon(Hansol Hospital)石村환승역S1108서울 도시철도 8호선37.505949127.106346서울교통공사서울특별시 송파구 송파대로 43902-2656-09332021-04-09B553766서울교통공사
8934134송파나루S1109서울 도시철도 9호선Songpanaru松坡나루일반역<NA><NA>37.51012127.113123서울교통공사서울특별시 송파구 오금로 16502-2656-09342021-04-09B553766서울교통공사
8944135한성백제S1109서울 도시철도 9호선Hanseong Baekje漢城百?일반역<NA><NA>37.516865127.118185서울교통공사서울특별시 송파구 위례성대로 5102-2656-09352021-04-09B553766서울교통공사
8954136올림픽공원S1109서울 도시철도 9호선Olympic Park올림픽公園(韓國體大)환승역S1105서울 도시철도 5호선37.516088127.1306서울교통공사서울특별시 송파구 양재대로 123302-2656-09362021-04-09B553766서울교통공사
8964137둔촌오륜S1109서울 도시철도 9호선Dunchon Oryun遁村五輪일반역<NA><NA>37.517975127.14017서울교통공사서울특별시 송파구 강동대로 32702-2656-09372021-04-09B553766서울교통공사
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