Overview

Dataset statistics

Number of variables10
Number of observations99
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.0 KiB
Average record size in memory82.3 B

Variable types

Categorical2
Text6
Boolean1
Numeric1

Dataset

Description수도권 1호선 역사들의 데이터로 철도운영기관명, 선명, 역명, 영어명, 로마자, 일본어, 중국어간체, 중국어번체, 환승역여부, 신설일자의 데이터가 있습니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15041013/fileData.do

Alerts

선명 has constant value ""Constant
철도운영기관명 is highly imbalanced (52.8%)Imbalance
역명 has unique valuesUnique
영어명 has unique valuesUnique
로마자 has unique valuesUnique
중국어간체 has unique valuesUnique
중국어번체 has unique valuesUnique

Reproduction

Analysis started2023-12-12 03:53:03.509599
Analysis finished2023-12-12 03:53:05.025027
Duration1.52 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

철도운영기관명
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
코레일
89 
서울교통공사
10 

Length

Max length6
Median length3
Mean length3.3030303
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
코레일 89
89.9%
서울교통공사 10
 
10.1%

Length

2023-12-12T12:53:05.098478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:53:05.207623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
코레일 89
89.9%
서울교통공사 10
 
10.1%

선명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
1호선
99 

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 99
100.0%

Length

2023-12-12T12:53:05.312007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:53:05.400922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1호선 99
100.0%

역명
Text

UNIQUE 

Distinct99
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2023-12-12T12:53:05.689218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length2
Mean length2.6464646
Min length2

Characters and Unicode

Total characters262
Distinct characters118
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99 ?
Unique (%)100.0%

Sample

1st row가능
2nd row가산디지털단지
3rd row간석
4th row개봉
5th row관악
ValueCountFrequency (%)
가능 1
 
1.0%
소요산 1
 
1.0%
의왕 1
 
1.0%
월계 1
 
1.0%
용산 1
 
1.0%
외대앞 1
 
1.0%
온양온천 1
 
1.0%
온수 1
 
1.0%
오산대 1
 
1.0%
오산 1
 
1.0%
Other values (89) 89
89.9%
2023-12-12T12:53:06.185125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 12
 
4.6%
천 10
 
3.8%
산 10
 
3.8%
대 9
 
3.4%
정 7
 
2.7%
서 5
 
1.9%
신 5
 
1.9%
도 5
 
1.9%
부 4
 
1.5%
지 4
 
1.5%
Other values (108) 191
72.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 254
96.9%
Open Punctuation 3
 
1.1%
Close Punctuation 3
 
1.1%
Decimal Number 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 12
 
4.7%
천 10
 
3.9%
산 10
 
3.9%
대 9
 
3.5%
정 7
 
2.8%
서 5
 
2.0%
신 5
 
2.0%
도 5
 
2.0%
부 4
 
1.6%
지 4
 
1.6%
Other values (104) 183
72.0%
Decimal Number
ValueCountFrequency (%)
5 1
50.0%
3 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 254
96.9%
Common 8
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 12
 
4.7%
천 10
 
3.9%
산 10
 
3.9%
대 9
 
3.5%
정 7
 
2.8%
서 5
 
2.0%
신 5
 
2.0%
도 5
 
2.0%
부 4
 
1.6%
지 4
 
1.6%
Other values (104) 183
72.0%
Common
ValueCountFrequency (%)
( 3
37.5%
) 3
37.5%
5 1
 
12.5%
3 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 254
96.9%
ASCII 8
 
3.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 12
 
4.7%
천 10
 
3.9%
산 10
 
3.9%
대 9
 
3.5%
정 7
 
2.8%
서 5
 
2.0%
신 5
 
2.0%
도 5
 
2.0%
부 4
 
1.6%
지 4
 
1.6%
Other values (104) 183
72.0%
ASCII
ValueCountFrequency (%)
( 3
37.5%
) 3
37.5%
5 1
 
12.5%
3 1
 
12.5%

영어명
Text

UNIQUE 

Distinct99
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2023-12-12T12:53:06.537871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length21
Mean length9.3030303
Min length4

Characters and Unicode

Total characters921
Distinct characters52
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99 ?
Unique (%)100.0%

Sample

1st rowGaneung
2nd rowGasan Digital Complex
3rd rowGanseok
4th rowGaebong
5th rowGwanak
ValueCountFrequency (%)
univ 6
 
5.0%
station 3
 
2.5%
of 2
 
1.7%
osan 2
 
1.7%
jongno 2
 
1.7%
seoul 2
 
1.7%
hankuk 1
 
0.8%
oryu-dong 1
 
0.8%
onsu 1
 
0.8%
onyangoncheon 1
 
0.8%
Other values (100) 100
82.6%
2023-12-12T12:53:07.048792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 137
14.9%
o 106
 
11.5%
g 84
 
9.1%
e 73
 
7.9%
a 73
 
7.9%
i 38
 
4.1%
u 35
 
3.8%
y 26
 
2.8%
S 25
 
2.7%
s 22
 
2.4%
Other values (42) 302
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 758
82.3%
Uppercase Letter 117
 
12.7%
Space Separator 24
 
2.6%
Other Punctuation 6
 
0.7%
Close Punctuation 5
 
0.5%
Open Punctuation 5
 
0.5%
Dash Punctuation 4
 
0.4%
Decimal Number 2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 137
18.1%
o 106
14.0%
g 84
11.1%
e 73
9.6%
a 73
9.6%
i 38
 
5.0%
u 35
 
4.6%
y 26
 
3.4%
s 22
 
2.9%
k 20
 
2.6%
Other values (15) 144
19.0%
Uppercase Letter
ValueCountFrequency (%)
S 25
21.4%
D 17
14.5%
J 11
9.4%
G 11
9.4%
U 9
 
7.7%
B 8
 
6.8%
H 5
 
4.3%
O 5
 
4.3%
N 5
 
4.3%
C 5
 
4.3%
Other values (9) 16
13.7%
Space Separator
ValueCountFrequency (%)
21
87.5%
  3
 
12.5%
Decimal Number
ValueCountFrequency (%)
3 1
50.0%
5 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 875
95.0%
Common 46
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 137
15.7%
o 106
12.1%
g 84
 
9.6%
e 73
 
8.3%
a 73
 
8.3%
i 38
 
4.3%
u 35
 
4.0%
y 26
 
3.0%
S 25
 
2.9%
s 22
 
2.5%
Other values (34) 256
29.3%
Common
ValueCountFrequency (%)
21
45.7%
. 6
 
13.0%
) 5
 
10.9%
( 5
 
10.9%
- 4
 
8.7%
  3
 
6.5%
3 1
 
2.2%
5 1
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 918
99.7%
None 3
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 137
14.9%
o 106
 
11.5%
g 84
 
9.2%
e 73
 
8.0%
a 73
 
8.0%
i 38
 
4.1%
u 35
 
3.8%
y 26
 
2.8%
S 25
 
2.7%
s 22
 
2.4%
Other values (41) 299
32.6%
None
ValueCountFrequency (%)
  3
100.0%

로마자
Text

UNIQUE 

Distinct99
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2023-12-12T12:53:07.439698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length20
Mean length9.1111111
Min length4

Characters and Unicode

Total characters902
Distinct characters52
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99 ?
Unique (%)100.0%

Sample

1st rowGaneung
2nd rowGasanDigital Complex
3rd rowGanseok
4th rowGaebong
5th rowGwanak
ValueCountFrequency (%)
univ 3
 
2.7%
dongducheon 2
 
1.8%
station 2
 
1.8%
pyeongtaek 2
 
1.8%
osan 2
 
1.8%
ganeung 1
 
0.9%
yeongdeungpo 1
 
0.9%
uijeongbu 1
 
0.9%
uiwang 1
 
0.9%
wolgye 1
 
0.9%
Other values (97) 97
85.8%
2023-12-12T12:53:08.089628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 135
15.0%
o 103
 
11.4%
g 85
 
9.4%
e 73
 
8.1%
a 72
 
8.0%
i 38
 
4.2%
u 36
 
4.0%
S 27
 
3.0%
y 25
 
2.8%
k 20
 
2.2%
Other values (42) 288
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 746
82.7%
Uppercase Letter 118
 
13.1%
Space Separator 16
 
1.8%
Dash Punctuation 8
 
0.9%
Other Punctuation 6
 
0.7%
Open Punctuation 3
 
0.3%
Close Punctuation 3
 
0.3%
Decimal Number 2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 135
18.1%
o 103
13.8%
g 85
11.4%
e 73
9.8%
a 72
9.7%
i 38
 
5.1%
u 36
 
4.8%
y 25
 
3.4%
k 20
 
2.7%
h 19
 
2.5%
Other values (15) 140
18.8%
Uppercase Letter
ValueCountFrequency (%)
S 27
22.9%
D 17
14.4%
J 11
9.3%
G 11
9.3%
B 8
 
6.8%
U 8
 
6.8%
C 5
 
4.2%
H 5
 
4.2%
O 5
 
4.2%
N 5
 
4.2%
Other values (9) 16
13.6%
Space Separator
ValueCountFrequency (%)
13
81.2%
  3
 
18.8%
Decimal Number
ValueCountFrequency (%)
3 1
50.0%
5 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 864
95.8%
Common 38
 
4.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 135
15.6%
o 103
11.9%
g 85
 
9.8%
e 73
 
8.4%
a 72
 
8.3%
i 38
 
4.4%
u 36
 
4.2%
S 27
 
3.1%
y 25
 
2.9%
k 20
 
2.3%
Other values (34) 250
28.9%
Common
ValueCountFrequency (%)
13
34.2%
- 8
21.1%
. 6
15.8%
( 3
 
7.9%
) 3
 
7.9%
  3
 
7.9%
3 1
 
2.6%
5 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 899
99.7%
None 3
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 135
15.0%
o 103
 
11.5%
g 85
 
9.5%
e 73
 
8.1%
a 72
 
8.0%
i 38
 
4.2%
u 36
 
4.0%
S 27
 
3.0%
y 25
 
2.8%
k 20
 
2.2%
Other values (41) 285
31.7%
None
ValueCountFrequency (%)
  3
100.0%

일본어
Text

Distinct98
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2023-12-12T12:53:08.377701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length4.8181818
Min length2

Characters and Unicode

Total characters477
Distinct characters70
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)98.0%

Sample

1st rowカヌン
2nd rowカサンデジタルダンジ
3rd rowカンソク
4th rowケボン
5th rowクァナク
ValueCountFrequency (%)
タンジョン 2
 
2.0%
ソンタン 1
 
1.0%
ウィワン 1
 
1.0%
ウォルゲ 1
 
1.0%
ヨンサン 1
 
1.0%
ウェデアプ 1
 
1.0%
オニャンオンチョン 1
 
1.0%
オンス 1
 
1.0%
オサンデ 1
 
1.0%
オサン 1
 
1.0%
Other values (88) 88
88.9%
2023-12-12T12:53:08.950333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
ン 114
23.9%
ョ 34
 
7.1%
チ 27
 
5.7%
ク 21
 
4.4%
ソ 15
 
3.1%
ト 15
 
3.1%
サ 14
 
2.9%
ジ 12
 
2.5%
ド 9
 
1.9%
ム 9
 
1.9%
Other values (60) 207
43.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 473
99.2%
Open Punctuation 2
 
0.4%
Close Punctuation 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ン 114
24.1%
ョ 34
 
7.2%
チ 27
 
5.7%
ク 21
 
4.4%
ソ 15
 
3.2%
ト 15
 
3.2%
サ 14
 
3.0%
ジ 12
 
2.5%
ド 9
 
1.9%
ム 9
 
1.9%
Other values (58) 203
42.9%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Katakana 466
97.7%
Han 7
 
1.5%
Common 4
 
0.8%

Most frequent character per script

Katakana
ValueCountFrequency (%)
ン 114
24.5%
ョ 34
 
7.3%
チ 27
 
5.8%
ク 21
 
4.5%
ソ 15
 
3.2%
ト 15
 
3.2%
サ 14
 
3.0%
ジ 12
 
2.6%
ド 9
 
1.9%
ム 9
 
1.9%
Other values (53) 196
42.1%
Han
ValueCountFrequency (%)
大 2
28.6%
学 2
28.6%
駅 1
14.3%
九 1
14.3%
老 1
14.3%
Common
ValueCountFrequency (%)
( 2
50.0%
) 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Katakana 466
97.7%
CJK 7
 
1.5%
ASCII 4
 
0.8%

Most frequent character per block

Katakana
ValueCountFrequency (%)
ン 114
24.5%
ョ 34
 
7.3%
チ 27
 
5.8%
ク 21
 
4.5%
ソ 15
 
3.2%
ト 15
 
3.2%
サ 14
 
3.0%
ジ 12
 
2.6%
ド 9
 
1.9%
ム 9
 
1.9%
Other values (53) 196
42.1%
ASCII
ValueCountFrequency (%)
( 2
50.0%
) 2
50.0%
CJK
ValueCountFrequency (%)
大 2
28.6%
学 2
28.6%
駅 1
14.3%
九 1
14.3%
老 1
14.3%

중국어간체
Text

UNIQUE 

Distinct99
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2023-12-12T12:53:09.471307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length2
Mean length2.7373737
Min length2

Characters and Unicode

Total characters271
Distinct characters161
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99 ?
Unique (%)100.0%

Sample

1st row佳陵
2nd row加山数码园区
3rd row间石驛
4th row开峰
5th row冠岳
ValueCountFrequency (%)
佳陵 1
 
1.0%
逍遙山 1
 
1.0%
义王 1
 
1.0%
月溪 1
 
1.0%
龙山 1
 
1.0%
韩国外国语大学 1
 
1.0%
温阳温泉 1
 
1.0%
温水 1
 
1.0%
乌山大学 1
 
1.0%
乌山 1
 
1.0%
Other values (89) 89
89.9%
2023-12-12T12:53:10.110988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
山 10
 
3.7%
大 9
 
3.3%
川 7
 
2.6%
学 7
 
2.6%
东 6
 
2.2%
井 5
 
1.8%
洞 5
 
1.8%
新 5
 
1.8%
道 4
 
1.5%
) 3
 
1.1%
Other values (151) 210
77.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 264
97.4%
Close Punctuation 3
 
1.1%
Open Punctuation 3
 
1.1%
Space Separator 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
山 10
 
3.8%
大 9
 
3.4%
川 7
 
2.7%
学 7
 
2.7%
东 6
 
2.3%
井 5
 
1.9%
洞 5
 
1.9%
新 5
 
1.9%
道 4
 
1.5%
浦 3
 
1.1%
Other values (148) 203
76.9%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Space Separator
ValueCountFrequency (%)
  1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 264
97.4%
Common 7
 
2.6%

Most frequent character per script

Han
ValueCountFrequency (%)
山 10
 
3.8%
大 9
 
3.4%
川 7
 
2.7%
学 7
 
2.7%
东 6
 
2.3%
井 5
 
1.9%
洞 5
 
1.9%
新 5
 
1.9%
道 4
 
1.5%
浦 3
 
1.1%
Other values (148) 203
76.9%
Common
ValueCountFrequency (%)
) 3
42.9%
( 3
42.9%
  1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
CJK 264
97.4%
ASCII 6
 
2.2%
None 1
 
0.4%

Most frequent character per block

CJK
ValueCountFrequency (%)
山 10
 
3.8%
大 9
 
3.4%
川 7
 
2.7%
学 7
 
2.7%
东 6
 
2.3%
井 5
 
1.9%
洞 5
 
1.9%
新 5
 
1.9%
道 4
 
1.5%
浦 3
 
1.1%
Other values (148) 203
76.9%
ASCII
ValueCountFrequency (%)
) 3
50.0%
( 3
50.0%
None
ValueCountFrequency (%)
  1
100.0%

중국어번체
Text

UNIQUE 

Distinct99
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2023-12-12T12:53:10.579947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length2
Mean length2.6666667
Min length2

Characters and Unicode

Total characters264
Distinct characters165
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99 ?
Unique (%)100.0%

Sample

1st row佳陵
2nd row加山디지털團地
3rd row間石
4th row開峰
5th row冠岳
ValueCountFrequency (%)
佳陵 1
 
1.0%
逍遙山 1
 
1.0%
義王 1
 
1.0%
月溪 1
 
1.0%
龍山 1
 
1.0%
外大앞 1
 
1.0%
溫陽溫泉 1
 
1.0%
溫水 1
 
1.0%
烏山大 1
 
1.0%
烏山 1
 
1.0%
Other values (89) 89
89.9%
2023-12-12T12:53:11.149754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
山 10
 
3.8%
大 9
 
3.4%
川 7
 
2.7%
東 6
 
2.3%
新 5
 
1.9%
井 5
 
1.9%
洞 5
 
1.9%
) 4
 
1.5%
道 4
 
1.5%
( 4
 
1.5%
Other values (155) 205
77.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 254
96.2%
Close Punctuation 4
 
1.5%
Open Punctuation 4
 
1.5%
Decimal Number 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
山 10
 
3.9%
大 9
 
3.5%
川 7
 
2.8%
東 6
 
2.4%
新 5
 
2.0%
井 5
 
2.0%
洞 5
 
2.0%
道 4
 
1.6%
西 3
 
1.2%
安 3
 
1.2%
Other values (151) 197
77.6%
Decimal Number
ValueCountFrequency (%)
3 1
50.0%
5 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 247
93.6%
Common 10
 
3.8%
Hangul 7
 
2.7%

Most frequent character per script

Han
ValueCountFrequency (%)
山 10
 
4.0%
大 9
 
3.6%
川 7
 
2.8%
東 6
 
2.4%
新 5
 
2.0%
井 5
 
2.0%
洞 5
 
2.0%
道 4
 
1.6%
西 3
 
1.2%
安 3
 
1.2%
Other values (145) 190
76.9%
Hangul
ValueCountFrequency (%)
앞 2
28.6%
서 1
14.3%
울 1
14.3%
털 1
14.3%
지 1
14.3%
디 1
14.3%
Common
ValueCountFrequency (%)
) 4
40.0%
( 4
40.0%
3 1
 
10.0%
5 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 240
90.9%
ASCII 10
 
3.8%
CJK Compat Ideographs 7
 
2.7%
Hangul 7
 
2.7%

Most frequent character per block

CJK
ValueCountFrequency (%)
山 10
 
4.2%
大 9
 
3.8%
川 7
 
2.9%
東 6
 
2.5%
新 5
 
2.1%
井 5
 
2.1%
洞 5
 
2.1%
道 4
 
1.7%
西 3
 
1.2%
安 3
 
1.2%
Other values (139) 183
76.2%
ASCII
ValueCountFrequency (%)
) 4
40.0%
( 4
40.0%
3 1
 
10.0%
5 1
 
10.0%
CJK Compat Ideographs
ValueCountFrequency (%)
龍 2
28.6%
勒 1
14.3%
綠 1
14.3%
鷺 1
14.3%
里 1
14.3%
陵 1
14.3%
Hangul
ValueCountFrequency (%)
앞 2
28.6%
서 1
14.3%
울 1
14.3%
털 1
14.3%
지 1
14.3%
디 1
14.3%

환승역여부
Boolean

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size231.0 B
False
72 
True
27 
ValueCountFrequency (%)
False 72
72.7%
True 27
 
27.3%
2023-12-12T12:53:11.317405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

신설일자
Real number (ℝ)

Distinct21
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19852163
Minimum19000708
Maximum20220607
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-12T12:53:11.469218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19000708
5-th percentile19740815
Q119740815
median19740815
Q320050120
95-th percentile20083110
Maximum20220607
Range1219899
Interquartile range (IQR)309305

Descriptive statistics

Standard deviation210226.04
Coefficient of variation (CV)0.010589579
Kurtosis4.4265764
Mean19852163
Median Absolute Deviation (MAD)0
Skewness-1.3315758
Sum1.9653641 × 109
Variance4.4194987 × 1010
MonotonicityNot monotonic
2023-12-12T12:53:11.661999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
19740815 50
50.5%
20050120 11
 
11.1%
20061225 6
 
6.1%
20081220 5
 
5.1%
19860902 5
 
5.1%
20061215 4
 
4.0%
19850425 3
 
3.0%
20030430 2
 
2.0%
20100121 1
 
1.0%
20220607 1
 
1.0%
Other values (11) 11
 
11.1%
ValueCountFrequency (%)
19000708 1
 
1.0%
19050101 1
 
1.0%
19111005 1
 
1.0%
19740815 50
50.5%
19800105 1
 
1.0%
19850425 3
 
3.0%
19860902 5
 
5.1%
19950216 1
 
1.0%
20030430 2
 
2.0%
20040401 1
 
1.0%
ValueCountFrequency (%)
20220607 1
 
1.0%
20211030 1
 
1.0%
20120118 1
 
1.0%
20100226 1
 
1.0%
20100121 1
 
1.0%
20081220 5
5.1%
20081215 1
 
1.0%
20061225 6
6.1%
20061215 4
4.0%
20051221 1
 
1.0%

Interactions

2023-12-12T12:53:04.621464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:53:11.802326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
철도운영기관명역명영어명로마자일본어중국어간체중국어번체환승역여부신설일자
철도운영기관명1.0001.0001.0001.0001.0001.0001.0000.4070.106
역명1.0001.0001.0001.0001.0001.0001.0001.0001.000
영어명1.0001.0001.0001.0001.0001.0001.0001.0001.000
로마자1.0001.0001.0001.0001.0001.0001.0001.0001.000
일본어1.0001.0001.0001.0001.0001.0001.0001.0001.000
중국어간체1.0001.0001.0001.0001.0001.0001.0001.0001.000
중국어번체1.0001.0001.0001.0001.0001.0001.0001.0001.000
환승역여부0.4071.0001.0001.0001.0001.0001.0001.0000.212
신설일자0.1061.0001.0001.0001.0001.0001.0000.2121.000
2023-12-12T12:53:11.973082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
환승역여부철도운영기관명
환승역여부1.0000.267
철도운영기관명0.2671.000
2023-12-12T12:53:12.111456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신설일자철도운영기관명환승역여부
신설일자1.0000.1320.284
철도운영기관명0.1321.0000.267
환승역여부0.2840.2671.000

Missing values

2023-12-12T12:53:04.759993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:53:04.945514image/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

철도운영기관명선명역명영어명로마자일본어중국어간체중국어번체환승역여부신설일자
0코레일1호선가능GaneungGaneungカヌン佳陵佳陵N20061215
1코레일1호선가산디지털단지Gasan Digital ComplexGasanDigital Complexカサンデジタルダンジ加山数码园区加山디지털團地Y19740815
2코레일1호선간석GanseokGanseokカンソク间石驛間石N19740815
3코레일1호선개봉GaebongGaebongケボン开峰開峰N19740815
4코레일1호선관악GwanakGwanakクァナク冠岳冠岳N19740815
5코레일1호선광명GwangmyeongGwangmyeongクァンミョン光明光明N20040401
6코레일1호선광운대Kwangwoon Univ.Kwangwoon Univ.クァンウンデ光云大学光云大Y19111005
7코레일1호선구로Guro StationGuro Station九老駅九老站九老N19740815
8코레일1호선구일GuilGuilクイル九一九一N19950216
9코레일1호선군포GunpoGunpoクンポ军浦軍浦N19740815
철도운영기관명선명역명영어명로마자일본어중국어간체중국어번체환승역여부신설일자
89서울교통공사1호선동대문DongdaemunDongdaemunトンデムン东大门東大門Y19740815
90서울교통공사1호선동묘앞DongmyoDongmyoトンミョアプ东庙東廟앞Y20051221
91서울교통공사1호선서울역Seoul StationSeoul Stationソウルヨク首尔站首爾(驛)Y19740815
92서울교통공사1호선시청City HallCity Hallシチョン市厅市廳Y19740815
93서울교통공사1호선신설동SinseoldongSinseol-dongシンソルトン新设洞新設洞Y19740815
94서울교통공사1호선제기동JegidongJegi-dongチェギドン祭基洞祭基洞N19740815
95서울교통공사1호선종각JonggakJonggakチョンガク钟阁鐘閣N19740815
96서울교통공사1호선종로3가Jongno 3(sam)gaJongno3-gaチョンノサムガ钟路三街鍾路3街Y19740815
97서울교통공사1호선종로5가Jongno 5(o)gaJongno5-gaチョンノオガ钟路五街鍾路5街N19740815
98서울교통공사1호선청량리(서울시립대입구)Cheongnyangni(University of Seoul)Cheongnyangni (SeoulSiripdaeip-gu)チョンニャンニ清凉里(首尔市立大学)淸凉里(서울市立大入口)Y19740815