Overview

Dataset statistics

Number of variables7
Number of observations784
Missing cells18
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.0 KiB
Average record size in memory56.2 B

Variable types

Text6
Categorical1

Dataset

Description전철역코드,전철역명,전철명명(영문),호선,외부코드,전철명명(중문),전철명명(일문)
Author서울교통공사
URLhttps://data.seoul.go.kr/dataList/OA-15442/S/1/datasetView.do

Alerts

전철명명(일문) has 15 (1.9%) missing valuesMissing
전철역코드 has unique valuesUnique
외부코드 has unique valuesUnique

Reproduction

Analysis started2024-05-11 04:22:27.864564
Analysis finished2024-05-11 04:22:30.777427
Duration2.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

전철역코드
Text

UNIQUE 

Distinct784
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
2024-05-11T04:22:31.540661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3136
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

Unique784 ?
Unique (%)100.0%

Sample

1st row1018
2nd row0150
3rd row1006
4th row1407
5th row1727
ValueCountFrequency (%)
1018 1
 
0.1%
1276 1
 
0.1%
1323 1
 
0.1%
1273 1
 
0.1%
1286 1
 
0.1%
1283 1
 
0.1%
1282 1
 
0.1%
1275 1
 
0.1%
1270 1
 
0.1%
1220 1
 
0.1%
Other values (774) 774
98.7%
2024-05-11T04:22:32.987551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 654
20.9%
2 521
16.6%
0 399
12.7%
4 329
10.5%
3 310
9.9%
5 235
 
7.5%
7 204
 
6.5%
8 197
 
6.3%
6 165
 
5.3%
9 113
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3127
99.7%
Uppercase Letter 9
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 654
20.9%
2 521
16.7%
0 399
12.8%
4 329
10.5%
3 310
9.9%
5 235
 
7.5%
7 204
 
6.5%
8 197
 
6.3%
6 165
 
5.3%
9 113
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
C 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3127
99.7%
Latin 9
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 654
20.9%
2 521
16.7%
0 399
12.8%
4 329
10.5%
3 310
9.9%
5 235
 
7.5%
7 204
 
6.5%
8 197
 
6.3%
6 165
 
5.3%
9 113
 
3.6%
Latin
ValueCountFrequency (%)
C 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3136
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 654
20.9%
2 521
16.6%
0 399
12.7%
4 329
10.5%
3 310
9.9%
5 235
 
7.5%
7 204
 
6.5%
8 197
 
6.3%
6 165
 
5.3%
9 113
 
3.6%

전철역명
Text

Distinct645
Distinct (%)82.3%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
2024-05-11T04:22:34.135380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length2
Mean length2.8596939
Min length2

Characters and Unicode

Total characters2242
Distinct characters306
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

Unique528 ?
Unique (%)67.3%

Sample

1st row석계
2nd row서울역
3rd row영등포
4th row온양온천
5th row두정
ValueCountFrequency (%)
김포공항 5
 
0.6%
왕십리 4
 
0.5%
서울역 4
 
0.5%
공덕 4
 
0.5%
청량리 4
 
0.5%
홍대입구 3
 
0.4%
상봉 3
 
0.4%
회기 3
 
0.4%
동대문역사문화공원 3
 
0.4%
종로3가 3
 
0.4%
Other values (635) 748
95.4%
2024-05-11T04:22:35.744774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 66
 
2.9%
산 60
 
2.7%
구 53
 
2.4%
신 51
 
2.3%
동 49
 
2.2%
천 48
 
2.1%
정 44
 
2.0%
청 41
 
1.8%
원 40
 
1.8%
지 33
 
1.5%
Other values (296) 1757
78.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2226
99.3%
Decimal Number 13
 
0.6%
Other Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 66
 
3.0%
산 60
 
2.7%
구 53
 
2.4%
신 51
 
2.3%
동 49
 
2.2%
천 48
 
2.2%
정 44
 
2.0%
청 41
 
1.8%
원 40
 
1.8%
지 33
 
1.5%
Other values (288) 1741
78.2%
Decimal Number
ValueCountFrequency (%)
3 5
38.5%
4 3
23.1%
1 2
 
15.4%
2 1
 
7.7%
9 1
 
7.7%
5 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
. 2
66.7%
? 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2226
99.3%
Common 16
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 66
 
3.0%
산 60
 
2.7%
구 53
 
2.4%
신 51
 
2.3%
동 49
 
2.2%
천 48
 
2.2%
정 44
 
2.0%
청 41
 
1.8%
원 40
 
1.8%
지 33
 
1.5%
Other values (288) 1741
78.2%
Common
ValueCountFrequency (%)
3 5
31.2%
4 3
18.8%
. 2
 
12.5%
1 2
 
12.5%
2 1
 
6.2%
9 1
 
6.2%
? 1
 
6.2%
5 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2226
99.3%
ASCII 16
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 66
 
3.0%
산 60
 
2.7%
구 53
 
2.4%
신 51
 
2.3%
동 49
 
2.2%
천 48
 
2.2%
정 44
 
2.0%
청 41
 
1.8%
원 40
 
1.8%
지 33
 
1.5%
Other values (288) 1741
78.2%
ASCII
ValueCountFrequency (%)
3 5
31.2%
4 3
18.8%
. 2
 
12.5%
1 2
 
12.5%
2 1
 
6.2%
9 1
 
6.2%
? 1
 
6.2%
5 1
 
6.2%

전철명명(영문)
Text

Distinct650
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
2024-05-11T04:22:36.923747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length34
Mean length9.747449
Min length3

Characters and Unicode

Total characters7642
Distinct characters65
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique537 ?
Unique (%)68.5%

Sample

1st rowSeokgye
2nd rowSeoul Station
3rd rowYeongdeungpo
4th rowOnyang oncheon
5th rowDujeong
ValueCountFrequency (%)
univ 34
 
3.2%
park 16
 
1.5%
office 15
 
1.4%
city 14
 
1.3%
incheon 11
 
1.0%
seoul 11
 
1.0%
hall 10
 
1.0%
market 9
 
0.9%
airport 9
 
0.9%
complex 8
 
0.8%
Other values (669) 912
86.9%
2024-05-11T04:22:38.863522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 985
 
12.9%
o 732
 
9.6%
a 693
 
9.1%
g 591
 
7.7%
e 579
 
7.6%
i 353
 
4.6%
u 308
 
4.0%
274
 
3.6%
s 204
 
2.7%
m 192
 
2.5%
Other values (55) 2731
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6203
81.2%
Uppercase Letter 1029
 
13.5%
Space Separator 274
 
3.6%
Dash Punctuation 46
 
0.6%
Other Punctuation 45
 
0.6%
Decimal Number 13
 
0.2%
Close Punctuation 10
 
0.1%
Open Punctuation 10
 
0.1%
Modifier Symbol 8
 
0.1%
Final Punctuation 4
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 985
15.9%
o 732
11.8%
a 693
11.2%
g 591
9.5%
e 579
9.3%
i 353
 
5.7%
u 308
 
5.0%
s 204
 
3.3%
m 192
 
3.1%
l 185
 
3.0%
Other values (15) 1381
22.3%
Uppercase Letter
ValueCountFrequency (%)
S 183
17.8%
G 120
11.7%
D 75
 
7.3%
C 72
 
7.0%
B 63
 
6.1%
H 59
 
5.7%
M 59
 
5.7%
J 55
 
5.3%
U 47
 
4.6%
Y 43
 
4.2%
Other values (14) 253
24.6%
Decimal Number
ValueCountFrequency (%)
3 5
38.5%
1 3
23.1%
4 2
 
15.4%
5 1
 
7.7%
2 1
 
7.7%
9 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
. 39
86.7%
' 4
 
8.9%
? 1
 
2.2%
, 1
 
2.2%
Space Separator
ValueCountFrequency (%)
274
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 8
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7232
94.6%
Common 410
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 985
13.6%
o 732
 
10.1%
a 693
 
9.6%
g 591
 
8.2%
e 579
 
8.0%
i 353
 
4.9%
u 308
 
4.3%
s 204
 
2.8%
m 192
 
2.7%
l 185
 
2.6%
Other values (39) 2410
33.3%
Common
ValueCountFrequency (%)
274
66.8%
- 46
 
11.2%
. 39
 
9.5%
) 10
 
2.4%
( 10
 
2.4%
` 8
 
2.0%
3 5
 
1.2%
’ 4
 
1.0%
' 4
 
1.0%
1 3
 
0.7%
Other values (6) 7
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7638
99.9%
Punctuation 4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 985
 
12.9%
o 732
 
9.6%
a 693
 
9.1%
g 591
 
7.7%
e 579
 
7.6%
i 353
 
4.6%
u 308
 
4.0%
274
 
3.6%
s 204
 
2.7%
m 192
 
2.5%
Other values (54) 2727
35.7%
Punctuation
ValueCountFrequency (%)
’ 4
100.0%

호선
Categorical

Distinct24
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
01호선
102 
수인분당선
63 
경의선
57 
05호선
56 
07호선
53 
Other values (19)
453 

Length

Max length7
Median length4
Mean length4.0522959
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01호선
2nd row01호선
3rd row01호선
4th row01호선
5th row01호선

Common Values

ValueCountFrequency (%)
01호선 102
13.0%
수인분당선 63
 
8.0%
경의선 57
 
7.3%
05호선 56
 
7.1%
07호선 53
 
6.8%
04호선 51
 
6.5%
02호선 51
 
6.5%
03호선 44
 
5.6%
06호선 39
 
5.0%
09호선 38
 
4.8%
Other values (14) 230
29.3%

Length

2024-05-11T04:22:39.483958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01호선 102
13.0%
수인분당선 63
 
8.0%
경의선 57
 
7.3%
05호선 56
 
7.1%
07호선 53
 
6.8%
04호선 51
 
6.5%
02호선 51
 
6.5%
03호선 44
 
5.6%
06호선 39
 
5.0%
09호선 38
 
4.8%
Other values (14) 230
29.3%

외부코드
Text

UNIQUE 

Distinct784
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
2024-05-11T04:22:40.976188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.4145408
Min length2

Characters and Unicode

Total characters2677
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique784 ?
Unique (%)100.0%

Sample

1st row120
2nd row133
3rd row139
4th rowP176
5th rowP168
ValueCountFrequency (%)
120 1
 
0.1%
k327 1
 
0.1%
p134 1
 
0.1%
k324 1
 
0.1%
k336 1
 
0.1%
k334 1
 
0.1%
k333 1
 
0.1%
k326 1
 
0.1%
k320 1
 
0.1%
k138 1
 
0.1%
Other values (774) 774
98.7%
2024-05-11T04:22:42.866330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 513
19.2%
2 392
14.6%
3 282
10.5%
4 251
9.4%
5 203
 
7.6%
0 155
 
5.8%
6 149
 
5.6%
7 141
 
5.3%
9 134
 
5.0%
K 130
 
4.9%
Other values (10) 327
12.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2311
86.3%
Uppercase Letter 353
 
13.2%
Dash Punctuation 13
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 513
22.2%
2 392
17.0%
3 282
12.2%
4 251
10.9%
5 203
 
8.8%
0 155
 
6.7%
6 149
 
6.4%
7 141
 
6.1%
9 134
 
5.8%
8 91
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
K 130
36.8%
P 71
20.1%
I 57
16.1%
S 32
 
9.1%
D 16
 
4.5%
Y 15
 
4.2%
U 15
 
4.2%
A 14
 
4.0%
X 3
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2324
86.8%
Latin 353
 
13.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 513
22.1%
2 392
16.9%
3 282
12.1%
4 251
10.8%
5 203
 
8.7%
0 155
 
6.7%
6 149
 
6.4%
7 141
 
6.1%
9 134
 
5.8%
8 91
 
3.9%
Latin
ValueCountFrequency (%)
K 130
36.8%
P 71
20.1%
I 57
16.1%
S 32
 
9.1%
D 16
 
4.5%
Y 15
 
4.2%
U 15
 
4.2%
A 14
 
4.0%
X 3
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 513
19.2%
2 392
14.6%
3 282
10.5%
4 251
9.4%
5 203
 
7.6%
0 155
 
5.8%
6 149
 
5.6%
7 141
 
5.3%
9 134
 
5.0%
K 130
 
4.9%
Other values (10) 327
12.2%

전철명명(중문)
Text

Distinct624
Distinct (%)79.9%
Missing3
Missing (%)0.4%
Memory size6.3 KiB
2024-05-11T04:22:43.980524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length2
Mean length3.006402
Min length2

Characters and Unicode

Total characters2348
Distinct characters444
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique517 ?
Unique (%)66.2%

Sample

1st row石溪
2nd row首?
3rd row永登浦
4th row???泉
5th row斗井
ValueCountFrequency (%)
25
 
3.2%
山 11
 
1.4%
大 9
 
1.1%
水 5
 
0.6%
江南 5
 
0.6%
江 5
 
0.6%
西 4
 
0.5%
富平 4
 
0.5%
洞 4
 
0.5%
新 4
 
0.5%
Other values (588) 711
90.3%
2024-05-11T04:22:45.634094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
? 542
 
23.1%
大 67
 
2.9%
山 59
 
2.5%
新 46
 
2.0%
川 37
 
1.6%
南 26
 
1.1%
谷 26
 
1.1%
浦 23
 
1.0%
市 23
 
1.0%
路 23
 
1.0%
Other values (434) 1476
62.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1759
74.9%
Other Punctuation 542
 
23.1%
Close Punctuation 18
 
0.8%
Open Punctuation 18
 
0.8%
Space Separator 6
 
0.3%
Uppercase Letter 3
 
0.1%
Decimal Number 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
大 67
 
3.8%
山 59
 
3.4%
新 46
 
2.6%
川 37
 
2.1%
南 26
 
1.5%
谷 26
 
1.5%
浦 23
 
1.3%
市 23
 
1.3%
路 23
 
1.3%
水 21
 
1.2%
Other values (426) 1408
80.0%
Uppercase Letter
ValueCountFrequency (%)
D 2
66.7%
P 1
33.3%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
1 1
50.0%
Other Punctuation
ValueCountFrequency (%)
? 542
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 1750
74.5%
Common 586
 
25.0%
Hangul 9
 
0.4%
Latin 3
 
0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
大 67
 
3.8%
山 59
 
3.4%
新 46
 
2.6%
川 37
 
2.1%
南 26
 
1.5%
谷 26
 
1.5%
浦 23
 
1.3%
市 23
 
1.3%
路 23
 
1.3%
水 21
 
1.2%
Other values (423) 1399
79.9%
Common
ValueCountFrequency (%)
? 542
92.5%
) 18
 
3.1%
( 18
 
3.1%
6
 
1.0%
2 1
 
0.2%
1 1
 
0.2%
Hangul
ValueCountFrequency (%)
쒧 5
55.6%
쎱 3
33.3%
씉 1
 
11.1%
Latin
ValueCountFrequency (%)
D 2
66.7%
P 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
CJK 1728
73.6%
ASCII 589
 
25.1%
CJK Compat Ideographs 22
 
0.9%
Hangul 9
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
? 542
92.0%
) 18
 
3.1%
( 18
 
3.1%
6
 
1.0%
D 2
 
0.3%
2 1
 
0.2%
1 1
 
0.2%
P 1
 
0.2%
CJK
ValueCountFrequency (%)
大 67
 
3.9%
山 59
 
3.4%
新 46
 
2.7%
川 37
 
2.1%
南 26
 
1.5%
谷 26
 
1.5%
浦 23
 
1.3%
市 23
 
1.3%
路 23
 
1.3%
水 21
 
1.2%
Other values (408) 1377
79.7%
Hangul
ValueCountFrequency (%)
쒧 5
55.6%
쎱 3
33.3%
씉 1
 
11.1%
CJK Compat Ideographs
ValueCountFrequency (%)
金 4
18.2%
梨 3
13.6%
女 2
 
9.1%
陵 2
 
9.1%
林 1
 
4.5%
臨 1
 
4.5%
龍 1
 
4.5%
良 1
 
4.5%
樂 1
 
4.5%
落 1
 
4.5%
Other values (5) 5
22.7%

전철명명(일문)
Text

MISSING 

Distinct651
Distinct (%)84.7%
Missing15
Missing (%)1.9%
Memory size6.3 KiB
2024-05-11T04:22:46.493442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length15
Mean length5.1456437
Min length2

Characters and Unicode

Total characters3957
Distinct characters154
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique550 ?
Unique (%)71.5%

Sample

1st rowソッケ
2nd rowソウル
3rd rowヨンドゥンポ
4th rowオニャンオンチョン
5th rowトゥジョン
ValueCountFrequency (%)
ワンシムニ 4
 
0.5%
コンドク 4
 
0.5%
サンボン 4
 
0.5%
チョンニャンニ 4
 
0.5%
スソ 3
 
0.4%
シチョン 3
 
0.4%
チョンノサムガ 3
 
0.4%
フェギ 3
 
0.4%
ホンデイック 3
 
0.4%
カンナム 3
 
0.4%
Other values (640) 738
95.6%
2024-05-11T04:22:47.914270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
ン 866
21.9%
ョ 247
 
6.2%
チ 208
 
5.3%
ク 159
 
4.0%
サ 119
 
3.0%
ム 114
 
2.9%
ル 107
 
2.7%
ジ 100
 
2.5%
シ 84
 
2.1%
ウ 83
 
2.1%
Other values (144) 1870
47.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3891
98.3%
Other Punctuation 50
 
1.3%
Close Punctuation 4
 
0.1%
Open Punctuation 4
 
0.1%
Space Separator 3
 
0.1%
Uppercase Letter 3
 
0.1%
Decimal Number 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ン 866
22.3%
ョ 247
 
6.3%
チ 208
 
5.3%
ク 159
 
4.1%
サ 119
 
3.1%
ム 114
 
2.9%
ル 107
 
2.7%
ジ 100
 
2.6%
シ 84
 
2.2%
ウ 83
 
2.1%
Other values (133) 1804
46.4%
Other Punctuation
ValueCountFrequency (%)
? 28
56.0%
· 20
40.0%
, 1
 
2.0%
. 1
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
D 2
66.7%
P 1
33.3%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
２ 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Katakana 3770
95.3%
Han 121
 
3.1%
Common 63
 
1.6%
Latin 3
 
0.1%

Most frequent character per script

Katakana
ValueCountFrequency (%)
ン 866
23.0%
ョ 247
 
6.6%
チ 208
 
5.5%
ク 159
 
4.2%
サ 119
 
3.2%
ム 114
 
3.0%
ル 107
 
2.8%
ジ 100
 
2.7%
シ 84
 
2.2%
ウ 83
 
2.2%
Other values (63) 1683
44.6%
Han
ValueCountFrequency (%)
市 6
 
5.0%
大 6
 
5.0%
川 5
 
4.1%
場 5
 
4.1%
仁 4
 
3.3%
山 4
 
3.3%
谷 4
 
3.3%
石 4
 
3.3%
黔 3
 
2.5%
港 3
 
2.5%
Other values (60) 77
63.6%
Common
ValueCountFrequency (%)
? 28
44.4%
· 20
31.7%
) 4
 
6.3%
( 4
 
6.3%
3
 
4.8%
, 1
 
1.6%
. 1
 
1.6%
1 1
 
1.6%
２ 1
 
1.6%
Latin
ValueCountFrequency (%)
D 2
66.7%
P 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Katakana 3770
95.3%
CJK 119
 
3.0%
ASCII 45
 
1.1%
None 21
 
0.5%
CJK Compat Ideographs 2
 
0.1%

Most frequent character per block

Katakana
ValueCountFrequency (%)
ン 866
23.0%
ョ 247
 
6.6%
チ 208
 
5.5%
ク 159
 
4.2%
サ 119
 
3.2%
ム 114
 
3.0%
ル 107
 
2.8%
ジ 100
 
2.7%
シ 84
 
2.2%
ウ 83
 
2.2%
Other values (63) 1683
44.6%
ASCII
ValueCountFrequency (%)
? 28
62.2%
) 4
 
8.9%
( 4
 
8.9%
3
 
6.7%
D 2
 
4.4%
, 1
 
2.2%
P 1
 
2.2%
. 1
 
2.2%
1 1
 
2.2%
None
ValueCountFrequency (%)
· 20
95.2%
２ 1
 
4.8%
CJK
ValueCountFrequency (%)
市 6
 
5.0%
大 6
 
5.0%
川 5
 
4.2%
場 5
 
4.2%
仁 4
 
3.4%
山 4
 
3.4%
谷 4
 
3.4%
石 4
 
3.4%
黔 3
 
2.5%
港 3
 
2.5%
Other values (58) 75
63.0%
CJK Compat Ideographs
ValueCountFrequency (%)
女 1
50.0%
陵 1
50.0%

Missing values

2024-05-11T04:22:29.495134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T04:22:30.135634image/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.
2024-05-11T04:22:30.563020image/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

전철역코드전철역명전철명명(영문)호선외부코드전철명명(중문)전철명명(일문)
01018석계Seokgye01호선120石溪ソッケ
10150서울역Seoul Station01호선133首?ソウル
21006영등포Yeongdeungpo01호선139永登浦ヨンドゥンポ
31407온양온천Onyang oncheon01호선P176???泉オニャンオンチョン
41727두정Dujeong01호선P168斗井トゥジョン
51720진위Jinwi01호선P161振威チヌィ
61005대방Daebang01호선137大方テバン
71910덕계Deokgye01호선106德溪トッケ
81809주안Juan01호선156朱安チュアン
91749서동탄Seodongtan01호선P157-1西??ソドンタン
전철역코드전철역명전철명명(영문)호선외부코드전철명명(중문)전철명명(일문)
7743121동수Dongsu인천선I121??トンス
7753120부평Bupyeong인천선I120富平プピョン
7763119부평시장Bupyeong Market인천선I119富平市場富平市場
7773118부평구청Bupyeong-gu Office인천선I118富平??プピョングチョン
7783117갈산Galsan인천선I117葛山カルサン
7793116작전Jakjeon인천선I116?田チャクチョン
7803110계양Gyeyang인천선I110桂?ケヤン
7813138국제업무지구Intl. Business District인천선I138??????ククチェオンムジグ
7823137센트럴파크Central Park인천선I137中央公?セントラルパ?ク
7833136인천대입구Incheon Nat'l Univ.인천선I136仁川大?インチョンデイック