Overview

Dataset statistics

Number of variables15
Number of observations114
Missing cells204
Missing cells (%)11.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 KiB
Average record size in memory124.2 B

Variable types

Numeric3
Text7
Categorical4
DateTime1

Dataset

Description부산교통공사_도시철도역사정보_20200228
Author부산교통공사
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15043686

Alerts

운영기관명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
노선명 is highly overall correlated with 역번호 and 2 other fieldsHigh correlation
노선번호 is highly overall correlated with 역번호 and 2 other fieldsHigh correlation
역번호 is highly overall correlated with 역위도 and 2 other fieldsHigh correlation
역위도 is highly overall correlated with 역번호 and 2 other fieldsHigh correlation
환승역구분 is highly imbalanced (51.5%)Imbalance
환승노선번호 has 102 (89.5%) missing valuesMissing
환승노선명 has 102 (89.5%) missing valuesMissing
역번호 has unique valuesUnique
역사명 has unique valuesUnique
역위도 has unique valuesUnique
역경도 has unique valuesUnique
역사전화번호 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:42:13.567139
Analysis finished2023-12-10 16:42:16.270735
Duration2.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

역번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct114
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean235.98246
Minimum95
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:42:16.402660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile100.65
Q1123.25
median218.5
Q3303.75
95-th percentile409.35
Maximum2020
Range1925
Interquartile range (IQR)180.5

Descriptive statistics

Standard deviation194.96698
Coefficient of variation (CV)0.82619269
Kurtosis62.483538
Mean235.98246
Median Absolute Deviation (MAD)90.5
Skewness6.9053639
Sum26902
Variance38012.124
MonotonicityNot monotonic
2023-12-11T01:42:16.635466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95 1
 
0.9%
304 1
 
0.9%
302 1
 
0.9%
301 1
 
0.9%
243 1
 
0.9%
242 1
 
0.9%
241 1
 
0.9%
240 1
 
0.9%
239 1
 
0.9%
238 1
 
0.9%
Other values (104) 104
91.2%
ValueCountFrequency (%)
95 1
0.9%
96 1
0.9%
97 1
0.9%
98 1
0.9%
99 1
0.9%
100 1
0.9%
101 1
0.9%
102 1
0.9%
103 1
0.9%
104 1
0.9%
ValueCountFrequency (%)
2020 1
0.9%
414 1
0.9%
413 1
0.9%
412 1
0.9%
411 1
0.9%
410 1
0.9%
409 1
0.9%
408 1
0.9%
407 1
0.9%
406 1
0.9%

역사명
Text

UNIQUE 

Distinct114
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-11T01:42:16.974001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length3
Mean length3.9385965
Min length3

Characters and Unicode

Total characters449
Distinct characters140
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

Unique114 ?
Unique (%)100.0%

Sample

1st row다대포해수욕장
2nd row다대포항역
3rd row낫개역
4th row신장림역
5th row장림역
ValueCountFrequency (%)
다대포해수욕장 1
 
0.9%
망미역 1
 
0.9%
양산역 1
 
0.9%
남양산역 1
 
0.9%
부산대양산캠퍼스역 1
 
0.9%
증산역 1
 
0.9%
호포역 1
 
0.9%
금곡역 1
 
0.9%
동원역 1
 
0.9%
율리역 1
 
0.9%
Other values (104) 104
91.2%
2023-12-11T01:42:17.843933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 113
25.2%
산 19
 
4.2%
대 17
 
3.8%
동 11
 
2.4%
호 10
 
2.2%
) 9
 
2.0%
부 9
 
2.0%
선 9
 
2.0%
( 9
 
2.0%
장 9
 
2.0%
Other values (130) 234
52.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 422
94.0%
Close Punctuation 9
 
2.0%
Open Punctuation 9
 
2.0%
Decimal Number 9
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 113
26.8%
산 19
 
4.5%
대 17
 
4.0%
동 11
 
2.6%
호 10
 
2.4%
부 9
 
2.1%
선 9
 
2.1%
장 9
 
2.1%
포 8
 
1.9%
남 8
 
1.9%
Other values (124) 209
49.5%
Decimal Number
ValueCountFrequency (%)
3 4
44.4%
4 2
22.2%
2 2
22.2%
1 1
 
11.1%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 422
94.0%
Common 27
 
6.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 113
26.8%
산 19
 
4.5%
대 17
 
4.0%
동 11
 
2.6%
호 10
 
2.4%
부 9
 
2.1%
선 9
 
2.1%
장 9
 
2.1%
포 8
 
1.9%
남 8
 
1.9%
Other values (124) 209
49.5%
Common
ValueCountFrequency (%)
) 9
33.3%
( 9
33.3%
3 4
14.8%
4 2
 
7.4%
2 2
 
7.4%
1 1
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 422
94.0%
ASCII 27
 
6.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 113
26.8%
산 19
 
4.5%
대 17
 
4.0%
동 11
 
2.6%
호 10
 
2.4%
부 9
 
2.1%
선 9
 
2.1%
장 9
 
2.1%
포 8
 
1.9%
남 8
 
1.9%
Other values (124) 209
49.5%
ASCII
ValueCountFrequency (%)
) 9
33.3%
( 9
33.3%
3 4
14.8%
4 2
 
7.4%
2 2
 
7.4%
1 1
 
3.7%

노선번호
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
S2601
40 
S2602
38 
S2603
17 
S2604
14 
S4802
5 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS2601
2nd rowS2601
3rd rowS2601
4th rowS2601
5th rowS2601

Common Values

ValueCountFrequency (%)
S2601 40
35.1%
S2602 38
33.3%
S2603 17
14.9%
S2604 14
 
12.3%
S4802 5
 
4.4%

Length

2023-12-11T01:42:18.060637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:42:18.216778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
s2601 40
35.1%
s2602 38
33.3%
s2603 17
14.9%
s2604 14
 
12.3%
s4802 5
 
4.4%

노선명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
부산도시철도 2호선
43 
부산도시철도 1호선
40 
부산도시철도 3호선
17 
부산도시철도 4호선
14 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산도시철도 2호선 43
37.7%
부산도시철도 1호선 40
35.1%
부산도시철도 3호선 17
 
14.9%
부산도시철도 4호선 14
 
12.3%

Length

2023-12-11T01:42:18.378362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:42:18.539058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산도시철도 114
50.0%
2호선 43
 
18.9%
1호선 40
 
17.5%
3호선 17
 
7.5%
4호선 14
 
6.1%

영문역사명
Text

Distinct108
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-11T01:42:18.928164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length35.5
Mean length9.245614
Min length4

Characters and Unicode

Total characters1054
Distinct characters47
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique102 ?
Unique (%)89.5%

Sample

1st rowDadaepo Beach
2nd rowDadaepo Harbor
3rd rowNatgae
4th rowSinjangnim
5th rowJangnim
ValueCountFrequency (%)
univ 7
 
4.7%
busan 5
 
3.4%
nat''''''''l 4
 
2.7%
yeonsan 2
 
1.3%
seomyeon 2
 
1.3%
of 2
 
1.3%
dadaepo 2
 
1.3%
sports 2
 
1.3%
city 2
 
1.3%
pusan 2
 
1.3%
Other values (114) 119
79.9%
2023-12-11T01:42:19.526237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 134
 
12.7%
a 107
 
10.2%
o 94
 
8.9%
e 91
 
8.6%
g 64
 
6.1%
u 49
 
4.6%
' 40
 
3.8%
s 36
 
3.4%
35
 
3.3%
i 34
 
3.2%
Other values (37) 370
35.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 819
77.7%
Uppercase Letter 148
 
14.0%
Other Punctuation 48
 
4.6%
Space Separator 35
 
3.3%
Dash Punctuation 4
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 134
16.4%
a 107
13.1%
o 94
11.5%
e 91
11.1%
g 64
7.8%
u 49
 
6.0%
s 36
 
4.4%
i 34
 
4.2%
m 31
 
3.8%
l 25
 
3.1%
Other values (14) 154
18.8%
Uppercase Letter
ValueCountFrequency (%)
S 18
12.2%
D 18
12.2%
B 15
10.1%
G 15
10.1%
M 14
9.5%
N 13
8.8%
J 11
7.4%
C 9
6.1%
U 7
 
4.7%
Y 7
 
4.7%
Other values (9) 21
14.2%
Other Punctuation
ValueCountFrequency (%)
' 40
83.3%
. 8
 
16.7%
Space Separator
ValueCountFrequency (%)
35
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 967
91.7%
Common 87
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 134
13.9%
a 107
 
11.1%
o 94
 
9.7%
e 91
 
9.4%
g 64
 
6.6%
u 49
 
5.1%
s 36
 
3.7%
i 34
 
3.5%
m 31
 
3.2%
l 25
 
2.6%
Other values (33) 302
31.2%
Common
ValueCountFrequency (%)
' 40
46.0%
35
40.2%
. 8
 
9.2%
- 4
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1054
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 134
 
12.7%
a 107
 
10.2%
o 94
 
8.9%
e 91
 
8.6%
g 64
 
6.1%
u 49
 
4.6%
' 40
 
3.8%
s 36
 
3.4%
35
 
3.3%
i 34
 
3.2%
Other values (37) 370
35.1%

한자역사명
Text

Distinct109
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-11T01:42:19.896451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length2
Mean length2.745614
Min length1

Characters and Unicode

Total characters313
Distinct characters172
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique104 ?
Unique (%)91.2%

Sample

1st row多大浦海水浴場
2nd row多大浦港
3rd row羅 浦
4th row新長林
5th row長林
ValueCountFrequency (%)
蓮山 2
 
1.7%
水營 2
 
1.7%
美南 2
 
1.7%
山 2
 
1.7%
東萊 2
 
1.7%
德川 2
 
1.7%
釜岩 1
 
0.8%
釜山大梁山campus 1
 
0.8%
甑山 1
 
0.8%
開琴 1
 
0.8%
Other values (102) 102
86.4%
2023-12-11T01:42:20.335392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
山 21
 
6.7%
大 15
 
4.8%
釜 11
 
3.5%
浦 8
 
2.6%
南 8
 
2.6%
東 7
 
2.2%
谷 5
 
1.6%
德 5
 
1.6%
水 5
 
1.6%
場 4
 
1.3%
Other values (162) 224
71.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 292
93.3%
Lowercase Letter 13
 
4.2%
Space Separator 4
 
1.3%
Uppercase Letter 3
 
1.0%
Other Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
山 21
 
7.2%
大 15
 
5.1%
釜 11
 
3.8%
浦 8
 
2.7%
南 8
 
2.7%
東 7
 
2.4%
谷 5
 
1.7%
德 5
 
1.7%
水 5
 
1.7%
場 4
 
1.4%
Other values (149) 203
69.5%
Lowercase Letter
ValueCountFrequency (%)
t 2
15.4%
u 2
15.4%
m 2
15.4%
s 1
7.7%
p 1
7.7%
a 1
7.7%
y 1
7.7%
i 1
7.7%
n 1
7.7%
e 1
7.7%
Space Separator
ValueCountFrequency (%)
4
100.0%
Uppercase Letter
ValueCountFrequency (%)
C 3
100.0%
Other Punctuation
ValueCountFrequency (%)
· 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 292
93.3%
Latin 16
 
5.1%
Common 5
 
1.6%

Most frequent character per script

Han
ValueCountFrequency (%)
山 21
 
7.2%
大 15
 
5.1%
釜 11
 
3.8%
浦 8
 
2.7%
南 8
 
2.7%
東 7
 
2.4%
谷 5
 
1.7%
德 5
 
1.7%
水 5
 
1.7%
場 4
 
1.4%
Other values (149) 203
69.5%
Latin
ValueCountFrequency (%)
C 3
18.8%
t 2
12.5%
u 2
12.5%
m 2
12.5%
s 1
 
6.2%
p 1
 
6.2%
a 1
 
6.2%
y 1
 
6.2%
i 1
 
6.2%
n 1
 
6.2%
Common
ValueCountFrequency (%)
4
80.0%
· 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 281
89.8%
ASCII 20
 
6.4%
CJK Compat Ideographs 11
 
3.5%
None 1
 
0.3%

Most frequent character per block

CJK
ValueCountFrequency (%)
山 21
 
7.5%
大 15
 
5.3%
釜 11
 
3.9%
浦 8
 
2.8%
南 8
 
2.8%
東 7
 
2.5%
谷 5
 
1.8%
德 5
 
1.8%
水 5
 
1.8%
場 4
 
1.4%
Other values (141) 192
68.3%
ASCII
ValueCountFrequency (%)
4
20.0%
C 3
15.0%
t 2
10.0%
u 2
10.0%
m 2
10.0%
s 1
 
5.0%
p 1
 
5.0%
a 1
 
5.0%
y 1
 
5.0%
i 1
 
5.0%
Other values (2) 2
10.0%
CJK Compat Ideographs
ValueCountFrequency (%)
梁 3
27.3%
蓮 2
18.2%
栗 1
 
9.1%
樂 1
 
9.1%
老 1
 
9.1%
羅 1
 
9.1%
冷 1
 
9.1%
樂 1
 
9.1%
None
ValueCountFrequency (%)
· 1
100.0%

환승역구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
일반역
102 
환승역
12 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반역 102
89.5%
환승역 12
 
10.5%

Length

2023-12-11T01:42:20.457828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:42:20.538291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반역 102
89.5%
환승역 12
 
10.5%

환승노선번호
Text

MISSING 

Distinct10
Distinct (%)83.3%
Missing102
Missing (%)89.5%
Memory size1.0 KiB
2023-12-11T01:42:20.658814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters132
Distinct characters10
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

Unique8 ?
Unique (%)66.7%

Sample

1st rowS2601+S2602
2nd rowS2601+S2603
3rd rowS2602+S2603
4th rowS2602+S2601
5th rowS2602+L4801
ValueCountFrequency (%)
s2602+s2603 2
16.7%
s2603+s2602 2
16.7%
s2601+s2602 1
8.3%
s2601+s2603 1
8.3%
s2602+s2601 1
8.3%
s2602+l4801 1
8.3%
s2603+s2601 1
8.3%
s2603+s2604 1
8.3%
s2604+s2603 1
8.3%
s2604+s2601 1
8.3%
2023-12-11T01:42:20.916065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 30
22.7%
0 24
18.2%
S 23
17.4%
6 23
17.4%
+ 12
 
9.1%
3 8
 
6.1%
1 6
 
4.5%
4 4
 
3.0%
L 1
 
0.8%
8 1
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 96
72.7%
Uppercase Letter 24
 
18.2%
Math Symbol 12
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 30
31.2%
0 24
25.0%
6 23
24.0%
3 8
 
8.3%
1 6
 
6.2%
4 4
 
4.2%
8 1
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
S 23
95.8%
L 1
 
4.2%
Math Symbol
ValueCountFrequency (%)
+ 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 108
81.8%
Latin 24
 
18.2%

Most frequent character per script

Common
ValueCountFrequency (%)
2 30
27.8%
0 24
22.2%
6 23
21.3%
+ 12
 
11.1%
3 8
 
7.4%
1 6
 
5.6%
4 4
 
3.7%
8 1
 
0.9%
Latin
ValueCountFrequency (%)
S 23
95.8%
L 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 30
22.7%
0 24
18.2%
S 23
17.4%
6 23
17.4%
+ 12
 
9.1%
3 8
 
6.1%
1 6
 
4.5%
4 4
 
3.0%
L 1
 
0.8%
8 1
 
0.8%

환승노선명
Text

MISSING 

Distinct10
Distinct (%)83.3%
Missing102
Missing (%)89.5%
Memory size1.0 KiB
2023-12-11T01:42:21.056401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length21
Mean length20.583333
Min length16

Characters and Unicode

Total characters247
Distinct characters17
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

Unique8 ?
Unique (%)66.7%

Sample

1st row부산도시철도 1호선+부산도시철도 2호선
2nd row부산도시철도 1호선+부산도시철도 3호선
3rd row부산도시철도 2호선+부산도시철도 3호선
4th row부산도시철도 2호선+부산도시철도 1호선
5th row부산도시철도 2호선+김해경전철
ValueCountFrequency (%)
부산도시철도 12
34.3%
3호선 4
 
11.4%
3호선+부산도시철도 4
 
11.4%
2호선+부산도시철도 3
 
8.6%
2호선 3
 
8.6%
1호선 3
 
8.6%
1호선+부산도시철도 2
 
5.7%
4호선+부산도시철도 2
 
5.7%
2호선+김해경전철 1
 
2.9%
4호선 1
 
2.9%
2023-12-11T01:42:21.359269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 46
18.6%
철 24
9.7%
부 23
9.3%
호 23
9.3%
산 23
9.3%
선 23
9.3%
23
9.3%
시 23
9.3%
+ 12
 
4.9%
3 8
 
3.2%
Other values (7) 19
7.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 189
76.5%
Space Separator 23
 
9.3%
Decimal Number 23
 
9.3%
Math Symbol 12
 
4.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 46
24.3%
철 24
12.7%
부 23
12.2%
호 23
12.2%
산 23
12.2%
선 23
12.2%
시 23
12.2%
김 1
 
0.5%
해 1
 
0.5%
경 1
 
0.5%
Decimal Number
ValueCountFrequency (%)
3 8
34.8%
2 7
30.4%
1 5
21.7%
4 3
 
13.0%
Space Separator
ValueCountFrequency (%)
23
100.0%
Math Symbol
ValueCountFrequency (%)
+ 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 189
76.5%
Common 58
 
23.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 46
24.3%
철 24
12.7%
부 23
12.2%
호 23
12.2%
산 23
12.2%
선 23
12.2%
시 23
12.2%
김 1
 
0.5%
해 1
 
0.5%
경 1
 
0.5%
Common
ValueCountFrequency (%)
23
39.7%
+ 12
20.7%
3 8
 
13.8%
2 7
 
12.1%
1 5
 
8.6%
4 3
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 189
76.5%
ASCII 58
 
23.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 46
24.3%
철 24
12.7%
부 23
12.2%
호 23
12.2%
산 23
12.2%
선 23
12.2%
시 23
12.2%
김 1
 
0.5%
해 1
 
0.5%
경 1
 
0.5%
ASCII
ValueCountFrequency (%)
23
39.7%
+ 12
20.7%
3 8
 
13.8%
2 7
 
12.1%
1 5
 
8.6%
4 3
 
5.2%

역위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct114
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.179012
Minimum35.048621
Maximum35.338728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:42:21.492820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.048621
5-th percentile35.093349
Q135.143059
median35.173629
Q335.212894
95-th percentile35.27566
Maximum35.338728
Range0.29010673
Interquartile range (IQR)0.06983575

Descriptive statistics

Standard deviation0.057755883
Coefficient of variation (CV)0.001641771
Kurtosis0.12826207
Mean35.179012
Median Absolute Deviation (MAD)0.0378365
Skewness0.22740458
Sum4010.4074
Variance0.003335742
MonotonicityNot monotonic
2023-12-11T01:42:21.634429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.089951 1
 
0.9%
35.176808 1
 
0.9%
35.171528 1
 
0.9%
35.167753 1
 
0.9%
35.33872773 1
 
0.9%
35.325359 1
 
0.9%
35.316955 1
 
0.9%
35.308302 1
 
0.9%
35.280406 1
 
0.9%
35.267248 1
 
0.9%
Other values (104) 104
91.2%
ValueCountFrequency (%)
35.048621 1
0.9%
35.057419 1
0.9%
35.065265 1
0.9%
35.074433 1
0.9%
35.081631 1
0.9%
35.089951 1
0.9%
35.095179 1
0.9%
35.097372 1
0.9%
35.097953 1
0.9%
35.099816 1
0.9%
ValueCountFrequency (%)
35.33872773 1
0.9%
35.325359 1
0.9%
35.316955 1
0.9%
35.308302 1
0.9%
35.28468657 1
0.9%
35.280406 1
0.9%
35.273105 1
0.9%
35.267248 1
0.9%
35.265404 1
0.9%
35.258656 1
0.9%

역경도
Real number (ℝ)

UNIQUE 

Distinct114
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.0546
Minimum128.96056
Maximum129.17699
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:42:21.796906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.96056
5-th percentile128.97295
Q1129.0122
median129.05945
Q3129.09197
95-th percentile129.15004
Maximum129.17699
Range0.216422
Interquartile range (IQR)0.07977

Descriptive statistics

Standard deviation0.054910022
Coefficient of variation (CV)0.00042547899
Kurtosis-0.80875508
Mean129.0546
Median Absolute Deviation (MAD)0.0424528
Skewness0.17720129
Sum14712.225
Variance0.0030151105
MonotonicityNot monotonic
2023-12-11T01:42:21.999593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.973971 1
 
0.9%
129.085748 1
 
0.9%
129.108225 1
 
0.9%
129.11459 1
 
0.9%
129.026391 1
 
0.9%
129.019457 1
 
0.9%
129.0139408 1
 
0.9%
129.010246 1
 
0.9%
129.017097 1
 
0.9%
129.0169054 1
 
0.9%
Other values (104) 104
91.2%
ValueCountFrequency (%)
128.960564 1
0.9%
128.9610488 1
0.9%
128.965908 1
0.9%
128.966803 1
0.9%
128.9696515 1
0.9%
128.971279 1
0.9%
128.973846 1
0.9%
128.973971 1
0.9%
128.977041 1
0.9%
128.977636 1
0.9%
ValueCountFrequency (%)
129.176986 1
0.9%
129.171823 1
0.9%
129.168604 1
0.9%
129.1604437 1
0.9%
129.158908 1
0.9%
129.154024 1
0.9%
129.147897 1
0.9%
129.146149 1
0.9%
129.138933 1
0.9%
129.137179 1
0.9%

운영기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
부산교통공사
114 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산교통공사 114
100.0%

Length

2023-12-11T01:42:22.151650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:42:22.280102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산교통공사 114
100.0%

역사도로명주소
Text

Distinct109
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-11T01:42:22.699123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length23
Mean length20.289474
Min length16

Characters and Unicode

Total characters2313
Distinct characters82
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

Unique104 ?
Unique (%)91.2%

Sample

1st row부산광역시 사하구 다대로 지하 692
2nd row부산광역시 사하구 다대로 지하 548
3rd row부산광역시 사하구 다대로 지하 422
4th row부산광역시 사하구 다대로 지하 310
5th row부산광역시 사하구 다대로 지하 230
ValueCountFrequency (%)
부산광역시 109
19.7%
지하 89
 
16.1%
중앙대로 21
 
3.8%
북구 13
 
2.4%
사하구 12
 
2.2%
동래구 11
 
2.0%
해운대구 10
 
1.8%
수영로 10
 
1.8%
부산진구 10
 
1.8%
반송로 9
 
1.6%
Other values (148) 258
46.7%
2023-12-11T01:42:23.460685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
438
18.9%
산 126
 
5.4%
부 119
 
5.1%
시 118
 
5.1%
로 114
 
4.9%
구 111
 
4.8%
광 109
 
4.7%
역 109
 
4.7%
하 102
 
4.4%
지 89
 
3.8%
Other values (72) 878
38.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1522
65.8%
Space Separator 438
 
18.9%
Decimal Number 348
 
15.0%
Dash Punctuation 5
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 126
 
8.3%
부 119
 
7.8%
시 118
 
7.8%
로 114
 
7.5%
구 111
 
7.3%
광 109
 
7.2%
역 109
 
7.2%
하 102
 
6.7%
지 89
 
5.8%
대 80
 
5.3%
Other values (60) 445
29.2%
Decimal Number
ValueCountFrequency (%)
1 65
18.7%
2 53
15.2%
0 41
11.8%
4 35
10.1%
3 32
9.2%
7 32
9.2%
9 25
 
7.2%
6 24
 
6.9%
5 21
 
6.0%
8 20
 
5.7%
Space Separator
ValueCountFrequency (%)
438
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1522
65.8%
Common 791
34.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 126
 
8.3%
부 119
 
7.8%
시 118
 
7.8%
로 114
 
7.5%
구 111
 
7.3%
광 109
 
7.2%
역 109
 
7.2%
하 102
 
6.7%
지 89
 
5.8%
대 80
 
5.3%
Other values (60) 445
29.2%
Common
ValueCountFrequency (%)
438
55.4%
1 65
 
8.2%
2 53
 
6.7%
0 41
 
5.2%
4 35
 
4.4%
3 32
 
4.0%
7 32
 
4.0%
9 25
 
3.2%
6 24
 
3.0%
5 21
 
2.7%
Other values (2) 25
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1522
65.8%
ASCII 791
34.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
438
55.4%
1 65
 
8.2%
2 53
 
6.7%
0 41
 
5.2%
4 35
 
4.4%
3 32
 
4.0%
7 32
 
4.0%
9 25
 
3.2%
6 24
 
3.0%
5 21
 
2.7%
Other values (2) 25
 
3.2%
Hangul
ValueCountFrequency (%)
산 126
 
8.3%
부 119
 
7.8%
시 118
 
7.8%
로 114
 
7.5%
구 111
 
7.3%
광 109
 
7.2%
역 109
 
7.2%
하 102
 
6.7%
지 89
 
5.8%
대 80
 
5.3%
Other values (60) 445
29.2%

역사전화번호
Text

UNIQUE 

Distinct114
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-11T01:42:23.862207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1368
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

Unique114 ?
Unique (%)100.0%

Sample

1st row051-678-6195
2nd row051-678-6196
3rd row051-678-6197
4th row051-678-6198
5th row051-618-6199
ValueCountFrequency (%)
051-678-6195 1
 
0.9%
051-678-6302 1
 
0.9%
051-678-6243 1
 
0.9%
051-678-6242 1
 
0.9%
051-678-6241 1
 
0.9%
051-678-6240 1
 
0.9%
051-678-6239 1
 
0.9%
051-678-6238 1
 
0.9%
051-678-6237 1
 
0.9%
051-678-6236 1
 
0.9%
Other values (104) 104
91.2%
2023-12-11T01:42:24.433633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 239
17.5%
- 228
16.7%
1 202
14.8%
0 161
11.8%
5 125
9.1%
8 124
9.1%
7 123
9.0%
2 76
 
5.6%
3 45
 
3.3%
4 30
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1140
83.3%
Dash Punctuation 228
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 239
21.0%
1 202
17.7%
0 161
14.1%
5 125
11.0%
8 124
10.9%
7 123
10.8%
2 76
 
6.7%
3 45
 
3.9%
4 30
 
2.6%
9 15
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 228
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1368
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 239
17.5%
- 228
16.7%
1 202
14.8%
0 161
11.8%
5 125
9.1%
8 124
9.1%
7 123
9.0%
2 76
 
5.6%
3 45
 
3.3%
4 30
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1368
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 239
17.5%
- 228
16.7%
1 202
14.8%
0 161
11.8%
5 125
9.1%
8 124
9.1%
7 123
9.0%
2 76
 
5.6%
3 45
 
3.3%
4 30
 
2.2%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
Minimum2020-02-28 00:00:00
Maximum2020-02-28 00:00:00
2023-12-11T01:42:24.607841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:42:24.738957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-11T01:42:15.165981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:42:14.361487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:42:14.758667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:42:15.301491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:42:14.509482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:42:14.907737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:42:15.495710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:42:14.653861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:42:15.051428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:42:24.864079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역번호노선번호노선명환승역구분환승노선번호환승노선명역위도역경도
역번호1.0000.7120.6720.0721.0001.0000.5970.403
노선번호0.7121.0001.0000.0831.0001.0000.9110.621
노선명0.6721.0001.0000.1831.0001.0000.7140.500
환승역구분0.0720.0830.1831.000NaNNaN0.1070.239
환승노선번호1.0001.0001.000NaN1.0001.0000.4650.000
환승노선명1.0001.0001.000NaN1.0001.0000.4650.000
역위도0.5970.9110.7140.1070.4650.4651.0000.706
역경도0.4030.6210.5000.2390.0000.0000.7061.000
2023-12-11T01:42:25.036595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
환승역구분노선명노선번호
환승역구분1.0000.1190.099
노선명0.1191.0000.995
노선번호0.0990.9951.000
2023-12-11T01:42:25.150533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역번호역위도역경도노선번호노선명환승역구분
역번호1.0000.6600.2980.6930.6980.118
역위도0.6601.0000.2830.5930.5020.074
역경도0.2980.2831.0000.3010.3170.179
노선번호0.6930.5930.3011.0000.9950.099
노선명0.6980.5020.3170.9951.0000.119
환승역구분0.1180.0740.1790.0990.1191.000

Missing values

2023-12-11T01:42:15.693381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:42:15.992921image/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.
2023-12-11T01:42:16.164882image/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

역번호역사명노선번호노선명영문역사명한자역사명환승역구분환승노선번호환승노선명역위도역경도운영기관명역사도로명주소역사전화번호데이터기준일자
095다대포해수욕장S2601부산도시철도 1호선Dadaepo Beach多大浦海水浴場일반역<NA><NA>35.089951128.973971부산교통공사부산광역시 사하구 다대로 지하 692051-678-61952020-02-28
196다대포항역S2601부산도시철도 1호선Dadaepo Harbor多大浦港일반역<NA><NA>35.081631128.977636부산교통공사부산광역시 사하구 다대로 지하 548051-678-61962020-02-28
297낫개역S2601부산도시철도 1호선Natgae羅 浦일반역<NA><NA>35.074433128.977041부산교통공사부산광역시 사하구 다대로 지하 422051-678-61972020-02-28
398신장림역S2601부산도시철도 1호선Sinjangnim新長林일반역<NA><NA>35.065265128.979873부산교통공사부산광역시 사하구 다대로 지하 310051-678-61982020-02-28
499장림역S2601부산도시철도 1호선Jangnim長林일반역<NA><NA>35.057419128.971279부산교통공사부산광역시 사하구 다대로 지하 230051-618-61992020-02-28
5100동매역S2601부산도시철도 1호선Dongmae東 山일반역<NA><NA>35.048621128.965908부산교통공사부산광역시 사하구 신산로 지하 168051-618-61002020-02-28
6101신평역S2601부산도시철도 1호선Sinpyeong新平일반역<NA><NA>35.095179128.960564부산교통공사부산광역시 사하구 하신번영로 140051-678-61012020-02-28
7102하단역S2601부산도시철도 1호선Hadan下端일반역<NA><NA>35.10618128.966803부산교통공사부산광역시 사하구 낙동남로 지하 1415051-678-61022020-02-28
8103당리역S2601부산도시철도 1호선Dangni堂里일반역<NA><NA>35.103532128.973846부산교통공사부산광역시 사하구 낙동대로 지하 405051-678-61032020-02-28
9104사하역S2601부산도시철도 1호선Saha沙下일반역<NA><NA>35.099847128.9831부산교통공사부산광역시 사하구 낙동대로 지하 309051-678-61042020-02-28
역번호역사명노선번호노선명영문역사명한자역사명환승역구분환승노선번호환승노선명역위도역경도운영기관명역사도로명주소역사전화번호데이터기준일자
104405충렬사역S2604부산도시철도 4호선Chungnyeolsa忠烈祠일반역<NA><NA>35.199859129.097636부산교통공사부산광역시 동래구 반송로 지하 205051-678-64052020-02-28
105406명장역S2604부산도시철도 4호선Myeongjang鳴藏일반역<NA><NA>35.205143129.101517부산교통공사부산광역시 동래구 반송로 지하 281051-678-64062020-02-28
106407서동역S2604부산도시철도 4호선Seo-dong書洞일반역<NA><NA>35.213333129.107683부산교통공사부산광역시 금정구 반송로 지하 387051-678-64072020-02-28
107408금사역S2604부산도시철도 4호선Geumsa錦絲일반역<NA><NA>35.215829129.115153부산교통공사부산광역시 금정구 반송로 지하 465051-678-64082020-02-28
108409반여농산물시장역S2604부산도시철도 4호선Banyeo Agricultural Market盤如農産物市場일반역<NA><NA>35.217779129.124061부산교통공사부산광역시 해운대구 반송로 550051-678-64092020-02-28
109410석대역S2604부산도시철도 4호선Seokdae石坮일반역<NA><NA>35.218112129.137179부산교통공사부산광역시 해운대구 석대천로 121051-678-64102020-02-28
110411영산대역S2604부산도시철도 4호선Youngsan Univ.靈山大일반역<NA><NA>35.225777129.146149부산교통공사부산광역시 해운대구 반송로 803051-678-64112020-02-28
111412동부산대학역S2604부산도시철도 4호선Dong-Pusan College東釜山大學일반역<NA><NA>35.232506129.154024부산교통공사부산광역시 해운대구 반송로 917051-678-64122020-02-28
112413고촌역S2604부산도시철도 4호선Gochon古村일반역<NA><NA>35.236031129.160444부산교통공사부산광역시 기장군 철마면 반송로 991051-678-64132020-02-28
113414안평역S2604부산도시철도 4호선Anpyeong安平일반역<NA><NA>35.237363129.171823부산교통공사부산광역시 기장군 철마면 반송로 1101051-678-64142020-02-28