Overview

Dataset statistics

Number of variables13
Number of observations707
Missing cells51
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory76.1 KiB
Average record size in memory110.2 B

Variable types

Text5
Categorical1
Numeric6
DateTime1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15066/F/1/datasetView.do

Alerts

인가대수 is highly overall correlated with 거리 and 1 other fieldsHigh correlation
배차간격 is highly overall correlated with 최소배차 and 1 other fieldsHigh correlation
거리 is highly overall correlated with 인가대수 and 2 other fieldsHigh correlation
운행시간 is highly overall correlated with 인가대수 and 1 other fieldsHigh correlation
최소배차 is highly overall correlated with 배차간격 and 1 other fieldsHigh correlation
최대배차 is highly overall correlated with 배차간격 and 1 other fieldsHigh correlation
유형 is highly overall correlated with 거리High correlation
운행시간 has 49 (6.9%) missing valuesMissing

Reproduction

Analysis started2024-05-10 22:55:13.476213
Analysis finished2024-05-10 22:55:31.143224
Duration17.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업체명
Text

Distinct208
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
2024-05-10T22:55:31.724204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length4.2602546
Min length3

Characters and Unicode

Total characters3012
Distinct characters169
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)9.2%

Sample

1st row보광교통
2nd row북부운수
3rd row북부운수
4th row대원여객
5th row한성여객
ValueCountFrequency (%)
공항리무진 20
 
2.8%
선진운수 20
 
2.8%
범일운수 14
 
2.0%
한성여객 14
 
2.0%
흥안운수 14
 
2.0%
한남여객 13
 
1.8%
한성운수 12
 
1.7%
북부운수 12
 
1.7%
도원교통 12
 
1.7%
대진여객 11
 
1.5%
Other values (198) 568
80.0%
2024-05-10T22:55:32.842793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
운 354
 
11.8%
수 350
 
11.6%
교 147
 
4.9%
통 147
 
4.9%
진 98
 
3.3%
성 87
 
2.9%
객 75
 
2.5%
여 73
 
2.4%
한 67
 
2.2%
신 57
 
1.9%
Other values (159) 1557
51.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2978
98.9%
Uppercase Letter 24
 
0.8%
Decimal Number 7
 
0.2%
Space Separator 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
운 354
 
11.9%
수 350
 
11.8%
교 147
 
4.9%
통 147
 
4.9%
진 98
 
3.3%
성 87
 
2.9%
객 75
 
2.5%
여 73
 
2.5%
한 67
 
2.2%
신 57
 
1.9%
Other values (154) 1523
51.1%
Uppercase Letter
ValueCountFrequency (%)
R 8
33.3%
T 8
33.3%
B 8
33.3%
Decimal Number
ValueCountFrequency (%)
3 7
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2978
98.9%
Latin 24
 
0.8%
Common 10
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
운 354
 
11.9%
수 350
 
11.8%
교 147
 
4.9%
통 147
 
4.9%
진 98
 
3.3%
성 87
 
2.9%
객 75
 
2.5%
여 73
 
2.5%
한 67
 
2.2%
신 57
 
1.9%
Other values (154) 1523
51.1%
Latin
ValueCountFrequency (%)
R 8
33.3%
T 8
33.3%
B 8
33.3%
Common
ValueCountFrequency (%)
3 7
70.0%
3
30.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2978
98.9%
ASCII 34
 
1.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
운 354
 
11.9%
수 350
 
11.8%
교 147
 
4.9%
통 147
 
4.9%
진 98
 
3.3%
성 87
 
2.9%
객 75
 
2.5%
여 73
 
2.5%
한 67
 
2.2%
신 57
 
1.9%
Other values (154) 1523
51.1%
ASCII
ValueCountFrequency (%)
R 8
23.5%
T 8
23.5%
B 8
23.5%
3 7
20.6%
3
 
8.8%

노선번호
Text

Distinct633
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
2024-05-10T22:55:33.779023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length3.9377652
Min length3

Characters and Unicode

Total characters2784
Distinct characters66
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique570 ?
Unique (%)80.6%

Sample

1st row0017
2nd row01A
3rd row01B
4th row0411
5th row100
ValueCountFrequency (%)
8101 5
 
0.7%
1143 4
 
0.6%
8221 4
 
0.6%
8561 3
 
0.4%
n61 3
 
0.4%
n75 3
 
0.4%
n37 3
 
0.4%
n73 2
 
0.3%
1162 2
 
0.3%
1165 2
 
0.3%
Other values (623) 676
95.6%
2024-05-10T22:55:35.200048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 486
17.5%
0 379
13.6%
2 265
9.5%
6 203
 
7.3%
3 200
 
7.2%
7 168
 
6.0%
5 168
 
6.0%
4 156
 
5.6%
8 74
 
2.7%
서 55
 
2.0%
Other values (56) 630
22.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2137
76.8%
Other Letter 571
 
20.5%
Uppercase Letter 61
 
2.2%
Dash Punctuation 15
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 55
 
9.6%
동 40
 
7.0%
강 33
 
5.8%
포 30
 
5.3%
성 30
 
5.3%
북 29
 
5.1%
로 25
 
4.4%
초 23
 
4.0%
대 23
 
4.0%
문 21
 
3.7%
Other values (38) 262
45.9%
Decimal Number
ValueCountFrequency (%)
1 486
22.7%
0 379
17.7%
2 265
12.4%
6 203
9.5%
3 200
9.4%
7 168
 
7.9%
5 168
 
7.9%
4 156
 
7.3%
8 74
 
3.5%
9 38
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
N 31
50.8%
A 8
 
13.1%
B 6
 
9.8%
U 4
 
6.6%
R 4
 
6.6%
O 4
 
6.6%
T 4
 
6.6%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2152
77.3%
Hangul 571
 
20.5%
Latin 61
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 55
 
9.6%
동 40
 
7.0%
강 33
 
5.8%
포 30
 
5.3%
성 30
 
5.3%
북 29
 
5.1%
로 25
 
4.4%
초 23
 
4.0%
대 23
 
4.0%
문 21
 
3.7%
Other values (38) 262
45.9%
Common
ValueCountFrequency (%)
1 486
22.6%
0 379
17.6%
2 265
12.3%
6 203
9.4%
3 200
9.3%
7 168
 
7.8%
5 168
 
7.8%
4 156
 
7.2%
8 74
 
3.4%
9 38
 
1.8%
Latin
ValueCountFrequency (%)
N 31
50.8%
A 8
 
13.1%
B 6
 
9.8%
U 4
 
6.6%
R 4
 
6.6%
O 4
 
6.6%
T 4
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2213
79.5%
Hangul 571
 
20.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 486
22.0%
0 379
17.1%
2 265
12.0%
6 203
9.2%
3 200
9.0%
7 168
 
7.6%
5 168
 
7.6%
4 156
 
7.0%
8 74
 
3.3%
9 38
 
1.7%
Other values (8) 76
 
3.4%
Hangul
ValueCountFrequency (%)
서 55
 
9.6%
동 40
 
7.0%
강 33
 
5.8%
포 30
 
5.3%
성 30
 
5.3%
북 29
 
5.1%
로 25
 
4.4%
초 23
 
4.0%
대 23
 
4.0%
문 21
 
3.7%
Other values (38) 262
45.9%

유형
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
마을
253 
지선
243 
간선
151 
공항
38 
광역
 
10
Other values (3)
 
12

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지선
2nd row순환
3rd row순환
4th row지선
5th row간선

Common Values

ValueCountFrequency (%)
마을 253
35.8%
지선 243
34.4%
간선 151
21.4%
공항 38
 
5.4%
광역 10
 
1.4%
동행 6
 
0.8%
관광 4
 
0.6%
순환 2
 
0.3%

Length

2024-05-10T22:55:35.717883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:55:36.102570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마을 253
35.8%
지선 243
34.4%
간선 151
21.4%
공항 38
 
5.4%
광역 10
 
1.4%
동행 6
 
0.8%
관광 4
 
0.6%
순환 2
 
0.3%

기점명
Text

Distinct356
Distinct (%)50.4%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
2024-05-10T22:55:36.840141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.2758133
Min length2

Characters and Unicode

Total characters3730
Distinct characters296
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

Unique220 ?
Unique (%)31.1%

Sample

1st row청암동
2nd row예장주차장
3rd row예장주차장
4th row용산차고지
5th row하계동
ValueCountFrequency (%)
복정역환승센터 19
 
2.7%
인천공항 16
 
2.3%
양천공영차고지 16
 
2.3%
장지공영차고지 11
 
1.6%
강동공영차고지 11
 
1.6%
은평차고지 11
 
1.6%
진관공영차고지 11
 
1.6%
우이동 10
 
1.4%
중랑공영차고지 10
 
1.4%
강동차고지 9
 
1.3%
Other values (346) 583
82.5%
2024-05-10T22:55:38.107735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 250
 
6.7%
지 198
 
5.3%
고 158
 
4.2%
차 150
 
4.0%
공 120
 
3.2%
역 108
 
2.9%
영 89
 
2.4%
아 61
 
1.6%
산 58
 
1.6%
파 57
 
1.5%
Other values (286) 2481
66.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3600
96.5%
Decimal Number 77
 
2.1%
Uppercase Letter 25
 
0.7%
Other Punctuation 21
 
0.6%
Open Punctuation 3
 
0.1%
Close Punctuation 3
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 250
 
6.9%
지 198
 
5.5%
고 158
 
4.4%
차 150
 
4.2%
공 120
 
3.3%
역 108
 
3.0%
영 89
 
2.5%
아 61
 
1.7%
산 58
 
1.6%
파 57
 
1.6%
Other values (263) 2351
65.3%
Uppercase Letter
ValueCountFrequency (%)
L 5
20.0%
T 5
20.0%
H 4
16.0%
C 2
 
8.0%
K 2
 
8.0%
A 2
 
8.0%
P 2
 
8.0%
G 1
 
4.0%
S 1
 
4.0%
E 1
 
4.0%
Decimal Number
ValueCountFrequency (%)
1 20
26.0%
7 15
19.5%
2 15
19.5%
4 7
 
9.1%
5 6
 
7.8%
3 6
 
7.8%
6 4
 
5.2%
0 2
 
2.6%
8 2
 
2.6%
Other Punctuation
ValueCountFrequency (%)
. 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3600
96.5%
Common 104
 
2.8%
Latin 26
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 250
 
6.9%
지 198
 
5.5%
고 158
 
4.4%
차 150
 
4.2%
공 120
 
3.3%
역 108
 
3.0%
영 89
 
2.5%
아 61
 
1.7%
산 58
 
1.6%
파 57
 
1.6%
Other values (263) 2351
65.3%
Common
ValueCountFrequency (%)
. 21
20.2%
1 20
19.2%
7 15
14.4%
2 15
14.4%
4 7
 
6.7%
5 6
 
5.8%
3 6
 
5.8%
6 4
 
3.8%
( 3
 
2.9%
) 3
 
2.9%
Other values (2) 4
 
3.8%
Latin
ValueCountFrequency (%)
L 5
19.2%
T 5
19.2%
H 4
15.4%
C 2
 
7.7%
K 2
 
7.7%
A 2
 
7.7%
P 2
 
7.7%
e 1
 
3.8%
G 1
 
3.8%
S 1
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3600
96.5%
ASCII 130
 
3.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 250
 
6.9%
지 198
 
5.5%
고 158
 
4.4%
차 150
 
4.2%
공 120
 
3.3%
역 108
 
3.0%
영 89
 
2.5%
아 61
 
1.7%
산 58
 
1.6%
파 57
 
1.6%
Other values (263) 2351
65.3%
ASCII
ValueCountFrequency (%)
. 21
16.2%
1 20
15.4%
7 15
11.5%
2 15
11.5%
4 7
 
5.4%
5 6
 
4.6%
3 6
 
4.6%
L 5
 
3.8%
T 5
 
3.8%
6 4
 
3.1%
Other values (13) 26
20.0%

종점명
Text

Distinct384
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
2024-05-10T22:55:38.863073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length4.6294201
Min length2

Characters and Unicode

Total characters3273
Distinct characters290
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique238 ?
Unique (%)33.7%

Sample

1st row이촌동
2nd row예장주차장
3rd row예장주차장
4th rowAT센터.양재꽃시장
5th row용산구청
ValueCountFrequency (%)
인천공항 15
 
2.1%
강남역 14
 
2.0%
서울역 13
 
1.8%
여의도 11
 
1.6%
석계역 9
 
1.3%
서소문 9
 
1.3%
양재역 9
 
1.3%
홍대입구역 8
 
1.1%
구로디지털단지역 7
 
1.0%
대방역 7
 
1.0%
Other values (374) 605
85.6%
2024-05-10T22:55:40.132267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 322
 
9.8%
동 120
 
3.7%
대 100
 
3.1%
구 80
 
2.4%
서 63
 
1.9%
지 63
 
1.9%
신 58
 
1.8%
리 51
 
1.6%
문 47
 
1.4%
공 45
 
1.4%
Other values (280) 2324
71.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3177
97.1%
Decimal Number 42
 
1.3%
Other Punctuation 24
 
0.7%
Uppercase Letter 20
 
0.6%
Close Punctuation 5
 
0.2%
Open Punctuation 5
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 322
 
10.1%
동 120
 
3.8%
대 100
 
3.1%
구 80
 
2.5%
서 63
 
2.0%
지 63
 
2.0%
신 58
 
1.8%
리 51
 
1.6%
문 47
 
1.5%
공 45
 
1.4%
Other values (257) 2228
70.1%
Uppercase Letter
ValueCountFrequency (%)
A 4
20.0%
T 3
15.0%
D 2
10.0%
Y 2
10.0%
M 2
10.0%
C 2
10.0%
P 1
 
5.0%
G 1
 
5.0%
L 1
 
5.0%
H 1
 
5.0%
Decimal Number
ValueCountFrequency (%)
2 9
21.4%
7 8
19.0%
1 5
11.9%
5 5
11.9%
3 5
11.9%
4 4
9.5%
6 3
 
7.1%
8 2
 
4.8%
9 1
 
2.4%
Other Punctuation
ValueCountFrequency (%)
. 24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3177
97.1%
Common 76
 
2.3%
Latin 20
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 322
 
10.1%
동 120
 
3.8%
대 100
 
3.1%
구 80
 
2.5%
서 63
 
2.0%
지 63
 
2.0%
신 58
 
1.8%
리 51
 
1.6%
문 47
 
1.5%
공 45
 
1.4%
Other values (257) 2228
70.1%
Common
ValueCountFrequency (%)
. 24
31.6%
2 9
 
11.8%
7 8
 
10.5%
) 5
 
6.6%
( 5
 
6.6%
1 5
 
6.6%
5 5
 
6.6%
3 5
 
6.6%
4 4
 
5.3%
6 3
 
3.9%
Other values (2) 3
 
3.9%
Latin
ValueCountFrequency (%)
A 4
20.0%
T 3
15.0%
D 2
10.0%
Y 2
10.0%
M 2
10.0%
C 2
10.0%
P 1
 
5.0%
G 1
 
5.0%
L 1
 
5.0%
H 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3177
97.1%
ASCII 96
 
2.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 322
 
10.1%
동 120
 
3.8%
대 100
 
3.1%
구 80
 
2.5%
서 63
 
2.0%
지 63
 
2.0%
신 58
 
1.8%
리 51
 
1.6%
문 47
 
1.5%
공 45
 
1.4%
Other values (257) 2228
70.1%
ASCII
ValueCountFrequency (%)
. 24
25.0%
2 9
 
9.4%
7 8
 
8.3%
) 5
 
5.2%
( 5
 
5.2%
1 5
 
5.2%
5 5
 
5.2%
3 5
 
5.2%
4 4
 
4.2%
A 4
 
4.2%
Other values (13) 22
22.9%

인가대수
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.120226
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2024-05-10T22:55:40.568417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median10
Q319
95-th percentile34
Maximum53
Range52
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.093387
Coefficient of variation (CV)0.76929976
Kurtosis1.0856467
Mean13.120226
Median Absolute Deviation (MAD)6
Skewness1.159521
Sum9276
Variance101.87646
MonotonicityNot monotonic
2024-05-10T22:55:41.121475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
3 52
 
7.4%
4 50
 
7.1%
5 42
 
5.9%
8 39
 
5.5%
7 39
 
5.5%
6 38
 
5.4%
9 36
 
5.1%
2 34
 
4.8%
12 26
 
3.7%
11 23
 
3.3%
Other values (38) 328
46.4%
ValueCountFrequency (%)
1 14
 
2.0%
2 34
4.8%
3 52
7.4%
4 50
7.1%
5 42
5.9%
6 38
5.4%
7 39
5.5%
8 39
5.5%
9 36
5.1%
10 21
3.0%
ValueCountFrequency (%)
53 2
0.3%
51 1
 
0.1%
50 1
 
0.1%
47 1
 
0.1%
46 2
0.3%
44 1
 
0.1%
42 1
 
0.1%
41 3
0.4%
40 3
0.4%
39 2
0.3%

배차간격
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.261669
Minimum0
Maximum245
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2024-05-10T22:55:41.575223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q110
median12
Q316
95-th percentile35
Maximum245
Range245
Interquartile range (IQR)6

Descriptive statistics

Standard deviation19.686588
Coefficient of variation (CV)1.210613
Kurtosis80.784335
Mean16.261669
Median Absolute Deviation (MAD)3
Skewness8.1569485
Sum11497
Variance387.56174
MonotonicityNot monotonic
2024-05-10T22:55:41.979767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
11 75
 
10.6%
10 71
 
10.0%
8 59
 
8.3%
15 56
 
7.9%
12 55
 
7.8%
9 44
 
6.2%
14 42
 
5.9%
13 35
 
5.0%
7 34
 
4.8%
16 30
 
4.2%
Other values (33) 206
29.1%
ValueCountFrequency (%)
0 1
 
0.1%
4 4
 
0.6%
5 10
 
1.4%
6 18
 
2.5%
7 34
4.8%
8 59
8.3%
9 44
6.2%
10 71
10.0%
11 75
10.6%
12 55
7.8%
ValueCountFrequency (%)
245 1
 
0.1%
240 1
 
0.1%
210 1
 
0.1%
200 1
 
0.1%
190 1
 
0.1%
95 1
 
0.1%
70 3
0.4%
65 3
0.4%
60 3
0.4%
55 2
0.3%

거리
Real number (ℝ)

HIGH CORRELATION 

Distinct108
Distinct (%)15.3%
Missing2
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean33.731915
Minimum1
Maximum220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2024-05-10T22:55:42.416404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q19
median24
Q346
95-th percentile90.6
Maximum220
Range219
Interquartile range (IQR)37

Descriptive statistics

Standard deviation36.211467
Coefficient of variation (CV)1.0735076
Kurtosis7.189613
Mean33.731915
Median Absolute Deviation (MAD)16
Skewness2.465902
Sum23781
Variance1311.2704
MonotonicityNot monotonic
2024-05-10T22:55:42.836474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 34
 
4.8%
8 30
 
4.2%
6 29
 
4.1%
12 26
 
3.7%
5 26
 
3.7%
11 24
 
3.4%
13 22
 
3.1%
10 21
 
3.0%
9 19
 
2.7%
4 19
 
2.7%
Other values (98) 455
64.4%
ValueCountFrequency (%)
1 1
 
0.1%
2 5
 
0.7%
3 15
2.1%
4 19
2.7%
5 26
3.7%
6 29
4.1%
7 34
4.8%
8 30
4.2%
9 19
2.7%
10 21
3.0%
ValueCountFrequency (%)
220 1
 
0.1%
204 1
 
0.1%
201 1
 
0.1%
196 1
 
0.1%
193 1
 
0.1%
190 1
 
0.1%
188 3
0.4%
184 2
0.3%
179 1
 
0.1%
168 2
0.3%

운행시간
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct145
Distinct (%)22.0%
Missing49
Missing (%)6.9%
Infinite0
Infinite (%)0.0%
Mean115.33891
Minimum12
Maximum290
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2024-05-10T22:55:43.303010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile27.85
Q148.25
median100
Q3180
95-th percentile240
Maximum290
Range278
Interquartile range (IQR)131.75

Descriptive statistics

Standard deviation74.750049
Coefficient of variation (CV)0.6480905
Kurtosis-1.1282964
Mean115.33891
Median Absolute Deviation (MAD)60
Skewness0.46242716
Sum75893
Variance5587.5699
MonotonicityNot monotonic
2024-05-10T22:55:43.753344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 29
 
4.1%
40 29
 
4.1%
60 28
 
4.0%
30 21
 
3.0%
210 19
 
2.7%
120 18
 
2.5%
180 17
 
2.4%
220 14
 
2.0%
36 13
 
1.8%
230 12
 
1.7%
Other values (135) 458
64.8%
(Missing) 49
 
6.9%
ValueCountFrequency (%)
12 1
 
0.1%
15 4
0.6%
18 6
0.8%
20 7
1.0%
21 1
 
0.1%
24 6
0.8%
25 3
0.4%
26 3
0.4%
27 2
 
0.3%
28 5
0.7%
ValueCountFrequency (%)
290 2
 
0.3%
285 1
 
0.1%
281 1
 
0.1%
280 3
 
0.4%
267 1
 
0.1%
265 2
 
0.3%
264 1
 
0.1%
260 8
1.1%
250 9
1.3%
245 2
 
0.3%

최소배차
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.537482
Minimum0
Maximum245
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2024-05-10T22:55:44.166649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q17
median9
Q312
95-th percentile25
Maximum245
Range245
Interquartile range (IQR)5

Descriptive statistics

Standard deviation17.994796
Coefficient of variation (CV)1.4352799
Kurtosis107.68101
Mean12.537482
Median Absolute Deviation (MAD)3
Skewness9.4466052
Sum8864
Variance323.81269
MonotonicityNot monotonic
2024-05-10T22:55:44.581786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
10 87
12.3%
7 85
12.0%
8 77
10.9%
9 65
9.2%
6 63
8.9%
5 58
8.2%
12 43
 
6.1%
15 36
 
5.1%
25 27
 
3.8%
20 24
 
3.4%
Other values (25) 142
20.1%
ValueCountFrequency (%)
0 1
 
0.1%
1 5
 
0.7%
2 2
 
0.3%
3 10
 
1.4%
4 14
 
2.0%
5 58
8.2%
6 63
8.9%
7 85
12.0%
8 77
10.9%
9 65
9.2%
ValueCountFrequency (%)
245 1
 
0.1%
240 1
 
0.1%
200 2
 
0.3%
70 1
 
0.1%
65 1
 
0.1%
60 5
0.7%
50 4
0.6%
40 5
0.7%
35 5
0.7%
30 6
0.8%

최대배차
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.758133
Minimum0
Maximum245
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2024-05-10T22:55:44.921723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.3
Q113
median17
Q322
95-th percentile40
Maximum245
Range245
Interquartile range (IQR)9

Descriptive statistics

Standard deviation20.952856
Coefficient of variation (CV)1.0093805
Kurtosis68.19467
Mean20.758133
Median Absolute Deviation (MAD)4
Skewness7.4200439
Sum14676
Variance439.02215
MonotonicityNot monotonic
2024-05-10T22:55:45.245108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
15 96
13.6%
20 77
 
10.9%
25 76
 
10.7%
12 65
 
9.2%
18 43
 
6.1%
14 37
 
5.2%
13 34
 
4.8%
10 33
 
4.7%
30 27
 
3.8%
17 24
 
3.4%
Other values (35) 195
27.6%
ValueCountFrequency (%)
0 1
 
0.1%
5 4
 
0.6%
6 3
 
0.4%
7 16
 
2.3%
8 12
 
1.7%
9 9
 
1.3%
10 33
4.7%
11 16
 
2.3%
12 65
9.2%
13 34
4.8%
ValueCountFrequency (%)
245 1
 
0.1%
240 1
 
0.1%
230 1
 
0.1%
220 1
 
0.1%
200 1
 
0.1%
120 1
 
0.1%
95 1
 
0.1%
90 1
 
0.1%
80 3
0.4%
70 4
0.6%

첫차시간
Text

Distinct54
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
2024-05-10T22:55:45.619472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters3535
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)2.1%

Sample

1st row05:15
2nd row06:30
3rd row06:30
4th row04:20
5th row04:00
ValueCountFrequency (%)
06:00 128
18.1%
04:30 104
14.7%
04:00 102
14.4%
04:20 44
 
6.2%
05:30 39
 
5.5%
05:00 35
 
5.0%
04:10 30
 
4.2%
05:50 27
 
3.8%
05:20 17
 
2.4%
05:40 14
 
2.0%
Other values (44) 167
23.6%
2024-05-10T22:55:46.319639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1605
45.4%
: 707
20.0%
4 379
 
10.7%
5 295
 
8.3%
3 222
 
6.3%
6 147
 
4.2%
2 106
 
3.0%
1 52
 
1.5%
7 14
 
0.4%
9 4
 
0.1%
Other values (2) 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2826
79.9%
Other Punctuation 707
 
20.0%
Space Separator 2
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1605
56.8%
4 379
 
13.4%
5 295
 
10.4%
3 222
 
7.9%
6 147
 
5.2%
2 106
 
3.8%
1 52
 
1.8%
7 14
 
0.5%
9 4
 
0.1%
8 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
: 707
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3535
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1605
45.4%
: 707
20.0%
4 379
 
10.7%
5 295
 
8.3%
3 222
 
6.3%
6 147
 
4.2%
2 106
 
3.0%
1 52
 
1.5%
7 14
 
0.4%
9 4
 
0.1%
Other values (2) 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3535
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1605
45.4%
: 707
20.0%
4 379
 
10.7%
5 295
 
8.3%
3 222
 
6.3%
6 147
 
4.2%
2 106
 
3.0%
1 52
 
1.5%
7 14
 
0.4%
9 4
 
0.1%
Other values (2) 4
 
0.1%

막차시간
Date

Distinct91
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
Minimum2024-05-10 00:00:00
Maximum2024-05-10 23:59:00
2024-05-10T22:55:46.677461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:47.115004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-05-10T22:55:26.838809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:16.508249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:18.381659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:20.337034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:22.305587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:24.692560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:27.117966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:16.765516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:18.699446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:20.646910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:22.762739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:24.961495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:28.246127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:17.030972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:19.045388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:20.953475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:23.169043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:25.368382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:28.562714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:17.412662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:19.377677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:21.334346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:23.460872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:25.730853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:28.889244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:17.740702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:19.747008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:21.634986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:23.819628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:26.070390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:29.265081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:18.063766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:20.022673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:21.936041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:24.379484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:26.470485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T22:55:47.378633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유형인가대수배차간격거리운행시간최소배차최대배차첫차시간막차시간
유형1.0000.4810.4990.7690.6560.4830.5050.8980.907
인가대수0.4811.0000.0000.6960.7390.1360.2180.6830.292
배차간격0.4990.0001.0000.7260.3330.9320.9860.7060.971
거리0.7690.6960.7261.0000.7830.7980.7360.8310.959
운행시간0.6560.7390.3330.7831.0000.3020.2540.7990.814
최소배차0.4830.1360.9320.7980.3021.0000.8550.7920.918
최대배차0.5050.2180.9860.7360.2540.8551.0000.6240.944
첫차시간0.8980.6830.7060.8310.7990.7920.6241.0000.982
막차시간0.9070.2920.9710.9590.8140.9180.9440.9821.000
2024-05-10T22:55:47.909954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인가대수배차간격거리운행시간최소배차최대배차유형
인가대수1.000-0.4020.6220.697-0.475-0.3360.256
배차간격-0.4021.0000.1360.0600.9130.9070.295
거리0.6220.1361.0000.9660.0600.1520.508
운행시간0.6970.0600.9661.000-0.0090.0820.387
최소배차-0.4750.9130.060-0.0091.0000.8120.322
최대배차-0.3360.9070.1520.0820.8121.0000.300
유형0.2560.2950.5080.3870.3220.3001.000

Missing values

2024-05-10T22:55:29.756332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T22:55:30.424042image/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.
2024-05-10T22:55:30.840967image/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

업체명노선번호유형기점명종점명인가대수배차간격거리운행시간최소배차최대배차첫차시간막차시간
0보광교통0017지선청암동이촌동1012126081305:1523:30
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