Overview

Dataset statistics

Number of variables13
Number of observations706
Missing cells52
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory76.0 KiB
Average record size in memory110.2 B

Variable types

Text4
Categorical1
Numeric6
DateTime2

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 50 (7.1%) missing valuesMissing

Reproduction

Analysis started2024-05-10 22:55:52.546232
Analysis finished2024-05-10 22:56:06.746157
Duration14.2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업체명
Text

Distinct208
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-05-10T22:56:07.480848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length4.2606232
Min length3

Characters and Unicode

Total characters3008
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 (%)
선진운수 21
 
3.0%
공항리무진 20
 
2.8%
범일운수 14
 
2.0%
한성여객 14
 
2.0%
흥안운수 13
 
1.8%
한남여객 13
 
1.8%
한성운수 12
 
1.7%
북부운수 12
 
1.7%
대진여객 11
 
1.6%
도원교통 11
 
1.6%
Other values (198) 568
80.1%
2024-05-10T22:56:08.920004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
운 355
 
11.8%
수 351
 
11.7%
교 146
 
4.9%
통 146
 
4.9%
진 99
 
3.3%
성 87
 
2.9%
객 73
 
2.4%
여 71
 
2.4%
한 67
 
2.2%
신 59
 
2.0%
Other values (159) 1554
51.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2974
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 355
 
11.9%
수 351
 
11.8%
교 146
 
4.9%
통 146
 
4.9%
진 99
 
3.3%
성 87
 
2.9%
객 73
 
2.5%
여 71
 
2.4%
한 67
 
2.3%
신 59
 
2.0%
Other values (154) 1520
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 2974
98.9%
Latin 24
 
0.8%
Common 10
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
운 355
 
11.9%
수 351
 
11.8%
교 146
 
4.9%
통 146
 
4.9%
진 99
 
3.3%
성 87
 
2.9%
객 73
 
2.5%
여 71
 
2.4%
한 67
 
2.3%
신 59
 
2.0%
Other values (154) 1520
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 2974
98.9%
ASCII 34
 
1.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
운 355
 
11.9%
수 351
 
11.8%
교 146
 
4.9%
통 146
 
4.9%
진 99
 
3.3%
성 87
 
2.9%
객 73
 
2.5%
여 71
 
2.4%
한 67
 
2.3%
신 59
 
2.0%
Other values (154) 1520
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

Distinct631
Distinct (%)89.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-05-10T22:56:09.914251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length3.9405099
Min length3

Characters and Unicode

Total characters2782
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

Unique567 ?
Unique (%)80.3%

Sample

1st row0017
2nd row01A
3rd row01B
4th row0411
5th row100
ValueCountFrequency (%)
8101 5
 
0.7%
1143 4
 
0.6%
8221 4
 
0.6%
n37 3
 
0.4%
8561 3
 
0.4%
n61 3
 
0.4%
n75 3
 
0.4%
3321 2
 
0.3%
1224 2
 
0.3%
6654 2
 
0.3%
Other values (621) 675
95.6%
2024-05-10T22:56:11.482142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 482
17.3%
0 380
13.7%
2 260
9.3%
6 205
 
7.4%
3 198
 
7.1%
5 169
 
6.1%
7 168
 
6.0%
4 157
 
5.6%
8 73
 
2.6%
서 55
 
2.0%
Other values (56) 635
22.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2133
76.7%
Other Letter 573
 
20.6%
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
 
5.2%
성 30
 
5.2%
북 29
 
5.1%
로 25
 
4.4%
대 23
 
4.0%
초 23
 
4.0%
문 21
 
3.7%
Other values (38) 264
46.1%
Decimal Number
ValueCountFrequency (%)
1 482
22.6%
0 380
17.8%
2 260
12.2%
6 205
9.6%
3 198
9.3%
5 169
 
7.9%
7 168
 
7.9%
4 157
 
7.4%
8 73
 
3.4%
9 41
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
N 31
50.8%
A 8
 
13.1%
B 6
 
9.8%
T 4
 
6.6%
O 4
 
6.6%
U 4
 
6.6%
R 4
 
6.6%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2148
77.2%
Hangul 573
 
20.6%
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
 
5.2%
성 30
 
5.2%
북 29
 
5.1%
로 25
 
4.4%
대 23
 
4.0%
초 23
 
4.0%
문 21
 
3.7%
Other values (38) 264
46.1%
Common
ValueCountFrequency (%)
1 482
22.4%
0 380
17.7%
2 260
12.1%
6 205
9.5%
3 198
9.2%
5 169
 
7.9%
7 168
 
7.8%
4 157
 
7.3%
8 73
 
3.4%
9 41
 
1.9%
Latin
ValueCountFrequency (%)
N 31
50.8%
A 8
 
13.1%
B 6
 
9.8%
T 4
 
6.6%
O 4
 
6.6%
U 4
 
6.6%
R 4
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2209
79.4%
Hangul 573
 
20.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 482
21.8%
0 380
17.2%
2 260
11.8%
6 205
9.3%
3 198
9.0%
5 169
 
7.7%
7 168
 
7.6%
4 157
 
7.1%
8 73
 
3.3%
9 41
 
1.9%
Other values (8) 76
 
3.4%
Hangul
ValueCountFrequency (%)
서 55
 
9.6%
동 40
 
7.0%
강 33
 
5.8%
포 30
 
5.2%
성 30
 
5.2%
북 29
 
5.1%
로 25
 
4.4%
대 23
 
4.0%
초 23
 
4.0%
문 21
 
3.7%
Other values (38) 264
46.1%

유형
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
마을
254 
지선
243 
간선
148 
공항
38 
광역
 
11
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 254
36.0%
지선 243
34.4%
간선 148
21.0%
공항 38
 
5.4%
광역 11
 
1.6%
동행 6
 
0.8%
관광 4
 
0.6%
순환 2
 
0.3%

Length

2024-05-10T22:56:12.058975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:56:12.506568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마을 254
36.0%
지선 243
34.4%
간선 148
21.0%
공항 38
 
5.4%
광역 11
 
1.6%
동행 6
 
0.8%
관광 4
 
0.6%
순환 2
 
0.3%

기점명
Text

Distinct357
Distinct (%)50.6%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-05-10T22:56:13.179641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.2719547
Min length2

Characters and Unicode

Total characters3722
Distinct characters297
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

Unique222 ?
Unique (%)31.4%

Sample

1st row청암동
2nd row예장주차장
3rd row예장주차장
4th row용산차고지
5th row하계동
ValueCountFrequency (%)
복정역환승센터 19
 
2.7%
양천공영차고지 16
 
2.3%
인천공항 16
 
2.3%
강동공영차고지 11
 
1.6%
진관공영차고지 11
 
1.6%
장지공영차고지 11
 
1.6%
은평차고지 11
 
1.6%
우이동 10
 
1.4%
중랑공영차고지 10
 
1.4%
강동차고지 9
 
1.3%
Other values (347) 582
82.4%
2024-05-10T22:56:14.578592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 246
 
6.6%
지 196
 
5.3%
고 157
 
4.2%
차 149
 
4.0%
공 119
 
3.2%
역 113
 
3.0%
영 87
 
2.3%
아 61
 
1.6%
산 58
 
1.6%
파 57
 
1.5%
Other values (287) 2479
66.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3594
96.6%
Decimal Number 75
 
2.0%
Uppercase Letter 25
 
0.7%
Other Punctuation 21
 
0.6%
Close Punctuation 3
 
0.1%
Open Punctuation 3
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 246
 
6.8%
지 196
 
5.5%
고 157
 
4.4%
차 149
 
4.1%
공 119
 
3.3%
역 113
 
3.1%
영 87
 
2.4%
아 61
 
1.7%
산 58
 
1.6%
파 57
 
1.6%
Other values (264) 2351
65.4%
Uppercase Letter
ValueCountFrequency (%)
L 5
20.0%
T 5
20.0%
H 4
16.0%
K 2
 
8.0%
A 2
 
8.0%
P 2
 
8.0%
C 2
 
8.0%
G 1
 
4.0%
E 1
 
4.0%
S 1
 
4.0%
Decimal Number
ValueCountFrequency (%)
1 20
26.7%
2 15
20.0%
7 14
18.7%
5 6
 
8.0%
3 6
 
8.0%
4 6
 
8.0%
6 4
 
5.3%
0 2
 
2.7%
8 2
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3594
96.6%
Common 102
 
2.7%
Latin 26
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 246
 
6.8%
지 196
 
5.5%
고 157
 
4.4%
차 149
 
4.1%
공 119
 
3.3%
역 113
 
3.1%
영 87
 
2.4%
아 61
 
1.7%
산 58
 
1.6%
파 57
 
1.6%
Other values (264) 2351
65.4%
Common
ValueCountFrequency (%)
. 21
20.6%
1 20
19.6%
2 15
14.7%
7 14
13.7%
5 6
 
5.9%
3 6
 
5.9%
4 6
 
5.9%
6 4
 
3.9%
) 3
 
2.9%
( 3
 
2.9%
Other values (2) 4
 
3.9%
Latin
ValueCountFrequency (%)
L 5
19.2%
T 5
19.2%
H 4
15.4%
K 2
 
7.7%
A 2
 
7.7%
P 2
 
7.7%
C 2
 
7.7%
G 1
 
3.8%
e 1
 
3.8%
E 1
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3594
96.6%
ASCII 128
 
3.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 246
 
6.8%
지 196
 
5.5%
고 157
 
4.4%
차 149
 
4.1%
공 119
 
3.3%
역 113
 
3.1%
영 87
 
2.4%
아 61
 
1.7%
산 58
 
1.6%
파 57
 
1.6%
Other values (264) 2351
65.4%
ASCII
ValueCountFrequency (%)
. 21
16.4%
1 20
15.6%
2 15
11.7%
7 14
10.9%
5 6
 
4.7%
3 6
 
4.7%
4 6
 
4.7%
L 5
 
3.9%
T 5
 
3.9%
6 4
 
3.1%
Other values (13) 26
20.3%

종점명
Text

Distinct383
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-05-10T22:56:15.209507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length4.6288952
Min length2

Characters and Unicode

Total characters3268
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

Unique235 ?
Unique (%)33.3%

Sample

1st row이촌동
2nd row예장주차장
3rd row예장주차장
4th rowAT센터.양재꽃시장
5th row용산구청
ValueCountFrequency (%)
인천공항 15
 
2.1%
강남역 13
 
1.8%
서울역 12
 
1.7%
여의도 11
 
1.6%
서소문 9
 
1.3%
양재역 9
 
1.3%
석계역 9
 
1.3%
홍대입구역 8
 
1.1%
구로디지털단지역 7
 
1.0%
수유역 7
 
1.0%
Other values (373) 606
85.8%
2024-05-10T22:56:16.612260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 322
 
9.9%
동 118
 
3.6%
대 100
 
3.1%
구 80
 
2.4%
지 63
 
1.9%
서 62
 
1.9%
신 58
 
1.8%
리 51
 
1.6%
문 47
 
1.4%
공 45
 
1.4%
Other values (280) 2322
71.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3173
97.1%
Decimal Number 41
 
1.3%
Other Punctuation 24
 
0.7%
Uppercase Letter 20
 
0.6%
Open Punctuation 5
 
0.2%
Close Punctuation 5
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 322
 
10.1%
동 118
 
3.7%
대 100
 
3.2%
구 80
 
2.5%
지 63
 
2.0%
서 62
 
2.0%
신 58
 
1.8%
리 51
 
1.6%
문 47
 
1.5%
공 45
 
1.4%
Other values (257) 2227
70.2%
Uppercase Letter
ValueCountFrequency (%)
A 4
20.0%
T 3
15.0%
C 2
10.0%
D 2
10.0%
Y 2
10.0%
M 2
10.0%
H 1
 
5.0%
S 1
 
5.0%
G 1
 
5.0%
L 1
 
5.0%
Decimal Number
ValueCountFrequency (%)
2 9
22.0%
7 8
19.5%
3 5
12.2%
5 5
12.2%
1 4
9.8%
4 4
9.8%
6 3
 
7.3%
8 2
 
4.9%
9 1
 
2.4%
Other Punctuation
ValueCountFrequency (%)
. 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3173
97.1%
Common 75
 
2.3%
Latin 20
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 322
 
10.1%
동 118
 
3.7%
대 100
 
3.2%
구 80
 
2.5%
지 63
 
2.0%
서 62
 
2.0%
신 58
 
1.8%
리 51
 
1.6%
문 47
 
1.5%
공 45
 
1.4%
Other values (257) 2227
70.2%
Common
ValueCountFrequency (%)
. 24
32.0%
2 9
 
12.0%
7 8
 
10.7%
3 5
 
6.7%
( 5
 
6.7%
) 5
 
6.7%
5 5
 
6.7%
1 4
 
5.3%
4 4
 
5.3%
6 3
 
4.0%
Other values (2) 3
 
4.0%
Latin
ValueCountFrequency (%)
A 4
20.0%
T 3
15.0%
C 2
10.0%
D 2
10.0%
Y 2
10.0%
M 2
10.0%
H 1
 
5.0%
S 1
 
5.0%
G 1
 
5.0%
L 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3173
97.1%
ASCII 95
 
2.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 322
 
10.1%
동 118
 
3.7%
대 100
 
3.2%
구 80
 
2.5%
지 63
 
2.0%
서 62
 
2.0%
신 58
 
1.8%
리 51
 
1.6%
문 47
 
1.5%
공 45
 
1.4%
Other values (257) 2227
70.2%
ASCII
ValueCountFrequency (%)
. 24
25.3%
2 9
 
9.5%
7 8
 
8.4%
3 5
 
5.3%
( 5
 
5.3%
) 5
 
5.3%
5 5
 
5.3%
1 4
 
4.2%
4 4
 
4.2%
A 4
 
4.2%
Other values (13) 22
23.2%

인가대수
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.026912
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2024-05-10T22:56:17.130455image/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 deviation10.176213
Coefficient of variation (CV)0.78116844
Kurtosis1.071866
Mean13.026912
Median Absolute Deviation (MAD)6
Skewness1.1760899
Sum9197
Variance103.5553
MonotonicityNot monotonic
2024-05-10T22:56:17.960029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
4 54
 
7.6%
3 53
 
7.5%
5 43
 
6.1%
7 41
 
5.8%
8 40
 
5.7%
6 38
 
5.4%
9 35
 
5.0%
2 34
 
4.8%
10 24
 
3.4%
13 21
 
3.0%
Other values (38) 323
45.8%
ValueCountFrequency (%)
1 14
 
2.0%
2 34
4.8%
3 53
7.5%
4 54
7.6%
5 43
6.1%
6 38
5.4%
7 41
5.8%
8 40
5.7%
9 35
5.0%
10 24
3.4%
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 

Distinct42
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.250708
Minimum0
Maximum245
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2024-05-10T22:56:18.595055image/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 deviation19.613811
Coefficient of variation (CV)1.2069512
Kurtosis82.132952
Mean16.250708
Median Absolute Deviation (MAD)3
Skewness8.2438408
Sum11473
Variance384.7016
MonotonicityNot monotonic
2024-05-10T22:56:19.171472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
11 74
 
10.5%
10 68
 
9.6%
15 61
 
8.6%
8 57
 
8.1%
12 53
 
7.5%
9 47
 
6.7%
14 42
 
5.9%
13 37
 
5.2%
7 33
 
4.7%
16 31
 
4.4%
Other values (32) 203
28.8%
ValueCountFrequency (%)
0 1
 
0.1%
4 4
 
0.6%
5 9
 
1.3%
6 18
 
2.5%
7 33
4.7%
8 57
8.1%
9 47
6.7%
10 68
9.6%
11 74
10.5%
12 53
7.5%
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 2
0.3%
55 2
0.3%

거리
Real number (ℝ)

HIGH CORRELATION 

Distinct107
Distinct (%)15.2%
Missing2
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean33.481534
Minimum1
Maximum220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2024-05-10T22:56:19.626767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q19
median24
Q346
95-th percentile90.7
Maximum220
Range219
Interquartile range (IQR)37

Descriptive statistics

Standard deviation36.263571
Coefficient of variation (CV)1.0830917
Kurtosis7.2150257
Mean33.481534
Median Absolute Deviation (MAD)16
Skewness2.4763689
Sum23571
Variance1315.0466
MonotonicityNot monotonic
2024-05-10T22:56:20.110788image/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%
5 26
 
3.7%
12 26
 
3.7%
11 24
 
3.4%
13 22
 
3.1%
10 21
 
3.0%
9 21
 
3.0%
4 19
 
2.7%
Other values (97) 452
64.0%
ValueCountFrequency (%)
1 1
 
0.1%
2 5
 
0.7%
3 16
2.3%
4 19
2.7%
5 26
3.7%
6 29
4.1%
7 34
4.8%
8 30
4.2%
9 21
3.0%
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 

Distinct147
Distinct (%)22.4%
Missing50
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean114.16159
Minimum12
Maximum290
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2024-05-10T22:56:20.547276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile27
Q147.75
median95
Q3180
95-th percentile240
Maximum290
Range278
Interquartile range (IQR)132.25

Descriptive statistics

Standard deviation74.586298
Coefficient of variation (CV)0.65333972
Kurtosis-1.0959202
Mean114.16159
Median Absolute Deviation (MAD)55
Skewness0.49030705
Sum74890
Variance5563.1158
MonotonicityNot monotonic
2024-05-10T22:56:21.031309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 31
 
4.4%
40 29
 
4.1%
60 28
 
4.0%
30 21
 
3.0%
210 18
 
2.5%
180 17
 
2.4%
120 15
 
2.1%
110 13
 
1.8%
36 13
 
1.8%
220 13
 
1.8%
Other values (137) 458
64.9%
(Missing) 50
 
7.1%
ValueCountFrequency (%)
12 1
 
0.1%
15 4
0.6%
18 7
1.0%
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 

Distinct34
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.53966
Minimum0
Maximum245
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2024-05-10T22:56:21.483063image/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 deviation17.977188
Coefficient of variation (CV)1.4336264
Kurtosis108.26264
Mean12.53966
Median Absolute Deviation (MAD)3
Skewness9.4833344
Sum8853
Variance323.17928
MonotonicityNot monotonic
2024-05-10T22:56:21.918862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
10 90
12.7%
7 85
12.0%
8 73
10.3%
6 65
9.2%
9 61
8.6%
5 56
 
7.9%
12 47
 
6.7%
15 36
 
5.1%
25 27
 
3.8%
20 24
 
3.4%
Other values (24) 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 56
7.9%
6 65
9.2%
7 85
12.0%
8 73
10.3%
9 61
8.6%
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 4
0.6%
35 5
0.7%
30 7
1.0%

최대배차
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.728045
Minimum0
Maximum245
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2024-05-10T22:56:22.349720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q113
median17
Q322
95-th percentile40
Maximum245
Range245
Interquartile range (IQR)9

Descriptive statistics

Standard deviation20.852803
Coefficient of variation (CV)1.0060188
Kurtosis69.65272
Mean20.728045
Median Absolute Deviation (MAD)4
Skewness7.5168199
Sum14634
Variance434.83941
MonotonicityNot monotonic
2024-05-10T22:56:22.898515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
15 95
13.5%
20 79
11.2%
25 75
 
10.6%
12 63
 
8.9%
18 42
 
5.9%
10 36
 
5.1%
14 36
 
5.1%
13 34
 
4.8%
30 26
 
3.7%
17 24
 
3.4%
Other values (35) 196
27.8%
ValueCountFrequency (%)
0 1
 
0.1%
5 3
 
0.4%
6 3
 
0.4%
7 16
 
2.3%
8 12
 
1.7%
9 8
 
1.1%
10 36
5.1%
11 15
 
2.1%
12 63
8.9%
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 3
0.4%

첫차시간
Date

Distinct52
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Minimum2024-05-10 00:00:00
Maximum2024-05-10 23:40:00
2024-05-10T22:56:23.326198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:56:23.976989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

막차시간
Date

Distinct90
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Minimum2024-05-10 00:00:00
Maximum2024-05-10 23:59:00
2024-05-10T22:56:24.401471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:56:24.915199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-05-10T22:56:02.828561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:54.654642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:56.191449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:57.527967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:58.836575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:56:00.527022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:56:03.354544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:54.910479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:56.445246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:57.737543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:59.117536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:56:00.826515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:56:03.724736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:55.164689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:56.708814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:57.967778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:59.398766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:56:01.082931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:56:04.061936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:55.416576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:56.959965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:58.125015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:59.655644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:56:01.428066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:56:04.471983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:55.680967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:57.200011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:58.295463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:59.924739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:56:02.004103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:56:04.804309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:55.937907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:57.361468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:55:58.515351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:56:00.269146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:56:02.427506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T22:56:25.179931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유형인가대수배차간격거리운행시간최소배차최대배차첫차시간막차시간
유형1.0000.4820.4920.7670.6590.4850.4970.8980.907
인가대수0.4821.0000.0000.6980.7360.1330.2210.6790.299
배차간격0.4920.0001.0000.7290.3480.9390.9860.6900.963
거리0.7670.6980.7291.0000.7840.7940.7360.8220.965
운행시간0.6590.7360.3480.7841.0000.3000.2540.8150.809
최소배차0.4850.1330.9390.7940.3001.0000.8560.7650.921
최대배차0.4970.2210.9860.7360.2540.8561.0000.6090.936
첫차시간0.8980.6790.6900.8220.8150.7650.6091.0000.979
막차시간0.9070.2990.9630.9650.8090.9210.9360.9791.000
2024-05-10T22:56:25.531616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인가대수배차간격거리운행시간최소배차최대배차유형
인가대수1.000-0.4150.6190.691-0.489-0.3500.258
배차간격-0.4151.0000.1340.0580.9130.9070.290
거리0.6190.1341.0000.9670.0540.1470.505
운행시간0.6910.0580.9671.000-0.0150.0760.389
최소배차-0.4890.9130.054-0.0151.0000.8120.323
최대배차-0.3500.9070.1470.0760.8121.0000.294
유형0.2580.2900.5050.3890.3230.2941.000

Missing values

2024-05-10T22:56:05.276007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T22:56:06.075336image/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.
2024-05-10T22:56:06.530159image/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
1북부운수01A순환예장주차장예장주차장14816606906:3023:00
2북부운수01B순환예장주차장예장주차장5171665151806:3023:00
3대원여객0411지선용산차고지AT센터.양재꽃시장211444220121904:2022:30
4한성여객100간선하계동용산구청32105723181304:0022:30
5한성운수101간선우이동서소문24103817051404:0023:00
6동아운수101간선우이동서소문24103817051404:0023:00
7한성여객1017지선월계동상왕십리111424105121904:3023:20
8흥안운수102간선상계주공7단지동대문20113013061504:0023:10
9삼화상운102간선상계주공7단지동대문20113013061504:0023:10
업체명노선번호유형기점명종점명인가대수배차간격거리운행시간최소배차최대배차첫차시간막차시간
696나경운수종로05마을서대문3번출구종로문화센터71153691506:0023:30
697와룡운수종로07마을명륜새마을금고명륜새마을금고2186<NA>152006:0022:00
698와룡운수종로08마을명륜3가종로5가1177355805:5023:40
699인왕교통종로09마을수성동계곡남대문71064061506:0023:30
700삼청교통종로11마을삼청동서울역71095381206:0023:00
701은수교통종로12마을서울대병원종로3가7954051206:0023:30
702약수교통종로13마을평창동주민센터부암슈퍼415850102005:5022:30
703금창운수 월계점중랑01마을중화1동동아약국신이문역225424252506:0023:50
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